From a3f6e4ab5dcb486102e6e1d8d775f3bd4ed0e9af Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Sun, 18 Aug 2024 18:53:33 +0000
Subject: [PATCH 01/20] Sm e2e st (#502) * sm-emb

* scripts

# What does this PR do?

<!--
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Note: the notebooks in the `course` and `transformers_doc` directories are auto-generated, so are best fixed at their source. Instead, follow the instructions below for these notebooks:

- `course`: Open a PR directly on the `course` repo (https://github.com/huggingface/course)
- `transformers_doc`: Open a PR directly on the `transformers` repo (https://github.com/huggingface/transformers)

-->

<!-- Remove if not applicable -->

Fixes # (issue)

## Who can review?

Feel free to tag members/contributors who may be interested in your PR.

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`examples`:

- PyTorch NLP & Accelerate: @sgugger
- TensorFlow: @Rocketknight1, @gante
- Computer vision: @NielsRogge
- Speech: @anton-l, @patrickvonplaten
- ONNX: @lewtun
- Optimum: @echarlaix
- Tokenizers: @n1t0, @Narsil
- Benchmarks: @patrickvonplaten

`huggingface_hub`: @muellerzr, @LysandreJik

`longform_qa`: @yjernite

`sagemaker`: @philschmid

 -->
---
 .devcontainer/devcontainer.json   | 63 +++++++++++++++++++++++++++++++
 course/en/chapter1/section3.ipynb | 62 ++++++++++++++++++++++++------
 requirements.txt                  |  1 +
 scripts/install.sh                |  3 ++
 4 files changed, 117 insertions(+), 12 deletions(-)
 create mode 100644 .devcontainer/devcontainer.json
 create mode 100644 requirements.txt
 create mode 100644 scripts/install.sh

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 00000000..3b183ca1
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,63 @@
+//devcontainer.json
+{
+    "name": "hf", 
+    // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+    "image": "mcr.microsoft.com/vscode/devcontainers/python:3.10",
+    // pip install needed python packages on creation
+    "postCreateCommand": "pip install -r /workspaces/notebooks/requirements.txt",
+    "mounts": [
+      // Mount the local .ssh directory so you can use SSH key-based authentication.
+      "source=${localEnv:HOME}/.ssh,target=/home/vscode/.ssh,type=bind,consistency=cached",
+      // Mount the local .gitconfig file so you can configure Git user settings.
+      "source=${localEnv:HOME}/.gitconfig,target=/home/vscode/.gitconfig,type=bind,consistency=cached"
+			],
+			"customizations": {
+				"vscode": {
+					"extensions": [
+					// liveshare
+					"ms-vsliveshare.vsliveshare", // Enables real-time collaboration between developers
+					// intellicode
+					"VisualStudioExptTeam.intellicode-api-usage-examples", // Provides examples of how to use IntelliCode APIs
+					"VisualStudioExptTeam.vscodeintellicode-completions", // Provides AI-assisted code completions
+					"VisualStudioExptTeam.vscodeintellicode-insiders",	// Provides AI-assisted code completions (Insiders version)
+					// ms-vscode-remote
+					"ms-vscode-remote.remote-ssh", // Provides SSH remote development capabilities
+					"ms-vscode-remote.remote-containers", // Provides container-based remote development capabilities
+					// python
+					"ms-python.python", // Provides Python language support
+					"ms-python.vscode-pylance", // Provides advanced Python language support
+					"ms-python.black-formatter", // Provides code formatting using the Black formatter
+					// format
+					"streetsidesoftware.code-spell-checker", // Provides spell checking for code comments and strings
+					"yzhang.markdown-all-in-one", // Provides Markdown language support
+					"aaron-bond.better-comments", // Provides improved commenting functionality
+					"njpwerner.autodocstring", // Provides automatic documentation generation for Python functions
+					// source Control
+					"GitHub.vscode-pull-request-github", // Provides GitHub pull request integration
+					"GitHub.codespaces", // Provides GitHub Codespaces integration
+					"GitHub.vscode-github-actions", // Provides GitHub Actions integration
+					"eamodio.gitlens", // Provides Git repository management capabilities
+					// copilot
+					"GitHub.copilot", // Provides AI-assisted code completions and suggestions
+					"GitHub.copilot-chat",  // Provides access to the GitHub Copilot chat (must sign up at https://github.com/github-copilot/chat_waitlist_signup/join)
+					"GitHub.copilot-labs", // Provides access to experimental GitHub Copilot features
+					// ai tools
+					//"HuggingFace.huggingface-vscode", // Provides natural language processing capabilities
+					//"TabNine.tabnine-vscode", // Provides AI-assisted code completions
+					//"Blackboxapp.blackbox", // Provides secure and private machine learning capabilities
+					"Codeium.codeium", // Provides AI-assisted code completions and suggestions
+					// ms-toolai
+					"ms-toolsai.datawrangler", // Provides data cleaning, transformation, and reshaping capabilities
+					"ms-toolsai.jupyter-renderers", // Provides additional Jupyter Notebook cell renderers
+					"ms-toolsai.jupyter", // Provides Jupyter Notebook integration with VS Code
+					"ms-toolsai.vscode-jupyter-cell-tags", // Provides support for cell tags in Jupyter Notebooks
+					"ms-toolsai.vscode-jupyter-slideshow", // Provides support for creating and viewing Jupyter Notebook slideshows
+					"ms-toolsai.jupyter-keymap"	// Provides additional keyboard shortcuts for Jupyter Notebooks	
+
+						]
+				}
+			}
+    // Features to add to the dev container. More info: https://containers.dev/features.
+    // "features": {}
+  }
+   
\ No newline at end of file
diff --git a/course/en/chapter1/section3.ipynb b/course/en/chapter1/section3.ipynb
index ea539fa2..1f7425a8 100644
--- a/course/en/chapter1/section3.ipynb
+++ b/course/en/chapter1/section3.ipynb
@@ -16,27 +16,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "At least one of TensorFlow 2.0 or PyTorch should be installed. To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ To install PyTorch, read the instructions at https://pytorch.org/.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pipeline\n\u001b[0;32m----> 3\u001b[0m classifier \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msentiment-analysis\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m classifier(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mI\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mve been waiting for a HuggingFace course my whole life.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/pipelines/__init__.py:895\u001b[0m, in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m framework \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    894\u001b[0m     model_classes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m]}\n\u001b[0;32m--> 895\u001b[0m     framework, model \u001b[38;5;241m=\u001b[39m \u001b[43minfer_framework_load_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    896\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    897\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_classes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_classes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    898\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    899\u001b[0m \u001b[43m        \u001b[49m\u001b[43mframework\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mframework\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    900\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    901\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    902\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    903\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    905\u001b[0m model_config \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\n\u001b[1;32m    906\u001b[0m hub_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39m_commit_hash\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/pipelines/base.py:237\u001b[0m, in \u001b[0;36minfer_framework_load_model\u001b[0;34m(model, config, model_classes, task, framework, **model_kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;124;03mSelect framework (TensorFlow or PyTorch) to use from the `model` passed. Returns a tuple (framework, model).\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;124;03m    `Tuple`: A tuple framework, model.\u001b[39;00m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_tf_available() \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_available():\n\u001b[0;32m--> 237\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    238\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAt least one of TensorFlow 2.0 or PyTorch should be installed. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    239\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    240\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo install PyTorch, read the instructions at https://pytorch.org/.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    241\u001b[0m     )\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    243\u001b[0m     model_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_from_pipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m task\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: At least one of TensorFlow 2.0 or PyTorch should be installed. To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ To install PyTorch, read the instructions at https://pytorch.org/."
+     ]
     }
    ],
    "source": [
@@ -315,6 +336,23 @@
   "colab": {
    "name": "Transformers, what can they do?",
    "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..747b7aa9
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+transformers
\ No newline at end of file
diff --git a/scripts/install.sh b/scripts/install.sh
new file mode 100644
index 00000000..d4a14bd7
--- /dev/null
+++ b/scripts/install.sh
@@ -0,0 +1,3 @@
+sudo apt-get update
+sudo apt-get install -y python3-dev python3-pip python3-venv
+sudo apt-get install -y build-essential libhdf5-dev
\ No newline at end of file

From 4b273f9a6b0ce84532c59574a5deafb3102c621a Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Mon, 19 Aug 2024 14:47:34 +0000
Subject: [PATCH 02/20] notes on `pipeline()` tasks available

---
 course/en/chapter1/section3.ipynb             | 360 ----------
 .../en/chapter1/section3_pipeline_tasks.ipynb | 619 ++++++++++++++++++
 requirements.txt                              |   4 +-
 3 files changed, 622 insertions(+), 361 deletions(-)
 delete mode 100644 course/en/chapter1/section3.ipynb
 create mode 100644 course/en/chapter1/section3_pipeline_tasks.ipynb

diff --git a/course/en/chapter1/section3.ipynb b/course/en/chapter1/section3.ipynb
deleted file mode 100644
index 1f7425a8..00000000
--- a/course/en/chapter1/section3.ipynb
+++ /dev/null
@@ -1,360 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformers, what can they do?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece] -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
-      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
-     ]
-    },
-    {
-     "ename": "RuntimeError",
-     "evalue": "At least one of TensorFlow 2.0 or PyTorch should be installed. To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ To install PyTorch, read the instructions at https://pytorch.org/.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[8], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pipeline\n\u001b[0;32m----> 3\u001b[0m classifier \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msentiment-analysis\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m classifier(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mI\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mve been waiting for a HuggingFace course my whole life.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/pipelines/__init__.py:895\u001b[0m, in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m framework \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    894\u001b[0m     model_classes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m]}\n\u001b[0;32m--> 895\u001b[0m     framework, model \u001b[38;5;241m=\u001b[39m \u001b[43minfer_framework_load_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    896\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    897\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel_classes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_classes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    898\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    899\u001b[0m \u001b[43m        \u001b[49m\u001b[43mframework\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mframework\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    900\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    901\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    902\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    903\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    905\u001b[0m model_config \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\n\u001b[1;32m    906\u001b[0m hub_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39m_commit_hash\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/pipelines/base.py:237\u001b[0m, in \u001b[0;36minfer_framework_load_model\u001b[0;34m(model, config, model_classes, task, framework, **model_kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;124;03mSelect framework (TensorFlow or PyTorch) to use from the `model` passed. Returns a tuple (framework, model).\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;124;03m    `Tuple`: A tuple framework, model.\u001b[39;00m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_tf_available() \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_available():\n\u001b[0;32m--> 237\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    238\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAt least one of TensorFlow 2.0 or PyTorch should be installed. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    239\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    240\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo install PyTorch, read the instructions at https://pytorch.org/.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    241\u001b[0m     )\n\u001b[1;32m    242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    243\u001b[0m     model_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_from_pipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m task\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: At least one of TensorFlow 2.0 or PyTorch should be installed. To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ To install PyTorch, read the instructions at https://pytorch.org/."
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformers, what can they do?",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter1/section3_pipeline_tasks.ipynb b/course/en/chapter1/section3_pipeline_tasks.ipynb
new file mode 100644
index 00000000..2ee1c396
--- /dev/null
+++ b/course/en/chapter1/section3_pipeline_tasks.ipynb
@@ -0,0 +1,619 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transformers, what can they do?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Text classification\n",
+    "- Zero-shot classification\n",
+    "- Text generation\n",
+    "- Text completion (mask filling)\n",
+    "- Token classification\n",
+    "- Question answering\n",
+    "- Summarization\n",
+    "- Translation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Video Title](https://img.youtube.com/vi/tiZFewofSLM/0.jpg)](https://www.youtube.com/watch?v=tiZFewofSLM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='sentiment-analysis'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'label': 'POSITIVE', 'score': 0.9598050713539124}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "classifier = pipeline(\"sentiment-analysis\")\n",
+    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Can pass multiple inputs as list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'label': 'POSITIVE', 'score': 0.9598050713539124},\n",
+       " {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier(\n",
+    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='zero-shot-classification'`\n",
+    " * More general text classification\n",
+    " * You supply labels\n",
+    " * `pipeline` intelligently recognises labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to facebook/bart-large-mnli and revision c626438 (https://huggingface.co/facebook/bart-large-mnli).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'sequence': 'This is a course about the Transformers library',\n",
+       " 'labels': ['education', 'business', 'politics'],\n",
+       " 'scores': [0.8445963263511658, 0.11197613179683685, 0.043427541851997375]}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "classifier = pipeline(\"zero-shot-classification\")\n",
+    "classifier(\n",
+    "    \"This is a course about the Transformers library\",\n",
+    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='text-generation'`\n",
+    "> * Will auto-complete a given prompt\n",
+    "> * Output is random → Resampling gives another answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'generated_text': 'In this course, we will teach you how to handle two different situations, each more complex than the first. You will take a series of tests and then teach each one on two different exams. Our second exam will focus on how you learn to solve'}]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "generator = pipeline(task=\"text-generation\")\n",
+    "generator(\"In this course, we will teach you how to\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** Default `model='bigram'`\n",
+    "\n",
+    "> ### `model='distilgpt2'`\n",
+    "> * Light-weight version of `gpt2`\n",
+    "> * Training objective og `gpt2` was next word `text-generation`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
+      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'generated_text': \"In this course, we will teach you how to best find and maintain one's best work environment, and how to use a good system of supervision,\"},\n",
+       " {'generated_text': 'In this course, we will teach you how to create and maintain your own brand. We have already provided the resources and content required to help you create'}]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
+    "generator(\n",
+    "    \"In this course, we will teach you how to\",\n",
+    "    max_length=30,\n",
+    "    num_return_sequences=2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='fill-mask'`\n",
+    "> * Training object of `bert` was `fill-mask`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to distilbert/distilroberta-base and revision ec58a5b (https://huggingface.co/distilbert/distilroberta-base).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
+      "Some weights of the model checkpoint at distilbert/distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
+      "- This IS expected if you are initializing RobertaForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing RobertaForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'score': 0.19198448956012726,\n",
+       "  'token': 30412,\n",
+       "  'token_str': ' mathematical',\n",
+       "  'sequence': 'This course will teach you all about mathematical models.'},\n",
+       " {'score': 0.042091626673936844,\n",
+       "  'token': 38163,\n",
+       "  'token_str': ' computational',\n",
+       "  'sequence': 'This course will teach you all about computational models.'}]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "unmasker = pipeline(\"fill-mask\")\n",
+    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='ner'`\n",
+    "> * Classify each word in sentence (`'ner'` is an example)\n",
+    "* `pipeline('ner')` recognizes named entities in a sentence and groups them if `grouped_entities=True`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
+       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
+       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
+       "]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "ner = pipeline(\"ner\", grouped_entities=True)\n",
+    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='question-answering'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "question_answerer = pipeline(\"question-answering\")\n",
+    "question_answerer(\n",
+    "    question=\"Where do I work?\",\n",
+    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='summarization'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
+       "                  'number of engineering graduates in the U.S. has declined in '\n",
+       "                  'traditional engineering disciplines such as mechanical, civil '\n",
+       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
+       "                  'developing economies such as China and India, as well as other '\n",
+       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
+       "                  'and advance engineering .'}]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "summarizer = pipeline(\"summarization\")\n",
+    "summarizer(\n",
+    "    \"\"\"\n",
+    "    America has changed dramatically during recent years. Not only has the number of \n",
+    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
+    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
+    "    the premier American universities engineering curricula now concentrate on \n",
+    "    and encourage largely the study of engineering science. As a result, there \n",
+    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
+    "    the environment, and related issues, and greater concentration on high \n",
+    "    technology subjects, largely supporting increasingly complex scientific \n",
+    "    developments. While the latter is important, it should not be at the expense \n",
+    "    of more traditional engineering.\n",
+    "\n",
+    "    Rapidly developing economies such as China and India, as well as other \n",
+    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
+    "    the teaching of engineering. Both China and India, respectively, graduate \n",
+    "    six and eight times as many traditional engineers as does the United States. \n",
+    "    Other industrial countries at minimum maintain their output, while America \n",
+    "    suffers an increasingly serious decline in the number of engineering graduates \n",
+    "    and a lack of well-educated engineers.\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `task='translation'`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3dcf9a452184266b2d51121ad4458f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f646a5ae4a64e72924a699b08b4e4a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/301M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86044dc0e6194f3e9200159b0c0ef401",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1aa68558092d4400a650b38a2197c3fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e05f8172cd0a4601b91f54ae12f5b461",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "source.spm:   0%|          | 0.00/802k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ddbb8fffb9547979a3fb4376b2a853e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "target.spm:   0%|          | 0.00/778k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a926343705fd43cb992f15b034876697",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/1.34M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
+      "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
+    "translator(\"Ce cours est produit par Hugging Face.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Transformers, what can they do?",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index 747b7aa9..854cef3d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +1,3 @@
-transformers
\ No newline at end of file
+gitdb==4.0.11
+GitPython==3.1.41
+smmap==5.0.1

From 946e995be7945dbbe35285bbe6b4d4312a0933a9 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Mon, 19 Aug 2024 14:53:58 +0000
Subject: [PATCH 03/20] =?UTF-8?q?=F0=9F=A7=B9=20clean-up?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../en/chapter1/section3_pipeline_tasks.ipynb | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/course/en/chapter1/section3_pipeline_tasks.ipynb b/course/en/chapter1/section3_pipeline_tasks.ipynb
index 2ee1c396..335e0b13 100644
--- a/course/en/chapter1/section3_pipeline_tasks.ipynb
+++ b/course/en/chapter1/section3_pipeline_tasks.ipynb
@@ -7,13 +7,6 @@
     "# Transformers, what can they do?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,6 +28,13 @@
     "[![Video Title](https://img.youtube.com/vi/tiZFewofSLM/0.jpg)](https://www.youtube.com/watch?v=tiZFewofSLM)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -54,11 +54,6 @@
     "!pip install datasets evaluate transformers[sentencepiece] -q"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "attachments": {
     "image.png": {

From d4d62fc4b7764fd918d9d9568550ae73621e9032 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Thu, 29 Aug 2024 21:59:00 +0000
Subject: [PATCH 04/20] removed unneeded languages

---
 course/de/chapter1/section10.ipynb            |   71 -
 course/de/chapter1/section3.ipynb             |  322 ---
 course/de/chapter1/section8.ipynb             |   63 -
 course/de/chapter3/section2_pt.ipynb          |  320 ---
 course/de/chapter3/section2_tf.ipynb          |  341 ---
 course/de/chapter3/section3.ipynb             |  194 --
 course/de/chapter3/section3_tf.ipynb          |  202 --
 course/de/chapter3/section4.ipynb             |  368 ---
 course/de/chapter4/section2_pt.ipynb          |   88 -
 course/de/chapter4/section2_tf.ipynb          |   88 -
 course/de/chapter4/section3_pt.ipynb          |  282 --
 course/de/chapter4/section3_tf.ipynb          |  282 --
 course/en/chapter1/quiz.ipynb                 |  153 ++
 .../en/chapter1/section3_pipeline_tasks.ipynb |   24 +-
 course/en/chapter1/section8.ipynb             |   63 -
 course/en/chapter1/section8_bias.ipynb        |  173 ++
 course/es/chapter1/section10.ipynb            |   71 -
 course/es/chapter1/section3.ipynb             |  322 ---
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diff --git a/course/de/chapter1/section10.ipynb b/course/de/chapter1/section10.ipynb
deleted file mode 100644
index 2b90a908..00000000
--- a/course/de/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz am Ende des Kapitels"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz am Ende des Kapitels",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter1/section3.ipynb b/course/de/chapter1/section3.ipynb
deleted file mode 100644
index 64388058..00000000
--- a/course/de/chapter1/section3.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformer-Modelle - wozu sind sie imstande?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformer-Modelle - wozu sind sie imstande?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter1/section8.ipynb b/course/de/chapter1/section8.ipynb
deleted file mode 100644
index 5ef0ddc8..00000000
--- a/course/de/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Bias und Einschränkungen"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Bias und Einschränkungen",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter3/section2_pt.ipynb b/course/de/chapter3/section2_pt.ipynb
deleted file mode 100644
index 9b609765..00000000
--- a/course/de/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Datenbearbeitung (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Genau wie vorher\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",  # Ich habe mein ganzes Leben auf einen HuggingFace-Kurs gewartet.\n",
-    "    \"This course is amazing!\",  # Dieser Kurs ist fantastisch!\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Dies ist neu\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Datenbearbeitung (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter3/section2_tf.ipynb b/course/de/chapter3/section2_tf.ipynb
deleted file mode 100644
index 340e310f..00000000
--- a/course/de/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Datenbearbeitung (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Genau wie vorher\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",  # Ich habe mein ganzes Leben auf einen HuggingFace-Kurs gewartet.\n",
-    "    \"This course is amazing!\",  # Dieser Kurs ist fantastisch!\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# Dies ist neu\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Datenbearbeitung (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter3/section3.ipynb b/course/de/chapter3/section3.ipynb
deleted file mode 100644
index 865c14b2..00000000
--- a/course/de/chapter3/section3.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-tuning von Modellen mit der Trainer API oder Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Fine-tuning von Modellen mit der Trainer API oder Keras",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter3/section3_tf.ipynb b/course/de/chapter3/section3_tf.ipynb
deleted file mode 100644
index 4acd474f..00000000
--- a/course/de/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-tuning von Modellen mit der Trainer API oder Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Fine-tuning von Modellen mit der Trainer API oder Keras",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter3/section4.ipynb b/course/de/chapter3/section4.ipynb
deleted file mode 100644
index 03abb8a1..00000000
--- a/course/de/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Komplettes Training"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Komplettes Training",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter4/section2_pt.ipynb b/course/de/chapter4/section2_pt.ipynb
deleted file mode 100644
index 5dae28b8..00000000
--- a/course/de/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Verwendung vortrainierter Modelle (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Verwendung vortrainierter Modelle (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter4/section2_tf.ipynb b/course/de/chapter4/section2_tf.ipynb
deleted file mode 100644
index 512f78ae..00000000
--- a/course/de/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Verwendung vortrainierter Modelle (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Verwendung vortrainierter Modelle (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter4/section3_pt.ipynb b/course/de/chapter4/section3_pt.ipynb
deleted file mode 100644
index f2fd52db..00000000
--- a/course/de/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Vortrainierte Modelle teilen (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User-Management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository erstellen und managen\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # Methoden, um inhaltliche Information abzufragen/abzuändern\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Mach was du möchtest mit dem Modell, z.B. trainieren, fine-tunen.\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Vortrainierte Modelle teilen (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/de/chapter4/section3_tf.ipynb b/course/de/chapter4/section3_tf.ipynb
deleted file mode 100644
index a11f6658..00000000
--- a/course/de/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Vortrainierte Modelle teilen (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User-Management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository erstellen und managen\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # Methoden, um inhaltliche Information abzufragen/abzuändern\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Mach was du möchtest mit dem Modell, z.B. trainieren, fine-tunen.\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Vortrainierte Modelle teilen (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter1/quiz.ipynb b/course/en/chapter1/quiz.ipynb
new file mode 100644
index 00000000..2e833c09
--- /dev/null
+++ b/course/en/chapter1/quiz.ipynb
@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
+      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dd20cfc8f04469aac1b76d8c9706f56",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/998 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c993adfd76c146e1bfb60e7e9a874bd4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/1.33G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
+      "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f5a30b4191049bb885913deacc78d44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/60.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24fbfb5126be488e8acc0f01ce99fbf5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n",
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/pipelines/token_classification.py:168: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"simple\"` instead.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'entity_group': 'PER',\n",
+       "  'score': 0.9981694,\n",
+       "  'word': 'Sylvain',\n",
+       "  'start': 11,\n",
+       "  'end': 18},\n",
+       " {'entity_group': 'ORG',\n",
+       "  'score': 0.97960204,\n",
+       "  'word': 'Hugging Face',\n",
+       "  'start': 33,\n",
+       "  'end': 45},\n",
+       " {'entity_group': 'LOC',\n",
+       "  'score': 0.9932106,\n",
+       "  'word': 'Brooklyn',\n",
+       "  'start': 49,\n",
+       "  'end': 57}]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "# with `grouped_entities=True`, it will group together the words belonging to the same entity, like \"Hugging Face\".\n",
+    "ner = pipeline(\"ner\", grouped_entities=True)\n",
+    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/course/en/chapter1/section3_pipeline_tasks.ipynb b/course/en/chapter1/section3_pipeline_tasks.ipynb
index 335e0b13..1d5e263a 100644
--- a/course/en/chapter1/section3_pipeline_tasks.ipynb
+++ b/course/en/chapter1/section3_pipeline_tasks.ipynb
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -200,16 +200,21 @@
      "text": [
       "No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).\n",
       "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n",
+      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
       "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to handle two different situations, each more complex than the first. You will take a series of tests and then teach each one on two different exams. Our second exam will focus on how you learn to solve'}]"
+       "[{'generated_text': \"In this course, we will teach you how to generate new users using Angular. It's up to\"},\n",
+       " {'generated_text': 'In this course, we will teach you how to use a wide variety of programming languages.\\n\\n'},\n",
+       " {'generated_text': 'In this course, we will teach you how to understand the different types of data structures used here to'}]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -217,7 +222,9 @@
    "source": [
     "from transformers import pipeline\n",
     "\n",
-    "generator = pipeline(task=\"text-generation\")\n",
+    "generator = pipeline(task=\"text-generation\",\n",
+    "                     num_return_sequences=3,\n",
+    "                     max_length=20)\n",
     "generator(\"In this course, we will teach you how to\")"
    ]
   },
@@ -232,6 +239,13 @@
     "> * Training objective og `gpt2` was next word `text-generation`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# [🤗 Model Hub](https://huggingface.co/models)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -260,7 +274,7 @@
    "source": [
     "from transformers import pipeline\n",
     "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
+    "generator = pipeline(task=\"text-generation\", model=\"distilgpt2\")\n",
     "generator(\n",
     "    \"In this course, we will teach you how to\",\n",
     "    max_length=30,\n",
diff --git a/course/en/chapter1/section8.ipynb b/course/en/chapter1/section8.ipynb
deleted file mode 100644
index 82085efd..00000000
--- a/course/en/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Bias and limitations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Bias and limitations",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter1/section8_bias.ipynb b/course/en/chapter1/section8_bias.ipynb
new file mode 100644
index 00000000..d663d395
--- /dev/null
+++ b/course/en/chapter1/section8_bias.ipynb
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bias and limitations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `beta` will be renamed internally to `bias`. Please use a different name to suppress this warning.\n",
+      "A parameter name that contains `gamma` will be renamed internally to `weight`. Please use a different name to suppress this warning.\n",
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
+      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['lawyer', 'teacher', 'nurse', 'contractor', 'manager']\n",
+      "['carpenter', 'lawyer', 'farmer', 'businessman', 'doctor']\n",
+      "['nurse', 'maid', 'teacher', 'waitress', 'prostitute']\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = unmasker(\"This person works as a [MASK].\")\n",
+    "print([r[\"token_str\"] for r in result])\n",
+    "\n",
+    "result = unmasker(\"This man works as a [MASK].\")\n",
+    "print([r[\"token_str\"] for r in result])\n",
+    "\n",
+    "result = unmasker(\"This woman works as a [MASK].\")\n",
+    "print([r[\"token_str\"] for r in result])"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Bias and limitations",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/course/es/chapter1/section10.ipynb b/course/es/chapter1/section10.ipynb
deleted file mode 100644
index e110d87a..00000000
--- a/course/es/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de final de capítulo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de final de capítulo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter1/section3.ipynb b/course/es/chapter1/section3.ipynb
deleted file mode 100644
index 7ef42005..00000000
--- a/course/es/chapter1/section3.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformadores, ¿qué pueden hacer?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformadores, ¿qué pueden hacer?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter1/section8.ipynb b/course/es/chapter1/section8.ipynb
deleted file mode 100644
index 9afcd4cb..00000000
--- a/course/es/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sesgos y limitaciones"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Sesgos y limitaciones",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section4_pt.ipynb b/course/es/chapter2/section4_pt.ipynb
deleted file mode 100644
index 5e2eb466..00000000
--- a/course/es/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizadores (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'era', 'un', 'titiritero']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson era un titiritero\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directorio_en_mi_computador\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizadores (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section4_tf.ipynb b/course/es/chapter2/section4_tf.ipynb
deleted file mode 100644
index 2ee7a1b8..00000000
--- a/course/es/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizadores (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'era', 'un', 'titiritero']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson era un titiritero\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directorio_en_mi_computador\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizadores (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section5_pt.ipynb b/course/es/chapter2/section5_pt.ipynb
deleted file mode 100644
index afda7d03..00000000
--- a/course/es/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Manejando Secuencias Múltiples (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# Esta línea va a fallar:\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Manejando Secuencias Múltiples (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section5_tf.ipynb b/course/es/chapter2/section5_tf.ipynb
deleted file mode 100644
index e83a2c72..00000000
--- a/course/es/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Manejando Secuencias Múltiples (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# Esta línea va a fallar:\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Manejando Secuencias Múltiples (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section6_pt.ipynb b/course/es/chapter2/section6_pt.ipynb
deleted file mode 100644
index b4f47296..00000000
--- a/course/es/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Poniendo todo junto (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Will pad the sequences up to the maximum sequence length\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Will pad the sequences up to the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Will pad the sequences up to the specified max length\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the specified max length\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Poniendo todo junto (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section6_tf.ipynb b/course/es/chapter2/section6_tf.ipynb
deleted file mode 100644
index 85cfabbe..00000000
--- a/course/es/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Poniendo todo junto (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Will pad the sequences up to the maximum sequence length\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Will pad the sequences up to the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Will pad the sequences up to the specified max length\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the specified max length\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Poniendo todo junto (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section8_pt.ipynb b/course/es/chapter2/section8_pt.ipynb
deleted file mode 100644
index 3cc90c3e..00000000
--- a/course/es/chapter2/section8_pt.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de final de capítulo (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = AutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de final de capítulo (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter2/section8_tf.ipynb b/course/es/chapter2/section8_tf.ipynb
deleted file mode 100644
index cec9402e..00000000
--- a/course/es/chapter2/section8_tf.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de final de capítulo (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = TFAutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de final de capítulo (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter3/section2_pt.ipynb b/course/es/chapter3/section2_pt.ipynb
deleted file mode 100644
index 7b4f145c..00000000
--- a/course/es/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Procesamiento de los datos (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# This is new\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Procesamiento de los datos (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter3/section2_tf.ipynb b/course/es/chapter3/section2_tf.ipynb
deleted file mode 100644
index 23dbe27f..00000000
--- a/course/es/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Procesamiento de los datos (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Procesamiento de los datos (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter3/section4.ipynb b/course/es/chapter3/section4.ipynb
deleted file mode 100644
index b03274f1..00000000
--- a/course/es/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Entrenamiento completo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Entrenamiento completo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter5/section2.ipynb b/course/es/chapter5/section2.ipynb
deleted file mode 100644
index ed9ce2ea..00000000
--- a/course/es/chapter5/section2.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ¿Y si mi dataset no está en el Hub?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz\n",
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gzip -dkv SQuAD_it-*.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=\"SQuAD_it-train.json\", field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    \"title\": \"Terremoto del Sichuan del 2008\",\n",
-       "    \"paragraphs\": [\n",
-       "        {\n",
-       "            \"context\": \"Il terremoto del Sichuan del 2008 o il terremoto...\",\n",
-       "            \"qas\": [\n",
-       "                {\n",
-       "                    \"answers\": [{\"answer_start\": 29, \"text\": \"2008\"}],\n",
-       "                    \"id\": \"56cdca7862d2951400fa6826\",\n",
-       "                    \"question\": \"In quale anno si è verificato il terremoto nel Sichuan?\",\n",
-       "                },\n",
-       "                ...\n",
-       "            ],\n",
-       "        },\n",
-       "        ...\n",
-       "    ],\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 48\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json\", \"test\": \"SQuAD_it-test.json\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")\n",
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json.gz\", \"test\": \"SQuAD_it-test.json.gz\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/crux82/squad-it/raw/master/\"\n",
-    "data_files = {\n",
-    "    \"train\": url + \"SQuAD_it-train.json.gz\",\n",
-    "    \"test\": url + \"SQuAD_it-test.json.gz\",\n",
-    "}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "¿Y si mi dataset no está en el Hub?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter5/section3.ipynb b/course/es/chapter5/section3.ipynb
deleted file mode 100644
index 7cb94e7a..00000000
--- a/course/es/chapter5/section3.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Es momento de subdividir"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
-    "!unzip drugsCom_raw.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
-    "# \\t es el caracter para tabulaciones en Python\n",
-    "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Unnamed: 0': [87571, 178045, 80482],\n",
-       " 'drugName': ['Naproxen', 'Duloxetine', 'Mobic'],\n",
-       " 'condition': ['Gout, Acute', 'ibromyalgia', 'Inflammatory Conditions'],\n",
-       " 'review': ['\"like the previous person mention, I&#039;m a strong believer of aleve, it works faster for my gout than the prescription meds I take. No more going to the doctor for refills.....Aleve works!\"',\n",
-       "  '\"I have taken Cymbalta for about a year and a half for fibromyalgia pain. It is great\\r\\nas a pain reducer and an anti-depressant, however, the side effects outweighed \\r\\nany benefit I got from it. I had trouble with restlessness, being tired constantly,\\r\\ndizziness, dry mouth, numbness and tingling in my feet, and horrible sweating. I am\\r\\nbeing weaned off of it now. Went from 60 mg to 30mg and now to 15 mg. I will be\\r\\noff completely in about a week. The fibro pain is coming back, but I would rather deal with it than the side effects.\"',\n",
-       "  '\"I have been taking Mobic for over a year with no side effects other than an elevated blood pressure.  I had severe knee and ankle pain which completely went away after taking Mobic.  I attempted to stop the medication however pain returned after a few days.\"'],\n",
-       " 'rating': [9.0, 3.0, 10.0],\n",
-       " 'date': ['September 2, 2015', 'November 7, 2011', 'June 5, 2013'],\n",
-       " 'usefulCount': [36, 13, 128]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
-    "# Mirar los primeros ejemplos\n",
-    "drug_sample[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split in drug_dataset.keys():\n",
-    "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 161297\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 53766\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.rename_column(\n",
-    "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
-    ")\n",
-    "drug_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AttributeError: 'NoneType' object has no attribute 'lower'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def lowercase_condition(example):\n",
-    "    return {\"condition\": example[\"condition\"].lower()}\n",
-    "\n",
-    "\n",
-    "drug_dataset.map(lowercase_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_nones(x):\n",
-    "    return x[\"condition\"] is not None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x * x)(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda base, height: 0.5 * base * height)(4, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['left ventricular dysfunction', 'adhd', 'birth control']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(lowercase_condition)\n",
-    "# Revisar que se pasaron a minúscula\n",
-    "drug_dataset[\"train\"][\"condition\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_review_length(example):\n",
-    "    return {\"review_length\": len(example[\"review\"].split())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': 206461,\n",
-       " 'drugName': 'Valsartan',\n",
-       " 'condition': 'left ventricular dysfunction',\n",
-       " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
-       " 'rating': 9.0,\n",
-       " 'date': 'May 20, 2012',\n",
-       " 'usefulCount': 27,\n",
-       " 'review_length': 17}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(compute_review_length)\n",
-    "# Inspeccionar el primer ejemplo de entrenamiento\n",
-    "drug_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': [103488, 23627, 20558],\n",
-       " 'drugName': ['Loestrin 21 1 / 20', 'Chlorzoxazone', 'Nucynta'],\n",
-       " 'condition': ['birth control', 'muscle spasm', 'pain'],\n",
-       " 'review': ['\"Excellent.\"', '\"useless\"', '\"ok\"'],\n",
-       " 'rating': [10.0, 1.0, 6.0],\n",
-       " 'date': ['November 4, 2008', 'March 24, 2017', 'August 20, 2016'],\n",
-       " 'usefulCount': [5, 2, 10],\n",
-       " 'review_length': [1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'train': 138514, 'test': 46108}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
-    "print(drug_dataset.num_rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm a transformer called BERT\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import html\n",
-    "\n",
-    "text = \"I&#039;m a transformer called BERT\"\n",
-    "html.unescape(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_drug_dataset = drug_dataset.map(\n",
-    "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "\n",
-    "def tokenize_function(examples):\n",
-    "    return tokenizer(examples[\"review\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%time tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
-    "\n",
-    "\n",
-    "def slow_tokenize_function(examples):\n",
-    "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    return tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[128, 49]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
-    "[len(inp) for inp in result[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ArrowInvalid: Column 1 named condition expected length 1463 but got length 1000"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(\n",
-    "    tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(206772, 138514)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    result = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    # Extraer el mapeo entre los índices nuevos y viejos\n",
-    "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
-    "    for key, values in examples.items():\n",
-    "        result[key] = [values[i] for i in sample_map]\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 206772\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 68876\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.set_format(\"pandas\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df = drug_dataset[\"train\"][:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frequencies = (\n",
-    "    train_df[\"condition\"]\n",
-    "    .value_counts()\n",
-    "    .to_frame()\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
-    ")\n",
-    "frequencies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['condition', 'frequency'],\n",
-       "    num_rows: 819\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "freq_dataset = Dataset.from_pandas(frequencies)\n",
-    "freq_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
-    "# Renombrar el conjunto \"test\" a \"validation\"\n",
-    "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
-    "# Añadir el conjunto \"test\" al `DatasetDict`\n",
-    "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
-    "drug_dataset_clean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_from_disk\n",
-    "\n",
-    "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
-    "drug_dataset_reloaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split, dataset in drug_dataset_clean.items():\n",
-    "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\"patient_id\":141780,\"drugName\":\"Escitalopram\",\"condition\":\"depression\",\"review\":\"\\\"I seemed to experience the regular side effects of LEXAPRO, insomnia, low sex drive, sleepiness during the day. I am taking it at night because my doctor said if it made me tired to take it at night. I assumed it would and started out taking it at night. Strange dreams, some pleasant. I was diagnosed with fibromyalgia. Seems to be helping with the pain. Have had anxiety and depression in my family, and have tried quite a few other medications that haven't worked. Only have been on it for two weeks but feel more positive in my mind, want to accomplish more in my life. Hopefully the side effects will dwindle away, worth it to stick with it from hearing others responses. Great medication.\\\"\",\"rating\":9.0,\"date\":\"May 29, 2011\",\"usefulCount\":10,\"review_length\":125}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "!head -n 1 drug-reviews-train.jsonl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\n",
-    "    \"train\": \"drug-reviews-train.jsonl\",\n",
-    "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
-    "    \"test\": \"drug-reviews-test.jsonl\",\n",
-    "}\n",
-    "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Es momento de subdividir",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter5/section4.ipynb b/course/es/chapter5/section4.ipynb
deleted file mode 100644
index 3150b456..00000000
--- a/course/es/chapter5/section4.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ¿Big data? 🤗 ¡Datasets al rescate!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install zstandard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['meta', 'text'],\n",
-       "    num_rows: 15518009\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Esto toma algunos minutos para ejecutarse, así que ve por un te o un café mientras esperas :)\n",
-    "data_files = \"https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\n",
-    "pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "pubmed_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pubmed_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install psutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RAM used: 5678.33 MB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# Process.memory_info está expresado en bytes, así que lo convertimos en megabytes\n",
-    "print(f\"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of files in dataset : 20979437051\n",
-       "Dataset size (cache file) : 19.54 GB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(f\"Number of files in dataset : {pubmed_dataset.dataset_size}\")\n",
-    "size_gb = pubmed_dataset.dataset_size / (1024**3)\n",
-    "print(f\"Dataset size (cache file) : {size_gb:.2f} GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Iterated over 15518009 examples (about 19.5 GB) in 64.2s, i.e. 0.304 GB/s'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import timeit\n",
-    "\n",
-    "code_snippet = \"\"\"batch_size = 1000\n",
-    "\n",
-    "for idx in range(0, len(pubmed_dataset), batch_size):\n",
-    "    _ = pubmed_dataset[idx:idx + batch_size]\n",
-    "\"\"\"\n",
-    "\n",
-    "time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())\n",
-    "print(\n",
-    "    f\"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in \"\n",
-    "    f\"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset_streamed = load_dataset(\n",
-    "    \"json\", data_files=data_files, split=\"train\", streaming=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iter(pubmed_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 4958, 5178, 4328, 6779, ...], 'attention_mask': [1, 1, 1, 1, 1, ...]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
-    "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"text\"]))\n",
-    "next(iter(tokenized_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11410799, 'language': 'eng'},\n",
-       " 'text': 'Randomized study of dose or schedule modification of granulocyte colony-stimulating factor in platinum-based chemotherapy for elderly patients with lung cancer ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)\n",
-    "next(iter(shuffled_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'pmid': 11409575, 'language': 'eng'},\n",
-       "  'text': 'Clinical signs of hypoxaemia in children with acute lower respiratory infection: indicators of oxygen therapy ...'},\n",
-       " {'meta': {'pmid': 11409576, 'language': 'eng'},\n",
-       "  'text': \"Hypoxaemia in children with severe pneumonia in Papua New Guinea ...\"},\n",
-       " {'meta': {'pmid': 11409577, 'language': 'eng'},\n",
-       "  'text': 'Oxygen concentrators and cylinders ...'},\n",
-       " {'meta': {'pmid': 11409578, 'language': 'eng'},\n",
-       "  'text': 'Oxygen supply in rural africa: a personal experience ...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_head = pubmed_dataset_streamed.take(5)\n",
-    "list(dataset_head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Skip the first 1,000 examples and include the rest in the training set\n",
-    "train_dataset = shuffled_dataset.skip(1000)\n",
-    "# Take the first 1,000 examples for the validation set\n",
-    "validation_dataset = shuffled_dataset.take(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'case_ID': '110921.json',\n",
-       "  'case_jurisdiction': 'scotus.tar.gz',\n",
-       "  'date_created': '2010-04-28T17:12:49Z'},\n",
-       " 'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "law_dataset_streamed = load_dataset(\n",
-    "    \"json\",\n",
-    "    data_files=\"https://mystic.the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\n",
-    "    split=\"train\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "next(iter(law_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'case_ID': '110921.json',\n",
-       "   'case_jurisdiction': 'scotus.tar.gz',\n",
-       "   'date_created': '2010-04-28T17:12:49Z'},\n",
-       "  'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from itertools import islice\n",
-    "from datasets import interleave_datasets\n",
-    "\n",
-    "combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])\n",
-    "list(islice(combined_dataset, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pile_set_name': 'Pile-CC'},\n",
-       " 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_url = \"https://mystic.the-eye.eu/public/AI/pile/\"\n",
-    "data_files = {\n",
-    "    \"train\": [base_url + \"train/\" + f\"{idx:02d}.jsonl.zst\" for idx in range(30)],\n",
-    "    \"validation\": base_url + \"val.jsonl.zst\",\n",
-    "    \"test\": base_url + \"test.jsonl.zst\",\n",
-    "}\n",
-    "pile_dataset = load_dataset(\"json\", data_files=data_files, streaming=True)\n",
-    "next(iter(pile_dataset[\"train\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "¿Big data? 🤗 ¡Datasets al rescate!",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter5/section5.ipynb b/course/es/chapter5/section5.ipynb
deleted file mode 100644
index 10e7cd1a..00000000
--- a/course/es/chapter5/section5.ipynb
+++ /dev/null
@@ -1,524 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Crea tu propio dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install requests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n",
-    "response = requests.get(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.status_code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'repository_url': 'https://api.github.com/repos/huggingface/datasets',\n",
-       "  'labels_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/labels{/name}',\n",
-       "  'comments_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/comments',\n",
-       "  'events_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/events',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "  'id': 968650274,\n",
-       "  'node_id': 'MDExOlB1bGxSZXF1ZXN0NzEwNzUyMjc0',\n",
-       "  'number': 2792,\n",
-       "  'title': 'Update GooAQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'labels': [],\n",
-       "  'state': 'open',\n",
-       "  'locked': False,\n",
-       "  'assignee': None,\n",
-       "  'assignees': [],\n",
-       "  'milestone': None,\n",
-       "  'comments': 1,\n",
-       "  'created_at': '2021-08-12T11:40:18Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'closed_at': None,\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'active_lock_reason': None,\n",
-       "  'pull_request': {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/2792',\n",
-       "   'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "   'diff_url': 'https://github.com/huggingface/datasets/pull/2792.diff',\n",
-       "   'patch_url': 'https://github.com/huggingface/datasets/pull/2792.patch'},\n",
-       "  'body': '[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.',\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "GITHUB_TOKEN = xxx  # Copy your GitHub token here\n",
-    "headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import math\n",
-    "from pathlib import Path\n",
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "\n",
-    "def fetch_issues(\n",
-    "    owner=\"huggingface\",\n",
-    "    repo=\"datasets\",\n",
-    "    num_issues=10_000,\n",
-    "    rate_limit=5_000,\n",
-    "    issues_path=Path(\".\"),\n",
-    "):\n",
-    "    if not issues_path.is_dir():\n",
-    "        issues_path.mkdir(exist_ok=True)\n",
-    "\n",
-    "    batch = []\n",
-    "    all_issues = []\n",
-    "    per_page = 100  # Número de issues por página\n",
-    "    num_pages = math.ceil(num_issues / per_page)\n",
-    "    base_url = \"https://api.github.com/repos\"\n",
-    "\n",
-    "    for page in tqdm(range(num_pages)):\n",
-    "        # Query con state=all para obtener tanto issues abiertos como cerrados\n",
-    "        query = f\"issues?page={page}&per_page={per_page}&state=all\"\n",
-    "        issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n",
-    "        batch.extend(issues.json())\n",
-    "\n",
-    "        if len(batch) > rate_limit and len(all_issues) < num_issues:\n",
-    "            all_issues.extend(batch)\n",
-    "            batch = []  # Vacía el batch para el siguiente periodo de tiempo\n",
-    "            print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n",
-    "            time.sleep(60 * 60 + 1)\n",
-    "\n",
-    "    all_issues.extend(batch)\n",
-    "    df = pd.DataFrame.from_records(all_issues)\n",
-    "    df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n",
-    "    print(\n",
-    "        f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dependiendo de tu conexión a internet, esto puede tomar varios minutos para ejecutarse...\n",
-    "fetch_issues()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'timeline_url', 'performed_via_github_app'],\n",
-       "    num_rows: 3019\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = load_dataset(\"json\", data_files=\"datasets-issues.jsonl\", split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">> URL: https://github.com/huggingface/datasets/pull/850\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/850', 'html_url': 'https://github.com/huggingface/datasets/pull/850', 'diff_url': 'https://github.com/huggingface/datasets/pull/850.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/850.patch'}\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/issues/2773\n",
-       ">> Pull request: None\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/pull/783\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/783', 'html_url': 'https://github.com/huggingface/datasets/pull/783', 'diff_url': 'https://github.com/huggingface/datasets/pull/783.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/783.patch'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = issues_dataset.shuffle(seed=666).select(range(3))\n",
-    "\n",
-    "# Imprime la URL y las entradas de pull_request\n",
-    "for url, pr in zip(sample[\"html_url\"], sample[\"pull_request\"]):\n",
-    "    print(f\">> URL: {url}\")\n",
-    "    print(f\">> Pull request: {pr}\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"is_pull_request\": False if x[\"pull_request\"] is None else True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792#issuecomment-897594128',\n",
-       "  'issue_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'id': 897594128,\n",
-       "  'node_id': 'IC_kwDODunzps41gDMQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'created_at': '2021-08-12T12:21:52Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'body': \"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issue_number = 2792\n",
-    "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "response = requests.get(url, headers=headers)\n",
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\"]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def get_comments(issue_number):\n",
-    "    url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "    response = requests.get(url, headers=headers)\n",
-    "    return [r[\"body\"] for r in response.json()]\n",
-    "\n",
-    "\n",
-    "# Revisar que el comportamiento de nuestra función es el esperado\n",
-    "get_comments(2792)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Dependiendo de tu conexión a internet, esto puede tomar varios minutos...\n",
-    "issues_with_comments_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"comments\": get_comments(x[\"number\"])}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_with_comments_dataset.to_json(\"issues-datasets-with-comments.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of datasets on Hub: 1487\n",
-       "Dataset Name: acronym_identification, Tags: ['annotations_creators:expert-generated', 'language_creators:found', 'languages:en', 'licenses:mit', 'multilinguality:monolingual', 'size_categories:10K<n<100K', 'source_datasets:original', 'task_categories:structure-prediction', 'task_ids:structure-prediction-other-acronym-identification']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_datasets\n",
-    "\n",
-    "all_datasets = list_datasets()\n",
-    "print(f\"Number of datasets on Hub: {len(all_datasets)}\")\n",
-    "print(all_datasets[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/datasets/lewtun/github-issues'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "repo_url = create_repo(name=\"github-issues\", repo_type=\"dataset\")\n",
-    "repo_url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)\n",
-    "!cp issues-datasets-with-comments.jsonl github-issues/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.lfs_track(\"*.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remote_dataset = load_dataset(\"lewtun/github-issues\", split=\"train\")\n",
-    "remote_dataset"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Crea tu propio dataset",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter5/section6_pt.ipynb b/course/es/chapter5/section6_pt.ipynb
deleted file mode 100644
index b5895c32..00000000
--- a/course/es/chapter5/section6_pt.ipynb
+++ /dev/null
@@ -1,518 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Búsqueda semántica con FAISS (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = AutoModel.from_pretrained(model_ckpt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
-    "    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).detach().cpu().numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Búsqueda semántica con FAISS (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter5/section6_tf.ipynb b/course/es/chapter5/section6_tf.ipynb
deleted file mode 100644
index 81bfd677..00000000
--- a/course/es/chapter5/section6_tf.ipynb
+++ /dev/null
@@ -1,506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Búsqueda semántica con FAISS (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
-    "    )\n",
-    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Búsqueda semántica con FAISS (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter5/section8.ipynb b/course/es/chapter5/section8.ipynb
deleted file mode 100644
index cd1db74b..00000000
--- a/course/es/chapter5/section8.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"allocine\", streaming=True, split=\"train\")\n",
-    "dataset[0]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/es/chapter8/section2.ipynb b/course/es/chapter8/section2.ipynb
deleted file mode 100644
index 39a4983a..00000000
--- a/course/es/chapter8/section2.ipynb
+++ /dev/null
@@ -1,392 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ¿Qué hacer cuando se produce un error?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Clona el repo y extrae la ruta local\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Crea un repo vacío en el Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Clona el repo vacío\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Copia los archivos\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Envia (push) al Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.gitattributes', 'README.md', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer_config.json', 'training_args.bin', 'vocab.txt']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.38669535517692566,\n",
-       " 'start': 34,\n",
-       " 'end': 95,\n",
-       " 'answer': 'the task of extracting an answer from a text given a question'}\n",
-       " # la tarea de extraer una respuesta de un texto a una pregunta dada"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "context_es = r\"\"\"\n",
-    "La respuesta a preguntas es la extracción de una respuesta textual a partir de \n",
-    "una pregunta. Un ejemplo de conjunto de datos de respuesta a preguntas es el \n",
-    "dataset SQuAD, que se basa por completo en esta tarea. Si deseas afinar un modelo \n",
-    "en una tarea SQuAD, puedes aprovechar el script\n",
-    " examples/pytorch/question-answering/run_squad.py\n",
-    "\n",
-    "🤗 Transformers es interoperable con los frameworks PyTorch, TensorFlow y JAX, \n",
-    "así que ¡puedes utilizar tus herramientas favoritas para una gran variedad de tareas!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "# ¿Qué es la respuesta extractiva a preguntas?\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\"  # ¿Qué frameworks puedo usar?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "---------------------------------------------------------------------------\n",
-       "AttributeError                            Traceback (most recent call last)\n",
-       "/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in <module>\n",
-       "      1 inputs = tokenizer(question, text, add_special_tokens=True)\n",
-       "      2 input_ids = inputs[\"input_ids\"]\n",
-       "----> 3 outputs = model(**inputs)\n",
-       "      4 answer_start_scores = outputs.start_logits\n",
-       "      5 answer_end_scores = outputs.end_logits\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\n",
-       "    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
-       "    724\n",
-       "--> 725         distilbert_output = self.distilbert(\n",
-       "    726             input_ids=input_ids,\n",
-       "    727             attention_mask=attention_mask,\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\n",
-       "    471             raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n",
-       "    472         elif input_ids is not None:\n",
-       "--> 473             input_shape = input_ids.size()\n",
-       "    474         elif inputs_embeds is not None:\n",
-       "    475             input_shape = inputs_embeds.size()[:-1]\n",
-       "\n",
-       "AttributeError: 'list' object has no attribute 'size'\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Obtiene el comienzo más probable de la respuesta con el argmax de la puntuación\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Obtiene el final más probable de la respuesta con el argmax de la puntuación\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 2029, 7705, 2015, 2064]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "¿Qué hacer cuando se produce un error?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter2/section2_pt.ipynb b/course/fa/chapter2/section2_pt.ipynb
deleted file mode 100644
index 8e404d3f..00000000
--- a/course/fa/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# پشت صحنه خط تولید (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]),\n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "پشت صحنه خط تولید (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter2/section2_tf.ipynb b/course/fa/chapter2/section2_tf.ipynb
deleted file mode 100644
index 59976dfa..00000000
--- a/course/fa/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# پشت صحنه خط تولید (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>,\n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "پشت صحنه خط تولید (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter2/section3_pt.ipynb b/course/fa/chapter2/section3_pt.ipynb
deleted file mode 100644
index 5d0434d5..00000000
--- a/course/fa/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# مدل‌ها (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "مدل‌ها (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter2/section3_tf.ipynb b/course/fa/chapter2/section3_tf.ipynb
deleted file mode 100644
index 9348b2d0..00000000
--- a/course/fa/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# مدل‌ها (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "مدل‌ها (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter3/section2_pt.ipynb b/course/fa/chapter3/section2_pt.ipynb
deleted file mode 100644
index 2afe8787..00000000
--- a/course/fa/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# پردازش داده (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# This is new\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "پردازش داده (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter3/section2_tf.ipynb b/course/fa/chapter3/section2_tf.ipynb
deleted file mode 100644
index 2b2bb3db..00000000
--- a/course/fa/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# پردازش داده (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "پردازش داده (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter4/section2_pt.ipynb b/course/fa/chapter4/section2_pt.ipynb
deleted file mode 100644
index edbd0189..00000000
--- a/course/fa/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# بکارگیری مدل‌های از پیش تعلیم دیده (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "بکارگیری مدل‌های از پیش تعلیم دیده (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fa/chapter4/section2_tf.ipynb b/course/fa/chapter4/section2_tf.ipynb
deleted file mode 100644
index 58402753..00000000
--- a/course/fa/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# بکارگیری مدل‌های از پیش تعلیم دیده (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "بکارگیری مدل‌های از پیش تعلیم دیده (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter1/section10.ipynb b/course/fr/chapter1/section10.ipynb
deleted file mode 100644
index 21a47788..00000000
--- a/course/fr/chapter1/section10.ipynb
+++ /dev/null
@@ -1,75 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de fin de chapitre"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\n",
-    "    \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    ")  # Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\n",
-    "    \"This is a course about the Transformers library\"\n",
-    ")  # C'est un cours sur la bibliothèque Transformers"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de fin de chapitre",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter1/section3.ipynb b/course/fr/chapter1/section3.ipynb
deleted file mode 100644
index 8a0235a9..00000000
--- a/course/fr/chapter1/section3.ipynb
+++ /dev/null
@@ -1,394 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_UXAjutbMhoa"
-   },
-   "source": [
-    "#  Que peuvent faire les *transformers* ?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "P3GIqRT-Mhoe"
-   },
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KGUwAC1zMhoj"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ehB754vsO78P"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5VmlNOR-OdIU"
-   },
-   "source": [
-    "### Analyse de sentiments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ILYepD-3Mhom"
-   },
-   "outputs": [],
-   "source": [
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uYmC3B-ZMhoo"
-   },
-   "outputs": [],
-   "source": [
-    "classifier = pipeline(\"sentiment-analysis\", model=\"tblard/tf-allocine\")\n",
-    "classifier(\"J'ai attendu un cours d'HuggingFace toute ma vie.\") "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WSiD3eYEMhos"
-   },
-   "source": [
-    "Intéressant ! On observe que le résultat est négatif là où pour la version en anglais le résultat est positif."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6jo4BXTXMhou"
-   },
-   "outputs": [],
-   "source": [
-    "classifier(\n",
-    "    [\"J'ai attendu un cours d'HuggingFace toute ma vie.\", \n",
-    "     \"Je déteste tellement ça !\"]\n",
-    ") # pour classifier plusieurs phrases"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vpC8hlc_OBc8"
-   },
-   "source": [
-    "La phrase \"J'ai attendu un cours d'HuggingFace toute ma vie.\" qui était précedemment négative devient à présent positive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "8LQ52fFqOrQa"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TgmwK2d6OlFE"
-   },
-   "source": [
-    "### Zéro shot classification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "agGiUvz5Mho1"
-   },
-   "outputs": [],
-   "source": [
-    "classifier = pipeline(\"zero-shot-classification\", model=\"BaptisteDoyen/camembert-base-xnli\")\n",
-    "classifier(\n",
-    "    \"C'est un cours sur la bibliothèque Transformers\",\n",
-    "    candidate_labels=[\"éducation\", \"politique\", \"affaires\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "X1WCoo63Mho4"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kujBeTwDO9_3"
-   },
-   "source": [
-    "### Génération de texte"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "66Sc0NpBMho6"
-   },
-   "outputs": [],
-   "source": [
-    "generator = pipeline(\"text-generation\", model=\"asi/gpt-fr-cased-small\")\n",
-    "generator(\"# Dans ce cours, nous vous enseignerons comment\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "wb6QpQF6Mho8"
-   },
-   "outputs": [],
-   "source": [
-    "generator = pipeline(\"text-generation\", model=\"asi/gpt-fr-cased-small\")\n",
-    "generator(\n",
-    "    \"# Dans ce cours, nous vous enseignerons comment\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=1,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "PvMBHts1Mho9"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0kU4aFbAPZ5a"
-   },
-   "source": [
-    "### Remplacement des mots masqués"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6UV_20PfMho_"
-   },
-   "outputs": [],
-   "source": [
-    "unmasker = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "unmasker(\" Ce cours vous apprendra tout sur les modèles <mask>.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cyZdM9VqMhpC"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "PvNljOY6Pao6"
-   },
-   "source": [
-    "### Reconnaissance d'entités nommées"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_k7zh7FYMhpD"
-   },
-   "outputs": [],
-   "source": [
-    "ner = pipeline(\"ner\", model=\"Jean-Baptiste/camembert-ner\", grouped_entities=True)\n",
-    "ner(\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "XZdeSFqnMhpF"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZEIu6FbnQUTb"
-   },
-   "source": [
-    "### Réponse à des questions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ZTX2bUsgMhpG"
-   },
-   "outputs": [],
-   "source": [
-    "question_answerer = pipeline(\"question-answering\", model=\"etalab-ia/camembert-base-squadFR-fquad-piaf\")\n",
-    "question_answerer(\n",
-    "    question=\"Où est-ce que je travaille ?\",\n",
-    "    context=\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Cz8QMHu8MhpI"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XZQEgjAZQW7-"
-   },
-   "source": [
-    "###  Résumé"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "7m_mQN_jMhpJ"
-   },
-   "outputs": [],
-   "source": [
-    "summarizer = pipeline(\"summarization\", model=\"moussaKam/barthez-orangesum-abstract\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    L'Amérique a changé de façon spectaculaire au cours des dernières années. Non seulement le nombre de \n",
-    "    diplômés dans les disciplines traditionnelles de l'ingénierie telles que le génie mécanique, civil, \n",
-    "    l'électricité, la chimie et l'aéronautique a diminué, mais dans la plupart \n",
-    "    des grandes universités américaines, les programmes d'études d'ingénierie se concentrent désormais sur \n",
-    "    et encouragent largement l'étude des sciences de l'ingénieur. Par conséquent, il y a \n",
-    "    de moins en moins d'offres dans les sujets d'ingénierie traitant de l'infrastructure, \n",
-    "    l'environnement et les questions connexes, et une plus grande concentration sur les sujets de haute \n",
-    "    technologie, qui soutiennent en grande partie des développements scientifiques de plus en plus \n",
-    "    complexes. Si cette dernière est importante, elle ne doit pas se faire au détriment\n",
-    "    de l'ingénierie plus traditionnelle.\n",
-    "\n",
-    "    Les économies en développement rapide telles que la Chine et l'Inde, ainsi que d'autres \n",
-    "    pays industrialisés d'Europe et d'Asie, continuent d'encourager et de promouvoir\n",
-    "    l'enseignement de l'ingénierie. La Chine et l'Inde, respectivement, diplôment \n",
-    "    six et huit fois plus d'ingénieurs traditionnels que les États-Unis. \n",
-    "    Les autres pays industriels maintiennent au minimum leur production, tandis que l'Amérique \n",
-    "    souffre d'une baisse de plus en plus importante du nombre de diplômés en ingénierie\n",
-    "    et un manque d'ingénieurs bien formés.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "D6VvM9ZTMhpL"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "T9Ombhj_QrDc"
-   },
-   "source": [
-    "###  Traduction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "VTIsplaDMhpM"
-   },
-   "outputs": [],
-   "source": [
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-fr\")\n",
-    "translator(\"This course is produced by Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter1/section8.ipynb b/course/fr/chapter1/section8.ipynb
deleted file mode 100644
index 9a2b0cfe..00000000
--- a/course/fr/chapter1/section8.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"ry2QKHC_Vf-g"},"source":["# Bias et limitations"]},{"cell_type":"markdown","metadata":{"id":"bzpb9yj-Vf-i"},"source":["Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"w3dy9S9JVf-j"},"outputs":[],"source":["!pip install transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MtKtsnukVf-k"},"outputs":[],"source":["from transformers import pipeline\n","\n","unmasker = pipeline(\"fill-mask\", model=\"camembert-base\")\n","result = unmasker(\"Cet homme travaille comme <mask>.\")\n","print([r[\"token_str\"] for r in result])\n","\n","result = unmasker(\"Cette femme travaille comme <mask>.\")\n","print([r[\"token_str\"] for r in result])"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter2/section2_pt.ipynb b/course/fr/chapter2/section2_pt.ipynb
deleted file mode 100644
index 315f87cc..00000000
--- a/course/fr/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Derrière le pipeline (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\", model=\"tblard/tf-allocine\")\n",
-    "classifier(\n",
-    "    [\"J'ai attendu un cours d'HuggingFace toute ma vie.\",\n",
-    "     \"Je déteste tellement ça !\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"J'ai attendu un cours d'HuggingFace toute ma vie.\",\n",
-    "    \"Je déteste tellement ça !\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "model = AutoModel.from_pretrained(checkpoint, from_tf=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, from_tf=True)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Derrière le pipeline (PyTorch)",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter2/section2_tf.ipynb b/course/fr/chapter2/section2_tf.ipynb
deleted file mode 100644
index bc512453..00000000
--- a/course/fr/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Derrière le pipeline (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\", model=\"tblard/tf-allocine\")\n",
-    "classifier(\n",
-    "    [\"J'ai attendu un cours d'HuggingFace toute ma vie.\",\n",
-    "     \"Je déteste tellement ça !\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"J'ai attendu un cours d'HuggingFace toute ma vie.\",\n",
-    "    \"Je déteste tellement ça !\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "model = AutoModel.from_pretrained(checkpoint, from_tf=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, from_tf=True)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Derrière le pipeline (TensorFlow)",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter2/section3_pt.ipynb b/course/fr/chapter2/section3_pt.ipynb
deleted file mode 100644
index f14bd85d..00000000
--- a/course/fr/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,161 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modèles (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertConfig, CamembertModel\n",
-    "\n",
-    "# Construire la configuration\n",
-    "config = CamembertConfig()\n",
-    "\n",
-    "# Construire le modèle à partir de la configuration\n",
-    "model = CamembertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertConfig, CamembertModel\n",
-    "\n",
-    "config = CamembertConfig()\n",
-    "model = CamembertModel(config)\n",
-    "\n",
-    "# Le modèle est initialisé de façon aléatoire !"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertModel\n",
-    "\n",
-    "model = CamembertModel.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"répertoire_sur_mon_ordinateur\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "encoded_sequences = tokenizer(sequences)\n",
-    "encoded_sequences"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Modèles (PyTorch)",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter2/section3_tf.ipynb b/course/fr/chapter2/section3_tf.ipynb
deleted file mode 100644
index 4526b0eb..00000000
--- a/course/fr/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"bJiOGfeVzxaY"},"source":["# Modèles (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"YzEvxF-Bzxaa"},"source":["Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eJCuVbhLzxad"},"outputs":[],"source":["!pip install transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kZ-zN8-9zxae"},"outputs":[],"source":["from transformers import CamembertConfig, TFCamembertModel\n","\n","# Construire la configuration\n","config = CamembertConfig()\n","\n","# Construire le modèle à partir de la configuration\n","model = TFCamembertModel(config)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Gx86kYZ9zxaf"},"outputs":[],"source":["print(config)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uyp4L7jOzxag"},"outputs":[],"source":["from transformers import CamembertConfig, TFCamembertModel\n","\n","config = CamembertConfig()\n","model = TFCamembertModel(config)\n","\n","# Le modèle est initialisé de façon aléatoire !"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UAfE2fS6zxah"},"outputs":[],"source":["from transformers import TFCamembertModel\n","\n","model = TFCamembertModel.from_pretrained(\"camembert-base\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LSfW_QL_zxai"},"outputs":[],"source":["model.save_pretrained(\"directory_on_my_computer\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nVXUdgN8zxaj"},"outputs":[],"source":["sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TYkwr0z5zxaj"},"outputs":[],"source":["from transformers import CamembertTokenizer\n","\n","tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n","encoded_sequences = tokenizer(sequences)\n","encoded_sequences"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vs1UT-Nazxak"},"outputs":[],"source":["import tensorflow as tf\n","\n","model_inputs = tf.constant(encoded_sequences)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PJpF4QWHzxal"},"outputs":[],"source":["output = model(model_inputs)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter2/section4_pt.ipynb b/course/fr/chapter2/section4_pt.ipynb
deleted file mode 100644
index 7383e40b..00000000
--- a/course/fr/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,139 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_text = \"Jim Henson était marionnettiste\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer(\"Utiliser un Transformer est simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"répertoire_sur_mon_ordinateur\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "\n",
-    "sequence = \"Utiliser un Transformer est simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "decoded_string = tokenizer.decode(ids)\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (PyTorch)",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter2/section4_tf.ipynb b/course/fr/chapter2/section4_tf.ipynb
deleted file mode 100644
index 47ffe280..00000000
--- a/course/fr/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,139 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_text = \"Jim Henson était marionnettiste.\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer(\"Utiliser un Transformer est simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"répertoire_sur_mon_ordinateur\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "\n",
-    "sequence = \"Utiliser un Transformer est simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "decoded_string = tokenizer.decode(ids)\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (TensorFlow)",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter2/section5_pt.ipynb b/course/fr/chapter2/section5_pt.ipynb
deleted file mode 100644
index 0224630d..00000000
--- a/course/fr/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"z3VvJhW3240t"},"source":["# Manipulation de plusieurs séquences (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"_5e8ZkKo240w"},"source":["Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C7Qg5Vcu240x"},"outputs":[],"source":["!pip install transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ptdpPu7c240y"},"outputs":[],"source":["import torch\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","\n","checkpoint = \"tblard/tf-allocine\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint, from_tf=True)\n","sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n","\n","tokens = tokenizer.tokenize(sequence)\n","ids = tokenizer.convert_tokens_to_ids(tokens)\n","input_ids = torch.tensor(ids)\n","# Cette ligne va échouer\n","model(input_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SIKjDImc2400"},"outputs":[],"source":["tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n","print(tokenized_inputs[\"input_ids\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"B7WZv8Ig2400"},"outputs":[],"source":["import torch\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","\n","checkpoint = \"tblard/tf-allocine\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint, from_tf=True)\n","sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n","\n","\n","tokens = tokenizer.tokenize(sequence)\n","ids = tokenizer.convert_tokens_to_ids(tokens)\n","\n","input_ids = torch.tensor([ids])\n","print(\"Input IDs:\", input_ids)\n","\n","output = model(input_ids)\n","print(\"Logits:\", output.logits)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"68dffl7V2401"},"outputs":[],"source":["batched_ids = [\n","    [200, 200, 200],\n","    [200, 200]\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XCAGAjvK2401"},"outputs":[],"source":["padding_id = 100\n","\n","batched_ids = [\n","    [200, 200, 200],\n","    [200, 200, padding_id],\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YFy-mD4-2403"},"outputs":[],"source":["model = AutoModelForSequenceClassification.from_pretrained(checkpoint, from_tf=True)\n","\n","sequence1_ids = [[200, 200, 200]]\n","sequence2_ids = [[200, 200]]\n","batched_ids = [\n","    [200, 200, 200],\n","    [200, 200, tokenizer.pad_token_id],\n","]\n","\n","print(model(torch.tensor(sequence1_ids)).logits)\n","print(model(torch.tensor(sequence2_ids)).logits)\n","print(model(torch.tensor(batched_ids)).logits)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iGjAr8ys2404"},"outputs":[],"source":["batched_ids = [\n","    [200, 200, 200],\n","    [200, 200, tokenizer.pad_token_id],\n","]\n","\n","attention_mask = [\n","    [1, 1, 1],\n","    [1, 1, 0],\n","]\n","\n","outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n","print(outputs.logits)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mQNPJsHm2405"},"outputs":[],"source":["# max_sequence_length = 512\n","sequence = sequence[:max_sequence_length]"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter2/section5_tf.ipynb b/course/fr/chapter2/section5_tf.ipynb
deleted file mode 100644
index 6009a6b7..00000000
--- a/course/fr/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,206 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CzXUV7O25sOM"
-   },
-   "source": [
-    "# Manipulation de plusieurs séquences (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "40CGRmXR5sOP"
-   },
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "b2eoLoIp5sOQ"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "liMUcXsS5sOR"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# Cette ligne va échouer\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FcY3Ehyz5sOT"
-   },
-   "outputs": [],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "r9g_7WTB5sOV"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n",
-    "\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_KWu4__95sOX"
-   },
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "UxkpCEXG5sOY"
-   },
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "UDqkStbe5sOZ"
-   },
-   "outputs": [],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "TpBRXe1v5sOb"
-   },
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "t5UgI12j5sOc"
-   },
-   "outputs": [],
-   "source": [
-    "# max_sequence_length = 512\n",
-    "equence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter2/section6_pt.ipynb b/course/fr/chapter2/section6_pt.ipynb
deleted file mode 100644
index fd78ac15..00000000
--- a/course/fr/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"5YBtEttz651_"},"source":["# Tout assembler (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"_JV7CMWI652C"},"source":["Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WttRiMBP652D"},"outputs":[],"source":["!pip install transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"v-w13IpK652F"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","checkpoint = \"tblard/tf-allocine\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","\n","sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n","\n","model_inputs = tokenizer(sequence)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C9u6J1Jz652G"},"outputs":[],"source":["sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n","\n","model_inputs = tokenizer(sequence)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yNm_f1Ae652H"},"outputs":[],"source":["sequences = [\n","    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n","    \"Moi aussi !\",\n","]\n","\n","model_inputs = tokenizer(sequences)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mrdJDxzC652I"},"outputs":[],"source":["# Remplit les séquences jusqu'à la longueur maximale de la séquence\n","model_inputs = tokenizer(sequences, padding=\"longest\")\n","\n","# Remplit les séquences jusqu'à la longueur maximale du modèle (512 pour BERT ou DistilBERT)\n","model_inputs = tokenizer(sequences, padding=\"max_length\")\n","\n","# Remplit les séquences jusqu'à la longueur maximale spécifiée\n","model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AbQqo_Pd652J"},"outputs":[],"source":["sequences = [\n","    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n","    \"Moi aussi !\",\n","]\n","\n","# Tronque les séquences qui sont plus longues que la longueur maximale du modèle\n","# (512 pour BERT ou DistilBERT)\n","model_inputs = tokenizer(sequences, truncation=True)\n","\n","# Tronque les séquences qui sont plus longues que la longueur maximale spécifiée\n","model_inputs = tokenizer(sequences, max_length=8, truncation=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jkBkTBHA652L"},"outputs":[],"source":["sequences = [\n","    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n","    \"Moi aussi !\",\n","]\n","\n","# Retourne des tenseurs PyTorch\n","model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n","\n","# Retourne des tenseurs TensorFlow\n","model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n","\n","# Retourne des tableaux NumPy\n","model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pOK7kTeA652M"},"outputs":[],"source":["sequence = \"J'ai attendu un cours de HuggingFace toute ma vie.\"\n","\n","model_inputs = tokenizer(sequence)\n","print(model_inputs[\"input_ids\"])\n","\n","tokens = tokenizer.tokenize(sequence)\n","ids = tokenizer.convert_tokens_to_ids(tokens)\n","print(ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CtNBk2Da652N"},"outputs":[],"source":["print(tokenizer.decode(model_inputs[\"input_ids\"]))\n","print(tokenizer.decode(ids))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EXqX7UZI652O"},"outputs":[],"source":["import torch\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","\n","checkpoint = \"tblard/tf-allocine\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint, from_tf=True)\n","sequences = [\n","    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n","    \"Moi aussi !\",\n","]\n","\n","\n","tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n","output = model(**tokens)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter2/section6_tf.ipynb b/course/fr/chapter2/section6_tf.ipynb
deleted file mode 100644
index 3026b3d4..00000000
--- a/course/fr/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,2372 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Lr9IazHC65OG"
-   },
-   "source": [
-    "# Tout assembler (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "8rD9hZss65OJ"
-   },
-   "source": [
-    "Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 12005,
-     "status": "ok",
-     "timestamp": 1663594325660,
-     "user": {
-      "displayName": "Bourdois Loïck",
-      "userId": "03480740790092210857"
-     },
-     "user_tz": -120
-    },
-    "id": "-EeZ0gn-65OK",
-    "outputId": "077f4f51-17b5-4e34-c892-ee6f9e8cd930"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "04crq_ak65OL"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "do8HV6mc65ON"
-   },
-   "outputs": [],
-   "source": [
-    "sequence = \"J'ai attendu un cours d’HuggingFace toute ma vie.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "0KnRX6fD65OO"
-   },
-   "outputs": [],
-   "source": [
-    "sequences = [\n",
-    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
-    "    \"Moi aussi !\",\n",
-    "]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "TGIPZ3C565OP"
-   },
-   "outputs": [],
-   "source": [
-    "# Remplit les séquences jusqu'à la longueur maximale de la séquence\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Remplit les séquences jusqu'à la longueur maximale du modèle (512 pour BERT ou DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Remplit les séquences jusqu'à la longueur maximale spécifiée\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6oyPdoLa65OQ"
-   },
-   "outputs": [],
-   "source": [
-    "sequences = [\n",
-    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
-    "    \"Moi aussi !\",\n",
-    "]\n",
-    "\n",
-    "# Tronque les séquences qui sont plus longues que la longueur maximale du modèle (512 pour BERT ou DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Tronque les séquences qui sont plus longues que la longueur maximale spécifiée\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "SjXOriAe65OR"
-   },
-   "outputs": [],
-   "source": [
-    "sequences = [\n",
-    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
-    "    \"Moi aussi !\",\n",
-    "]\n",
-    "\n",
-    "# Retourne des tenseurs PyTorch\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Retourne des tenseurs TensorFlow\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Retourne des tableaux NumPy\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "wal0cML465OT",
-    "outputId": "56276f8a-ca78-43e4-e9f7-1d93dfdf52fc"
-   },
-   "outputs": [],
-   "source": [
-    "sequence = \"J'ai attendu un cours de HuggingFace toute ma vie.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "37Uboygp65OU",
-    "outputId": "8aaaa410-d725-47e0-bfea-649ec147b5bf"
-   },
-   "outputs": [],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 351,
-     "referenced_widgets": [
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-      "fa250ec0644140a0bf9d44b57c0cc5d4",
-      "2e68925355da416aaffdc124689bc9d0"
-     ]
-    },
-    "executionInfo": {
-     "elapsed": 26128,
-     "status": "ok",
-     "timestamp": 1663594351772,
-     "user": {
-      "displayName": "Bourdois Loïck",
-      "userId": "03480740790092210857"
-     },
-     "user_tz": -120
-    },
-    "id": "M8e1MYp965OV",
-    "outputId": "304023e8-49af-45e8-b3ad-542a25c4b0ad"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"tblard/tf-allocine\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"J'ai attendu un cours de HuggingFace toute ma vie.\",\n",
-    "    \"Moi aussi !\",\n",
-    "]\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
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-      "visibility": null,
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-     }
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-    "fa250ec0644140a0bf9d44b57c0cc5d4": {
-     "model_module": "@jupyter-widgets/base",
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-      "top": null,
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-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter2/section8_pt.ipynb b/course/fr/chapter2/section8_pt.ipynb
deleted file mode 100644
index 0e66d029..00000000
--- a/course/fr/chapter2/section8_pt.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de fin de chapitre (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = AutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de fin de chapitre (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter2/section8_tf.ipynb b/course/fr/chapter2/section8_tf.ipynb
deleted file mode 100644
index 1e210ed7..00000000
--- a/course/fr/chapter2/section8_tf.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de fin de chapitre (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = TFAutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de fin de chapitre (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter3/section2_pt.ipynb b/course/fr/chapter3/section2_pt.ipynb
deleted file mode 100644
index 10ccbad0..00000000
--- a/course/fr/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"iOOZOgNUWmO9"},"source":["#  Préparer  des données (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"NP0--nriWmPA"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4lshhqWdWmPB"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kUeCRz_qWmPC"},"outputs":[],"source":["import torch\n","from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n","\n","# Comme avant\n","checkpoint = \"camembert-base\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n","sequences = [\n","    \"J'ai attendu un cours d'HuggingFace toute ma vie.\", \n","    \"Je déteste tellement ça !\"]\n","batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n","\n","# C'est nouveau\n","batch[\"labels\"] = torch.tensor([1, 1])\n","\n","optimizer = AdamW(model.parameters())\n","loss = model(**batch).loss\n","loss.backward()\n","optimizer.step()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EQlfGavTWmPD"},"outputs":[],"source":["from datasets import load_dataset\n","\n","raw_datasets = load_dataset(\"paws-x\", \"fr\")\n","raw_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dRbS6uuIWmPE"},"outputs":[],"source":["raw_train_dataset = raw_datasets[\"train\"]\n","raw_train_dataset[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4i0iOjQ0WmPF"},"outputs":[],"source":["raw_train_dataset.features"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fF9p109xWmPF"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","checkpoint = \"camembert-base\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n","tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zycRmMuLWmPG"},"outputs":[],"source":["inputs = tokenizer(\"C'est la première phrase.\", \"C'est la deuxième.\")\n","inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HUh1gWLDWmPH"},"outputs":[],"source":["tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"puyxiXf1WmPI"},"outputs":[],"source":["tokenized_dataset = tokenizer(\n","    raw_datasets[\"train\"][\"sentence1\"],\n","    raw_datasets[\"train\"][\"sentence2\"],\n","    padding=True,\n","    truncation=True,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KSivd-YnWmPJ"},"outputs":[],"source":["def tokenize_function(example):\n","    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4qXjbD3HWmPJ"},"outputs":[],"source":["tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n","tokenized_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U3w5hwUZWmPK"},"outputs":[],"source":["from transformers import DataCollatorWithPadding\n","\n","data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_MijuhbYWmPL"},"outputs":[],"source":["samples = tokenized_datasets[\"train\"][:8]\n","samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n","[len(x) for x in samples[\"input_ids\"]]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JLP_NVbzWmPL"},"outputs":[],"source":["batch = data_collator(samples)\n","{k: v.shape for k, v in batch.items()}"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter3/section2_tf.ipynb b/course/fr/chapter3/section2_tf.ipynb
deleted file mode 100644
index 1140437d..00000000
--- a/course/fr/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,265 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IIgKlE2DbZaP"
-   },
-   "source": [
-    "#  Préparer  des données (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Nc00b8BWbZaQ"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "me9NX9X4bZaQ"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FFc_IGipbZaS"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"camembert-base\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"J'ai attendu un cours d'HuggingFace toute ma vie.\", \n",
-    "    \"Je déteste tellement ça !\"]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mnRaEEZibZaT"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"paws-x\", \"fr\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "HO2D7pJYbZaU"
-   },
-   "outputs": [],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "iJxImFEQbZaU"
-   },
-   "outputs": [],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mcfjYv9hbZaV"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Jd5FXWQBbZaW"
-   },
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\"C'est la première phrase.\", \"C'est la deuxième.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ly68WIIHbZaX"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Avts72_WbZaY"
-   },
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "TMvS1GB-bZaZ"
-   },
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "AH7OVmjAbZaa"
-   },
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "B-mS1PgvbZaa"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "huGLcqWdbZab"
-   },
-   "outputs": [],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "d6g3J9rbbZab"
-   },
-   "outputs": [],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "th3DW4MhbZab"
-   },
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "gpuClass": "standard",
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter3/section3.ipynb b/course/fr/chapter3/section3.ipynb
deleted file mode 100644
index 27d9d6bc..00000000
--- a/course/fr/chapter3/section3.ipynb
+++ /dev/null
@@ -1,216 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "xFqoZo2jgBuP"
-   },
-   "source": [
-    "# Finetuner un modèle avec l'API Trainer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "odo72vovgBuR"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KL0srL9lgBuS"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lxEQZhrFgBuU"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"paws-x\", \"fr\")\n",
-    "checkpoint = \"camembert-base\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Dp-s6a1rgBuV"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "1r1XVVN9gBuV"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "d9eakOZOgBuW"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "2hF9UHdXgBuX"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train() # Attention, une epoch prend 12h !"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "eWao7LvzgBuY"
-   },
-   "outputs": [],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "2hen_ecUgBuZ"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JS6HytHngBua"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "POWKrVmkgBub"
-   },
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = load_metric(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "T03glg22gBuc"
-   },
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "gpuClass": "standard",
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter3/section3_tf.ipynb b/course/fr/chapter3/section3_tf.ipynb
deleted file mode 100644
index 04b893c2..00000000
--- a/course/fr/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,221 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "r28eBle5gBRb"
-   },
-   "source": [
-    "# Finetuner un modèle avec Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "N87zfVrxgBRe"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "utBT47KIgBRf"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FJ-ImuKtgBRg"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"paws-x\", \"fr\")\n",
-    "checkpoint = \"camembert-base\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "411-vGP8gBRi"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ChcrgFOegBRk"
-   },
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "C0p3eZg8gBRl"
-   },
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# Le nombre d'étapes d'entraînement est le nombre d'échantillons dans le jeu de données, divisé par la taille du batch, puis multiplié par le nombre total d'époques.\n",
-    "# Notez que le jeu de données tf_train_dataset est ici un batch de données tf.data.Dataset \n",
-    "# et non le jeu de données original Hugging Face, donc sa len() est déjà num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "oM6s7MDmgBRl"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "gJhWFti0gBRm"
-   },
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "k5DiX0KegBRn"
-   },
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "1VvLVrOmgBRo"
-   },
-   "outputs": [],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5xL_iSCXgBRp"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter3/section4.ipynb b/course/fr/chapter3/section4.ipynb
deleted file mode 100644
index eaa09799..00000000
--- a/course/fr/chapter3/section4.ipynb
+++ /dev/null
@@ -1,362 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rGgb0tYAgCXS"
-   },
-   "source": [
-    "# Un entraînement complet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rb9JpxcVgCXU"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "SXDhUvwegCXV"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KU3SNvXugCXX"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"paws-x\", \"fr\")\n",
-    "checkpoint = \"camembert-base\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "i3sASOtBgCXY"
-   },
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "qPDal3ZZgCXY"
-   },
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Ljub68PygCXa"
-   },
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "DbXdwYYcgCXb"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "AfYEvRJ3gCXb"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cSVoIMofgCXd"
-   },
-   "outputs": [],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "GG4qqJZTgCXe"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fborIyYfgCXe"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "gGNfkrI8gCXf"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6pU0KLlIgCXg"
-   },
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Uebr5rvegCXg"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tqd55EFWgCXh"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "PCAcBjKjgCXi"
-   },
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Y7InjBE5gCXi"
-   },
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter4/section2_pt.ipynb b/course/fr/chapter4/section2_pt.ipynb
deleted file mode 100644
index b969767b..00000000
--- a/course/fr/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,101 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fZZfhdGe9N5a"
-   },
-   "source": [
-    "# Utilisation de modèles pré-entraînés (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "usSQUVuB9N5d"
-   },
-   "source": [
-    "Installez la bibliothèque 🤗 Transformers pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "WlPCMTt69N5e"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Ustb_doD9N5f",
-    "outputId": "e5b76d3d-3ce8-4a7d-d1d1-5cda2fdc4d74"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "wCChm9ND9N5g"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ufdx500s9N5h"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter4/section2_tf.ipynb b/course/fr/chapter4/section2_tf.ipynb
deleted file mode 100644
index ded9a2d4..00000000
--- a/course/fr/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"DwpJtMY49Tu3"},"source":["# Utilisation de modèles pré-entraînés (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"rOaHUuqC9Tu6"},"source":["Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iTixfw4p9Tu7"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rR-83T0L9Tu8"},"outputs":[],"source":["from transformers import pipeline\n","\n","camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n","results = camembert_fill_mask(\"Le camembert est <mask> :)\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qD0eu-mj9Tu-"},"outputs":[],"source":["from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n","\n","tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n","model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PfYTLhFL9Tu-"},"outputs":[],"source":["from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n","model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter4/section3_pt.ipynb b/course/fr/chapter4/section3_pt.ipynb
deleted file mode 100644
index 8e16b707..00000000
--- a/course/fr/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,334 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6L5pIPDu9UMY"
-   },
-   "source": [
-    "# Partage de modèles pré-entraînés (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vlFBwP6k9UMa"
-   },
-   "source": [
-    "Installez la bibliothèque 🤗 Transformers pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "zB9R8kG59UMc"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Th9dtmkA9UMd"
-   },
-   "source": [
-    "Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tFXWG0By9UMe"
-   },
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TsyBQCn89UMf"
-   },
-   "source": [
-    "Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KjetS6299UMf"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "gqvHh_7N9UMg"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"camembert-base-finetuned-paws-x\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "oG69RVoU9UMh"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "UZ7Xthy29UMh"
-   },
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "iV1bPhKu9UMi"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FWPHSXvI9UMj"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6Za4fkvN9UMj"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "CqfBg6lo9UMk"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Gestion de l'utilisateur\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Création et gestion du dépôt\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # Quelques méthodes pour récupérer/changer des informations sur le contenu\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lP0VX3_59UMl"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "zgBYWjPU9UMm"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KzJ2LkpS9UMn"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "BEFovpLD9UMp"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ZOoRCMtK9UMp"
-   },
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "vlU4Utcr9UMq"
-   },
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "whpSyDIP9UMq"
-   },
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lNMWPH2d9UMq"
-   },
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "7zkGrZlR9UMr"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Faites ce que vous voulez avec le modèle, entraînez-le, finetunez-le...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter4/section3_tf.ipynb b/course/fr/chapter4/section3_tf.ipynb
deleted file mode 100644
index d13ee742..00000000
--- a/course/fr/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,334 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kTHK_zzU9VDZ"
-   },
-   "source": [
-    "# Partage de modèles pré-entraînés (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "o7yACkWt9VDb"
-   },
-   "source": [
-    "Installez la bibliothèque 🤗 Transformers pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jqz4b3j-9VDc"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "EnJaBW3F9VDd"
-   },
-   "source": [
-    "Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "8WVsxebR9VDe"
-   },
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "9BIXGjdV9VDf"
-   },
-   "source": [
-    "Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fy3htsL-9VDg"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "c8dfr6V89VDg"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"camembert-base-finetuned-paws-x\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "I1fUmjLX9VDh"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "97pRWpyo9VDi"
-   },
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uoxrZuLy9VDi"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ewRTPTvd9VDk"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "YlsxkELo9VDk"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "429P-xUG9VDl"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Gestion de l'utilisateur\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Création et gestion du dépôt\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # Quelques méthodes pour récupérer/changer des informations sur le contenu\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KqfBiUKi9VDl"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "53Ybj6N39VDm"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "DwcAm34i9VDm"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "EvqW-UYB9VDm"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "vGQjKdg49VDm"
-   },
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ciz8xZiw9VDn"
-   },
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5ex39L0Y9VDn"
-   },
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "HvPQc_1c9VDn"
-   },
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "LJ5Eu1Qz9VDn"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Faites ce que vous voulez avec le modèle, entraînez-le, finetunez-le...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter5/section2.ipynb b/course/fr/chapter5/section2.ipynb
deleted file mode 100644
index c66de256..00000000
--- a/course/fr/chapter5/section2.ipynb
+++ /dev/null
@@ -1,135 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Que faire si mon jeu de données n'est pas sur le Hub ?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz\n",
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gzip -dkv SQuAD_it-*.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=\"SQuAD_it-train.json\", field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "squad_it_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json\", \"test\": \"SQuAD_it-test.json\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")\n",
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json.gz\", \"test\": \"SQuAD_it-test.json.gz\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/crux82/squad-it/raw/master/\"\n",
-    "data_files = {\n",
-    "    \"train\": url + \"SQuAD_it-train.json.gz\",\n",
-    "    \"test\": url + \"SQuAD_it-test.json.gz\",\n",
-    "}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Que faire si mon jeu de données n'est pas sur le Hub ?",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter5/section3.ipynb b/course/fr/chapter5/section3.ipynb
deleted file mode 100644
index 44d1c905..00000000
--- a/course/fr/chapter5/section3.ipynb
+++ /dev/null
@@ -1,497 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Il est temps de trancher et de découper"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
-    "!unzip drugsCom_raw.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
-    "# \\t est le caractère de tabulation en Python\n",
-    "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
-    "# Un coup d'œil sur les premiers exemples\n",
-    "drug_sample[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split in drug_dataset.keys():\n",
-    "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.rename_column(\n",
-    "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
-    ")\n",
-    "drug_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def lowercase_condition(example):\n",
-    "    return {\"condition\": example[\"condition\"].lower()}\n",
-    "\n",
-    "\n",
-    "drug_dataset.map(lowercase_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_nones(x):\n",
-    "    return x[\"condition\"] is not None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(lambda x: x * x)(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(lambda base, height: 0.5 * base * height)(4, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lowercase_condition)\n",
-    "# Vérification que la mise en minuscule a fonctionné\n",
-    "drug_dataset[\"train\"][\"condition\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_review_length(example):\n",
-    "    return {\"review_length\": len(example[\"review\"].split())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(compute_review_length)\n",
-    "# Inspecter le premier exemple d'entraînement\n",
-    "drug_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
-    "print(drug_dataset.num_rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import html\n",
-    "\n",
-    "text = \"I&#039;m a transformer called BERT\"\n",
-    "html.unescape(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_drug_dataset = drug_dataset.map(\n",
-    "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "\n",
-    "def tokenize_function(examples):\n",
-    "    return tokenizer(examples[\"review\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%time tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
-    "\n",
-    "\n",
-    "def slow_tokenize_function(examples):\n",
-    "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    return tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
-    "[len(inp) for inp in result[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(\n",
-    "    tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    result = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    # Extraire la correspondance entre les nouveaux et les anciens indices\n",
-    "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
-    "    for key, values in examples.items():\n",
-    "        result[key] = [values[i] for i in sample_map]\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.set_format(\"pandas\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df = drug_dataset[\"train\"][:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frequencies = (\n",
-    "    train_df[\"condition\"]\n",
-    "    .value_counts()\n",
-    "    .to_frame()\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
-    ")\n",
-    "frequencies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "freq_dataset = Dataset.from_pandas(frequencies)\n",
-    "freq_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
-    "# Renommer la division par défaut \"test\" en \"validation\"\n",
-    "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
-    "# Ajoutez le jeu \"test\" à notre `DatasetDict`\n",
-    "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
-    "drug_dataset_clean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_from_disk\n",
-    "\n",
-    "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
-    "drug_dataset_reloaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split, dataset in drug_dataset_clean.items():\n",
-    "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!head -n 1 drug-reviews-train.jsonl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\n",
-    "    \"train\": \"drug-reviews-train.jsonl\",\n",
-    "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
-    "    \"test\": \"drug-reviews-test.jsonl\",\n",
-    "}\n",
-    "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Il est temps de trancher et de découper",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter5/section4.ipynb b/course/fr/chapter5/section4.ipynb
deleted file mode 100644
index 7989db7f..00000000
--- a/course/fr/chapter5/section4.ipynb
+++ /dev/null
@@ -1,246 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Données massives ? 🤗 Datasets à la rescousse !"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install zstandard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Cela prend quelques minutes à exécuter, alors allez prendre un thé ou un café en attendant :)\n",
-    "data_files = \"https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\n",
-    "pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "pubmed_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install psutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# Process.memory_info est exprimé en octets, donc convertir en mégaoctets\n",
-    "print(f\"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Number of files in dataset : {pubmed_dataset.dataset_size}\")\n",
-    "size_gb = pubmed_dataset.dataset_size / (1024**3)\n",
-    "print(f\"Dataset size (cache file) : {size_gb:.2f} GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import timeit\n",
-    "\n",
-    "code_snippet = \"\"\"batch_size = 1000\n",
-    "\n",
-    "for idx in range(0, len(pubmed_dataset), batch_size):\n",
-    "    _ = pubmed_dataset[idx:idx + batch_size]\n",
-    "\"\"\"\n",
-    "\n",
-    "time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())\n",
-    "print(\n",
-    "    f\"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in \"\n",
-    "    f\"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset_streamed = load_dataset(\n",
-    "    \"json\", data_files=data_files, split=\"train\", streaming=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next(iter(pubmed_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
-    "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"text\"]))\n",
-    "next(iter(tokenized_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)\n",
-    "next(iter(shuffled_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset_head = pubmed_dataset_streamed.take(5)\n",
-    "list(dataset_head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ignorer les 1 000 premiers exemples et inclure le reste dans l'ensemble d'apprentissage.\n",
-    "train_dataset = shuffled_dataset.skip(1000)\n",
-    "# Prendre les 1 000 premiers exemples pour l'ensemble de validation.\n",
-    "validation_dataset = shuffled_dataset.take(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "law_dataset_streamed = load_dataset(\n",
-    "    \"json\",\n",
-    "    data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\n",
-    "    split=\"train\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "next(iter(law_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from itertools import islice\n",
-    "from datasets import interleave_datasets\n",
-    "\n",
-    "combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])\n",
-    "list(islice(combined_dataset, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_url = \"https://the-eye.eu/public/AI/pile/\"\n",
-    "data_files = {\n",
-    "    \"train\": [base_url + \"train/\" + f\"{idx:02d}.jsonl.zst\" for idx in range(30)],\n",
-    "    \"validation\": base_url + \"val.jsonl.zst\",\n",
-    "    \"test\": base_url + \"test.jsonl.zst\",\n",
-    "}\n",
-    "pile_dataset = load_dataset(\"json\", data_files=data_files, streaming=True)\n",
-    "next(iter(pile_dataset[\"train\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Données massives ? 🤗 Datasets à la rescousse !",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter5/section5.ipynb b/course/fr/chapter5/section5.ipynb
deleted file mode 100644
index b8913960..00000000
--- a/course/fr/chapter5/section5.ipynb
+++ /dev/null
@@ -1,356 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Création de votre propre jeu de données"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Vous devrez également être connecté au *Hub* d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install requests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n",
-    "response = requests.get(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response.status_code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "GITHUB_TOKEN = xxx  # Copiez votre jeton GitHub ici\n",
-    "headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import math\n",
-    "from pathlib import Path\n",
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "\n",
-    "def fetch_issues(\n",
-    "    owner=\"huggingface\",\n",
-    "    repo=\"datasets\",\n",
-    "    num_issues=10_000,\n",
-    "    rate_limit=5_000,\n",
-    "    issues_path=Path(\".\"),\n",
-    "):\n",
-    "    if not issues_path.is_dir():\n",
-    "        issues_path.mkdir(exist_ok=True)\n",
-    "\n",
-    "    batch = []\n",
-    "    all_issues = []\n",
-    "    per_page = 100  # Nombre d'issues à renvoyer par page\n",
-    "    num_pages = math.ceil(num_issues / per_page)\n",
-    "    base_url = \"https://api.github.com/repos\"\n",
-    "\n",
-    "    for page in tqdm(range(num_pages)):\n",
-    "        # Requête avec state=all pour obtenir les questions ouvertes et fermées\n",
-    "        query = f\"issues?page={page}&per_page={per_page}&state=all\"\n",
-    "        issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n",
-    "        batch.extend(issues.json())\n",
-    "\n",
-    "        if len(batch) > rate_limit and len(all_issues) < num_issues:\n",
-    "            all_issues.extend(batch)\n",
-    "            batch = []  # Vider le batch pour la prochaine période de temps\n",
-    "            print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n",
-    "            time.sleep(60 * 60 + 1)\n",
-    "\n",
-    "    all_issues.extend(batch)\n",
-    "    df = pd.DataFrame.from_records(all_issues)\n",
-    "    df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n",
-    "    print(\n",
-    "        f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# En fonction de votre connexion Internet, l'exécution peut prendre plusieurs minutes...\n",
-    "fetch_issues()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = load_dataset(\"json\", data_files=\"datasets-issues.jsonl\", split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample = issues_dataset.shuffle(seed=666).select(range(3))\n",
-    "\n",
-    "# Afficher l'URL et les entrées de la demande de tirage\n",
-    "for url, pr in zip(sample[\"html_url\"], sample[\"pull_request\"]):\n",
-    "    print(f\">> URL: {url}\")\n",
-    "    print(f\">> Pull request: {pr}\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"is_pull_request\": False if x[\"pull_request\"] is None else True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issue_number = 2792\n",
-    "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "response = requests.get(url, headers=headers)\n",
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_comments(issue_number):\n",
-    "    url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "    response = requests.get(url, headers=headers)\n",
-    "    return [r[\"body\"] for r in response.json()]\n",
-    "\n",
-    "\n",
-    "# Tester notre fonction fonctionne comme prévu\n",
-    "get_comments(2792)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Selon votre connexion internet, cela peut prendre quelques minutes...\n",
-    "issues_with_comments_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"comments\": get_comments(x[\"number\"])}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_with_comments_dataset.to_json(\"issues-datasets-with-comments.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import list_datasets\n",
-    "\n",
-    "all_datasets = list_datasets()\n",
-    "print(f\"Number of datasets on Hub: {len(all_datasets)}\")\n",
-    "print(all_datasets[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "repo_url = create_repo(name=\"github-issues\", repo_type=\"dataset\")\n",
-    "repo_url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)\n",
-    "!cp datasets-issues-with-comments.jsonl github-issues/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.lfs_track(\"*.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "remote_dataset = load_dataset(\"lewtun/github-issues\", split=\"train\")\n",
-    "remote_dataset"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Création de votre propre jeu de données",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter5/section6_pt.ipynb b/course/fr/chapter5/section6_pt.ipynb
deleted file mode 100644
index 064076b5..00000000
--- a/course/fr/chapter5/section6_pt.ipynb
+++ /dev/null
@@ -1,316 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Recherche sémantique avec FAISS (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = AutoModel.from_pretrained(model_ckpt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
-    "    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).detach().cpu().numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Recherche sémantique avec FAISS (PyTorch)",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter5/section6_tf.ipynb b/course/fr/chapter5/section6_tf.ipynb
deleted file mode 100644
index bc738289..00000000
--- a/course/fr/chapter5/section6_tf.ipynb
+++ /dev/null
@@ -1,303 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Recherche sémantique avec FAISS (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
-    "    )\n",
-    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Recherche sémantique avec FAISS (TensorFlow)",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter5/section8.ipynb b/course/fr/chapter5/section8.ipynb
deleted file mode 100644
index ce82d898..00000000
--- a/course/fr/chapter5/section8.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de fin de chapitre"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"allocine\", streaming=True, split=\"train\")\n",
-    "dataset[0]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de fin de chapitre",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter6/section2.ipynb b/course/fr/chapter6/section2.ipynb
deleted file mode 100644
index 9610d856..00000000
--- a/course/fr/chapter6/section2.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"kPH6J0bz6fWE"},"source":["# Entraîner un nouveau *tokenizer* à partir d'un ancien\n"]},{"cell_type":"markdown","metadata":{"id":"JCwKOHB16fWG"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EKZwoG-R6fWH"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"T4VLpZVS6fWI"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VX6n6n6q6fWJ"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"2TFawZYz6fWJ"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6vDTho3b6fWK"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b-rF79yr6fWL"},"outputs":[],"source":["from datasets import load_dataset\n","\n","# Le chargement peut prendre quelques minutes, alors prenez un café ou un thé pendant que vous attendez !\n","raw_datasets = load_dataset(\"code_search_net\", \"python\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FNkLCOyR6fWM"},"outputs":[],"source":["raw_datasets[\"train\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nTR9ekHP6fWN"},"outputs":[],"source":["print(raw_datasets[\"train\"][123456][\"whole_func_string\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_u1reTIm6fWO"},"outputs":[],"source":["# Ne décommentez pas la ligne suivante à moins que votre jeu de données soit petit !\n","# training_corpus = [raw_datasets[\"train\"][i: i + 1000][\"whole_func_string\"] for i in range(0, len(raw_datasets[\"train\"]), 1000)]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"foy0q1Pl6fWO"},"outputs":[],"source":["training_corpus = (\n","    raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n","    for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7AIjWoG86fWP"},"outputs":[],"source":["gen = (i for i in range(10))\n","print(list(gen))\n","print(list(gen))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"w5xC4ax36fWQ"},"outputs":[],"source":["def get_training_corpus():\n","    return (\n","        raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n","        for i in range(0, len(raw_datasets[\"train\"]), 1000)\n","    )\n","\n","training_corpus = get_training_corpus()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"llN0Uc-w6fWQ"},"outputs":[],"source":["def get_training_corpus():\n","    dataset = raw_datasets[\"train\"]\n","    for start_idx in range(0, len(dataset), 1000):\n","        samples = dataset[start_idx : start_idx + 1000]\n","        yield samples[\"whole_func_string\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jPwY2Rgt6fWR"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","old_tokenizer = AutoTokenizer.from_pretrained(\"asi/gpt-fr-cased-base\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jT9tLQ7c6fWR"},"outputs":[],"source":["example = '''def add_numbers(a, b):\n","    \"\"\"Add the two numbers `a` and `b`.\"\"\"\n","    return a + b'''\n","\n","tokens = old_tokenizer.tokenize(example)\n","tokens"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ClZZZsMp6fWS"},"outputs":[],"source":["tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000) # prend un peu de temps"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YEJF6D2o6fWS"},"outputs":[],"source":["tokens = tokenizer.tokenize(example)\n","tokens"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iU-YMlLQ6fWT"},"outputs":[],"source":["print(len(tokens))\n","print(len(old_tokenizer.tokenize(example)))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fom02FPr6fWT"},"outputs":[],"source":["example = \"\"\"class LinearLayer():\n","    def __init__(self, input_size, output_size):\n","        self.weight = torch.randn(input_size, output_size)\n","        self.bias = torch.zeros(output_size)\n","\n","    def __call__(self, x):\n","        return x @ self.weights + self.bias\n","    \"\"\"\n","tokenizer.tokenize(example)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8F5XIhsB6fWT"},"outputs":[],"source":["tokenizer.save_pretrained(\"code-search-net-tokenizer\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9wGxQgzP6fWU"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ioU8aN6p6fWU"},"outputs":[],"source":["tokenizer.push_to_hub(\"code-search-net-tokenizer\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IaBhMaJs6fWV"},"outputs":[],"source":["# Remplacez \"huggingface-course\" ci-dessous par votre espace de nom réel pour utiliser votre propre tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section3_pt.ipynb b/course/fr/chapter6/section3_pt.ipynb
deleted file mode 100644
index 5569b49b..00000000
--- a/course/fr/chapter6/section3_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"reqty85ax-qV"},"source":["# Les pouvoirs spéciaux des *tokenizers* rapides (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"qxWxpzXSx-qX"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vj5jxthwx-qY"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Fk6sMkBfx-qa"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n","example = \"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\"\n","encoding = tokenizer(example)\n","print(type(encoding))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9sVpYGqXx-qb"},"outputs":[],"source":["tokenizer.is_fast"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W06VnY6yx-qd"},"outputs":[],"source":["encoding.is_fast"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FZPb1bWpx-qd"},"outputs":[],"source":["encoding.tokens()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9c0ObjFDx-qe"},"outputs":[],"source":["encoding.word_ids()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6XremT1mx-qf"},"outputs":[],"source":["start, end = encoding.word_to_chars(3)\n","example[start:end]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dv02nEt3x-qg"},"outputs":[],"source":["from transformers import pipeline\n","\n","token_classifier = pipeline(\"token-classification\", model=\"Jean-Baptiste/camembert-ner\")\n","token_classifier(\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cOjcPtMgx-qg"},"outputs":[],"source":["from transformers import pipeline\n","\n","token_classifier = pipeline(\"token-classification\", model=\"Jean-Baptiste/camembert-ner\", aggregation_strategy=\"simple\")\n","token_classifier(\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y3HsfFlnx-qh"},"outputs":[],"source":["from transformers import AutoTokenizer, AutoModelForTokenClassification\n","\n","model_checkpoint = \"Jean-Baptiste/camembert-ner\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n","model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)\n","\n","example = \"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\"\n","inputs = tokenizer(example, return_tensors=\"pt\")\n","outputs = model(**inputs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o8NRZukux-qi"},"outputs":[],"source":["print(inputs[\"input_ids\"].shape)\n","print(outputs.logits.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jHSvzSsux-qi"},"outputs":[],"source":["import torch\n","\n","probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()\n","predictions = outputs.logits.argmax(dim=-1)[0].tolist()\n","print(predictions)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zZRh688nx-qj"},"outputs":[],"source":["model.config.id2label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a2_OPOgMx-qj"},"outputs":[],"source":["results = []\n","tokens = inputs.tokens()\n","\n","for idx, pred in enumerate(predictions):\n","    label = model.config.id2label[pred]\n","    if label != \"O\":\n","        results.append(\n","            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n","        )\n","\n","print(results)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b4homS7ax-qk"},"outputs":[],"source":["inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n","inputs_with_offsets[\"offset_mapping\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TVSsv4FTx-qk"},"outputs":[],"source":["example[12:14]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CKjxmmnSx-qk"},"outputs":[],"source":["results = []\n","inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n","tokens = inputs_with_offsets.tokens()\n","offsets = inputs_with_offsets[\"offset_mapping\"]\n","\n","for idx, pred in enumerate(predictions):\n","    label = model.config.id2label[pred]\n","    if label != \"O\":\n","        start, end = offsets[idx]\n","        results.append(\n","            {\n","                \"entity\": label,\n","                \"score\": probabilities[idx][pred],\n","                \"word\": tokens[idx],\n","                \"start\": start,\n","                \"end\": end,\n","            }\n","        )\n","\n","print(results)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8et97wPNx-qm"},"outputs":[],"source":["example[39:51]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Gsz-T3OHx-qm"},"outputs":[],"source":["import numpy as np\n","\n","results = []\n","inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n","tokens = inputs_with_offsets.tokens()\n","offsets = inputs_with_offsets[\"offset_mapping\"]\n","\n","idx = 0\n","while idx < len(predictions):\n","    pred = predictions[idx]\n","    label = model.config.id2label[pred]\n","    if label != \"O\":\n","        # Enlevez le B- ou le I-\n","        label = label[2:]\n","        start, _ = offsets[idx]\n","\n","        # Récupérer tous les tokens étiquetés avec I-label\n","        all_scores = []\n","        while (\n","            idx < len(predictions)\n","            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n","        ):\n","            all_scores.append(probabilities[idx][pred])\n","            _, end = offsets[idx]\n","            idx += 1\n","\n","        # Le score est la moyenne de tous les scores des tokens de cette entité groupée\n","        score = np.mean(all_scores).item()\n","        word = example[start:end]\n","        results.append(\n","            {\n","                \"entity_group\": label,\n","                \"score\": score,\n","                \"word\": word,\n","                \"start\": start,\n","                \"end\": end,\n","            }\n","        )\n","    idx += 1\n","\n","print(results)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section3_tf.ipynb b/course/fr/chapter6/section3_tf.ipynb
deleted file mode 100644
index 0fe69e1e..00000000
--- a/course/fr/chapter6/section3_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"ChRzDUdsyL-B"},"source":["# Les pouvoirs spéciaux des *tokenizers* rapides (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"7xw9nL30yL-C"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WGZCCqS3yL-D"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"My4_vkRzyL-F"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n","example = \"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\"\n","encoding = tokenizer(example)\n","print(type(encoding))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KUse7nScyL-G"},"outputs":[],"source":["tokenizer.is_fast"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3ss1OFNvyL-H"},"outputs":[],"source":["encoding.is_fast"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2xlugRuRyL-H"},"outputs":[],"source":["encoding.tokens()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fF6mrfa-yL-I"},"outputs":[],"source":["encoding.word_ids()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qg67RcfPyL-J"},"outputs":[],"source":["start, end = encoding.word_to_chars(3)\n","example[start:end]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5tO0xA5eyL-L"},"outputs":[],"source":["from transformers import pipeline\n","\n","token_classifier = pipeline(\"token-classification\", model=\"Jean-Baptiste/camembert-ner\")\n","token_classifier(\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LStlJciXyL-M"},"outputs":[],"source":["from transformers import pipeline\n","\n","token_classifier = pipeline(\"token-classification\", model=\"Jean-Baptiste/camembert-ner\", aggregation_strategy=\"simple\")\n","token_classifier(\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0LX1m7layL-N"},"outputs":[],"source":["from transformers import AutoTokenizer, TFAutoModelForTokenClassification\n","\n","model_checkpoint = \"Jean-Baptiste/camembert-ner\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n","model = TFAutoModelForTokenClassification.from_pretrained(model_checkpoint, from_pt=True)\n","\n","example = \"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\"\n","inputs = tokenizer(example, return_tensors=\"tf\")\n","outputs = model(**inputs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"al49780zyL-N"},"outputs":[],"source":["print(inputs[\"input_ids\"].shape)\n","print(outputs.logits.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vtkYUQ1ByL-O"},"outputs":[],"source":["import tensorflow as tf\n","\n","probabilities = tf.math.softmax(outputs.logits, axis=-1)[0]\n","probabilities = probabilities.numpy().tolist()\n","predictions = tf.math.argmax(outputs.logits, axis=-1)[0]\n","predictions = predictions.numpy().tolist()\n","print(predictions)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"deJWHIOzyL-O"},"outputs":[],"source":["model.config.id2label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GvKiLHScyL-P"},"outputs":[],"source":["results = []\n","tokens = inputs.tokens()\n","\n","for idx, pred in enumerate(predictions):\n","    label = model.config.id2label[pred]\n","    if label != \"O\":\n","        results.append(\n","            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n","        )\n","\n","print(results)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aM8S1lCZyL-P"},"outputs":[],"source":["inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n","inputs_with_offsets[\"offset_mapping\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0mUPsw8eyL-Q"},"outputs":[],"source":["example[12:14]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cAfCSvOtyL-Q"},"outputs":[],"source":["results = []\n","inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n","tokens = inputs_with_offsets.tokens()\n","offsets = inputs_with_offsets[\"offset_mapping\"]\n","\n","for idx, pred in enumerate(predictions):\n","    label = model.config.id2label[pred]\n","    if label != \"O\":\n","        start, end = offsets[idx]\n","        results.append(\n","            {\n","                \"entity\": label,\n","                \"score\": probabilities[idx][pred],\n","                \"word\": tokens[idx],\n","                \"start\": start,\n","                \"end\": end,\n","            }\n","        )\n","\n","print(results)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4l-GWLaNyL-R"},"outputs":[],"source":["example[33:45]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dbHE-ENbyL-R"},"outputs":[],"source":["import numpy as np\n","\n","results = []\n","inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n","tokens = inputs_with_offsets.tokens()\n","offsets = inputs_with_offsets[\"offset_mapping\"]\n","\n","idx = 0\n","while idx < len(predictions):\n","    pred = predictions[idx]\n","    label = model.config.id2label[pred]\n","    if label != \"O\":\n","        # Enlevez le B- ou le I-\n","        label = label[2:]\n","        start, _ = offsets[idx]\n","\n","        # Récupérer tous les tokens étiquetés avec I-label\n","        all_scores = []\n","        while (\n","            idx < len(predictions)\n","            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n","        ):\n","            all_scores.append(probabilities[idx][pred])\n","            _, end = offsets[idx]\n","            idx += 1\n","\n","        # Le score est la moyenne de tous les scores des tokens de cette entité groupée\n","        score = np.mean(all_scores).item()\n","        word = example[start:end]\n","        results.append(\n","            {\n","                \"entity_group\": label,\n","                \"score\": score,\n","                \"word\": word,\n","                \"start\": start,\n","                \"end\": end,\n","            }\n","        )\n","    idx += 1\n","\n","print(results)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section3b_pt.ipynb b/course/fr/chapter6/section3b_pt.ipynb
deleted file mode 100644
index a198e23e..00000000
--- a/course/fr/chapter6/section3b_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"pSVK7chzzKjs"},"source":["# Fast tokenizers in the QA pipeline (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"OyYZyviMzKjv"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9QG7herTzKjw"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fAjp4YruzKjy"},"outputs":[],"source":["from transformers import pipeline\n","\n","question_answerer = pipeline(\"question-answering\", model=\"etalab-ia/camembert-base-squadFR-fquad-piaf\")\n","context = \"\"\"\n","🤗 Transformers s'appuie sur les trois bibliothèques d'apprentissage profond les plus populaires (Jax, PyTorch et TensorFlow) avec une intégration transparente entre elles.\n","C'est simple d'entraîner vos modèles avec l'une avant de les charger pour l'inférence avec l'autre.\n","\"\"\"\n","question = \"Quelles bibliothèques d'apprentissage profond derrière 🤗 Transformers ?\"\n","question_answerer(question=question, context=context)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4jwNeGy2zKjz"},"outputs":[],"source":["long_context = \"\"\"\n","🤗 Transformers : l'état de l'art du NLP\n","\n","🤗 Transformers fournit des milliers de modèles pré-entraînés pour effectuer des tâches sur des textes telles que la classification, \n","l'extraction d'informations, la réponse à des questions, le résumé de textes, la traduction, la génération de texte et plus encore dans plus de 100 langues.\n","Son objectif est de rendre le traitement automatique des langues de pointe plus facile à utiliser pour tout le monde.\n","\n","🤗 Transformers fournit des API permettant de télécharger et d'utiliser rapidement ces modèles pré-entraînés sur un texte donné, de les affiner sur vos propres ensembles de données et de les partager avec la communauté sur notre site Web.\n","puis de les partager avec la communauté sur notre hub de modèles. En même temps, chaque module python définissant une architecture est entièrement autonome et peut être modifié pour permettre des expériences de recherche rapides.\n","peut être modifié pour permettre des expériences de recherche rapides.\n","\n","Pourquoi devrais-je utiliser des transformateurs ?\n","\n","1. Des modèles de pointe faciles à utiliser :\n","  - Haute performance sur les tâches NLU et NLG.\n","  - Faible barrière à l'entrée pour les éducateurs et les praticiens.\n","  - Peu d'abstractions pour l'utilisateur avec seulement trois classes à apprendre.\n","  - Une API unifiée pour utiliser tous nos modèles pré-entraînés.\n","  - Des coûts de calcul plus faibles, une empreinte carbone réduite :\n","\n","2. Les chercheurs peuvent partager les modèles formés au lieu de toujours les reformer.\n","  - Les praticiens peuvent réduire le temps de calcul et les coûts de production.\n","  - Des dizaines d'architectures avec plus de 10 000 modèles pré-formés, certains dans plus de 100 langues.\n","\n","3. Choisissez le cadre approprié pour chaque étape de la vie d'un modèle :\n","  - Entraînez des modèles de pointe en 3 lignes de code.\n","  - Déplacez un seul modèle entre les frameworks TF2.0/PyTorch à volonté.\n","  - Choisissez de manière transparente le bon framework pour l'entraînement, l'évaluation et la production.\n","\n","4. Adaptez facilement un modèle ou un exemple à vos besoins :\n","  - Nous fournissons des exemples pour chaque architecture afin de reproduire les résultats publiés par ses auteurs originaux.\n","  - Les éléments internes des modèles sont exposés de manière aussi cohérente que possible.\n","  - Les fichiers de modèles peuvent être utilisés indépendamment de la bibliothèque pour des expériences rapides.\n","\n","🤗 Transformers s'appuie sur les trois bibliothèques d'apprentissage profond les plus populaires (Jax, PyTorch et TensorFlow) avec une intégration parfaite\n","entre elles. Il est simple d'entraîner vos modèles avec l'une avant de les charger pour l'inférence avec l'autre.\n","\"\"\"\n","question_answerer(question=question, context=long_context)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KcdS5LnBzKj1"},"outputs":[],"source":["from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n","\n","model_checkpoint = \"etalab-ia/camembert-base-squadFR-fquad-piaf\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n","model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n","\n","inputs = tokenizer(question, context, return_tensors=\"pt\")\n","outputs = model(**inputs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9jYw50-zzKj1"},"outputs":[],"source":["start_logits = outputs.start_logits\n","end_logits = outputs.end_logits\n","print(start_logits.shape, end_logits.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HD1g-DiazKj2"},"outputs":[],"source":["import torch\n","\n","sequence_ids = inputs.sequence_ids()\n","# Masque tout sauf les tokens du contexte\n","mask = [i != 1 for i in sequence_ids]\n","# Démasque le token [CLS]\n","mask[0] = False\n","mask = torch.tensor(mask)[None]\n","\n","start_logits[mask] = -10000\n","end_logits[mask] = -10000"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nIrc2wTCzKj3"},"outputs":[],"source":["start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)[0]\n","end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JkJFWXP_zKj4"},"outputs":[],"source":["scores = start_probabilities[:, None] * end_probabilities[None, :]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b3L7lBuszKj4"},"outputs":[],"source":["scores = torch.triu(scores)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xY8akXPnzKj5"},"outputs":[],"source":["max_index = scores.argmax().item()\n","start_index = max_index // scores.shape[1]\n","end_index = max_index % scores.shape[1]\n","print(scores[start_index, end_index])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gio6N6nkzKj6"},"outputs":[],"source":["inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n","offsets = inputs_with_offsets[\"offset_mapping\"]\n","\n","start_char, _ = offsets[start_index]\n","_, end_char = offsets[end_index]\n","answer = context[start_char:end_char]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Tod3ARfjzKj6"},"outputs":[],"source":["result = {\n","    \"answer\": answer,\n","    \"start\": start_char,\n","    \"end\": end_char,\n","    \"score\": scores[start_index, end_index],\n","}\n","print(result)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nKVYAHzczKj7"},"outputs":[],"source":["inputs = tokenizer(question, long_context)\n","print(len(inputs[\"input_ids\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_NOSMYOmzKj8"},"outputs":[],"source":["inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n","print(tokenizer.decode(inputs[\"input_ids\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kUMsTPl_zKj8"},"outputs":[],"source":["sentence = \"Cette phrase n'est pas trop longue mais nous allons la diviser quand même.\"\n","inputs = tokenizer(\n","    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",")\n","\n","for ids in inputs[\"input_ids\"]:\n","    print(tokenizer.decode(ids))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AIDdnQQgzKj9"},"outputs":[],"source":["print(inputs.keys())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pALqYjWMzKj9"},"outputs":[],"source":["print(inputs[\"overflow_to_sample_mapping\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5_JjliLfzKj-"},"outputs":[],"source":["sentences = [\n","    \"Cette phrase n'est pas trop longue mais on va quand même la diviser.\",\n","    \"Cette phrase est plus courte mais sera quand même divisée.\",\n","]\n","inputs = tokenizer(\n","    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",")\n","\n","print(inputs[\"overflow_to_sample_mapping\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CUOTTRzHzKj-"},"outputs":[],"source":["inputs = tokenizer(\n","    question,\n","    long_context,\n","    stride=128,\n","    max_length=384,\n","    padding=\"longest\",\n","    truncation=\"only_second\",\n","    return_overflowing_tokens=True,\n","    return_offsets_mapping=True,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jlLuo0IHzKj_"},"outputs":[],"source":["_ = inputs.pop(\"overflow_to_sample_mapping\")\n","offsets = inputs.pop(\"offset_mapping\")\n","\n","inputs = inputs.convert_to_tensors(\"pt\")\n","print(inputs[\"input_ids\"].shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HYa2REanzKj_"},"outputs":[],"source":["outputs = model(**inputs)\n","\n","start_logits = outputs.start_logits\n","end_logits = outputs.end_logits\n","print(start_logits.shape, end_logits.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xwZp_IIlzKj_"},"outputs":[],"source":["sequence_ids = inputs.sequence_ids()\n","# Masque tout sauf les tokens du contexte.\n","mask = [i != 1 for i in sequence_ids]\n","# Démasquer le token [CLS]\n","mask[0] = False\n","# Masquer tous les tokens [PAD]\n","mask = torch.logical_or(torch.tensor(mask)[None], (inputs[\"attention_mask\"] == 0))\n","\n","start_logits[mask] = -10000\n","end_logits[mask] = -10000"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SOU2p4uCzKj_"},"outputs":[],"source":["start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)\n","end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P0-LGDt0zKkA"},"outputs":[],"source":["candidates = []\n","for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n","    scores = start_probs[:, None] * end_probs[None, :]\n","    idx = torch.triu(scores).argmax().item()\n","\n","    start_idx = idx // scores.shape[0]\n","    end_idx = idx % scores.shape[0]\n","    score = scores[start_idx, end_idx].item()\n","    candidates.append((start_idx, end_idx, score))\n","\n","print(candidates)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d0rhyBlHzKkA"},"outputs":[],"source":["for candidate, offset in zip(candidates, offsets):\n","    start_token, end_token, score = candidate\n","    start_char, _ = offset[start_token]\n","    _, end_char = offset[end_token]\n","    answer = long_context[start_char:end_char]\n","    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n","    print(result)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section3b_tf.ipynb b/course/fr/chapter6/section3b_tf.ipynb
deleted file mode 100644
index 7a9c9532..00000000
--- a/course/fr/chapter6/section3b_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"IENfsd6OzLJk"},"source":["# Fast tokenizers in the QA pipeline (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"TFJKY2xRzLJl"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5uzrESnpzLJm"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fDkApsjczLJo"},"outputs":[],"source":["from transformers import pipeline\n","\n","question_answerer = pipeline(\"question-answering\", model=\"etalab-ia/camembert-base-squadFR-fquad-piaf\")\n","context = \"\"\"\n","🤗 Transformers s'appuie sur les trois bibliothèques d'apprentissage profond les plus populaires (Jax, PyTorch et TensorFlow) avec une intégration transparente entre elles.\n","C'est simple d'entraîner vos modèles avec l'une avant de les charger pour l'inférence avec l'autre.\n","\"\"\"\n","question = \"Quelles bibliothèques d'apprentissage profond derrière 🤗 Transformers ?\"\n","question_answerer(question=question, context=context)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KMez69qxzLJp"},"outputs":[],"source":["long_context = \"\"\"\n","🤗 Transformers : l'état de l'art du NLP\n","\n","🤗 Transformers fournit des milliers de modèles pré-entraînés pour effectuer des tâches sur des textes telles que la classification, \n","l'extraction d'informations, la réponse à des questions, le résumé de textes, la traduction, la génération de texte et plus encore dans plus de 100 langues.\n","Son objectif est de rendre le traitement automatique des langues de pointe plus facile à utiliser pour tout le monde.\n","\n","🤗 Transformers fournit des API permettant de télécharger et d'utiliser rapidement ces modèles pré-entraînés sur un texte donné, de les affiner sur vos propres ensembles de données et de les partager avec la communauté sur notre site Web.\n","puis de les partager avec la communauté sur notre hub de modèles. En même temps, chaque module python définissant une architecture est entièrement autonome et peut être modifié pour permettre des expériences de recherche rapides.\n","peut être modifié pour permettre des expériences de recherche rapides.\n","\n","Pourquoi devrais-je utiliser des transformateurs ?\n","\n","1. Des modèles de pointe faciles à utiliser :\n","  - Haute performance sur les tâches NLU et NLG.\n","  - Faible barrière à l'entrée pour les éducateurs et les praticiens.\n","  - Peu d'abstractions pour l'utilisateur avec seulement trois classes à apprendre.\n","  - Une API unifiée pour utiliser tous nos modèles pré-entraînés.\n","  - Des coûts de calcul plus faibles, une empreinte carbone réduite :\n","\n","2. Les chercheurs peuvent partager les modèles formés au lieu de toujours les reformer.\n","  - Les praticiens peuvent réduire le temps de calcul et les coûts de production.\n","  - Des dizaines d'architectures avec plus de 10 000 modèles pré-formés, certains dans plus de 100 langues.\n","\n","3. Choisissez le cadre approprié pour chaque étape de la vie d'un modèle :\n","  - Entraînez des modèles de pointe en 3 lignes de code.\n","  - Déplacez un seul modèle entre les frameworks TF2.0/PyTorch à volonté.\n","  - Choisissez de manière transparente le bon framework pour l'entraînement, l'évaluation et la production.\n","\n","4. Adaptez facilement un modèle ou un exemple à vos besoins :\n","  - Nous fournissons des exemples pour chaque architecture afin de reproduire les résultats publiés par ses auteurs originaux.\n","  - Les éléments internes des modèles sont exposés de manière aussi cohérente que possible.\n","  - Les fichiers de modèles peuvent être utilisés indépendamment de la bibliothèque pour des expériences rapides.\n","\n","🤗 Transformers s'appuie sur les trois bibliothèques d'apprentissage profond les plus populaires (Jax, PyTorch et TensorFlow) avec une intégration parfaite\n","entre elles. Il est simple d'entraîner vos modèles avec l'une avant de les charger pour l'inférence avec l'autre.\n","\"\"\"\n","question_answerer(question=question, context=long_context)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fW8C_FHezLJr"},"outputs":[],"source":["from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering\n","\n","model_checkpoint = \"etalab-ia/camembert-base-squadFR-fquad-piaf\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n","model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n","\n","inputs = tokenizer(question, context, return_tensors=\"tf\")\n","outputs = model(**inputs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wMAXGjP1zLJr"},"outputs":[],"source":["start_logits = outputs.start_logits\n","end_logits = outputs.end_logits\n","print(start_logits.shape, end_logits.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ToZUdfkMzLJs"},"outputs":[],"source":["import tensorflow as tf\n","\n","sequence_ids = inputs.sequence_ids()\n","# Masque tout sauf les tokens du contexte\n","mask = [i != 1 for i in sequence_ids]\n","# Démasque le token [CLS]\n","mask[0] = False\n","mask = tf.constant(mask)[None]\n","\n","start_logits = tf.where(mask, -10000, start_logits)\n","end_logits = tf.where(mask, -10000, end_logits)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DZIz7kcezLJt"},"outputs":[],"source":["start_probabilities = tf.math.softmax(start_logits, axis=-1)[0].numpy()\n","end_probabilities = tf.math.softmax(end_logits, axis=-1)[0].numpy()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IFLxKQNuzLJu"},"outputs":[],"source":["scores = start_probabilities[:, None] * end_probabilities[None, :]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cHYe9g9YzLJu"},"outputs":[],"source":["import numpy as np\n","\n","scores = np.triu(scores)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AE94iBHFzLJv"},"outputs":[],"source":["max_index = scores.argmax().item()\n","start_index = max_index // scores.shape[1]\n","end_index = max_index % scores.shape[1]\n","print(scores[start_index, end_index])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PjN8vv0WzLJv"},"outputs":[],"source":["inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n","offsets = inputs_with_offsets[\"offset_mapping\"]\n","\n","start_char, _ = offsets[start_index]\n","_, end_char = offsets[end_index]\n","answer = context[start_char:end_char]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pio3r0RUzLJw"},"outputs":[],"source":["result = {\n","    \"answer\": answer,\n","    \"start\": start_char,\n","    \"end\": end_char,\n","    \"score\": scores[start_index, end_index],\n","}\n","print(result)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pP47jYLzzLJw"},"outputs":[],"source":["inputs = tokenizer(question, long_context)\n","print(len(inputs[\"input_ids\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WfWfqhZ3zLJw"},"outputs":[],"source":["inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n","print(tokenizer.decode(inputs[\"input_ids\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RoqWglfNzLJy"},"outputs":[],"source":["sentence = \"Cette phrase n'est pas trop longue mais nous allons la diviser quand même.\"\n","inputs = tokenizer(\n","    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",")\n","\n","for ids in inputs[\"input_ids\"]:\n","    print(tokenizer.decode(ids))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Do20E3r5zLJy"},"outputs":[],"source":["print(inputs.keys())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a_2YbONbzLJy"},"outputs":[],"source":["print(inputs[\"overflow_to_sample_mapping\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"h9O56s4zzLJz"},"outputs":[],"source":["sentences = [\n","    \"Cette phrase n'est pas trop longue mais on va quand même la diviser.\",\n","    \"Cette phrase est plus courte mais sera quand même divisée.\",\n","]\n","inputs = tokenizer(\n","    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",")\n","\n","print(inputs[\"overflow_to_sample_mapping\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ObWr65GtzLJz"},"outputs":[],"source":["inputs = tokenizer(\n","    question,\n","    long_context,\n","    stride=128,\n","    max_length=384,\n","    padding=\"longest\",\n","    truncation=\"only_second\",\n","    return_overflowing_tokens=True,\n","    return_offsets_mapping=True,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d5-pj34mzLJz"},"outputs":[],"source":["_ = inputs.pop(\"overflow_to_sample_mapping\")\n","offsets = inputs.pop(\"offset_mapping\")\n","\n","inputs = inputs.convert_to_tensors(\"tf\")\n","print(inputs[\"input_ids\"].shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-ilIbdvCzLJ0"},"outputs":[],"source":["outputs = model(**inputs)\n","\n","start_logits = outputs.start_logits\n","end_logits = outputs.end_logits\n","print(start_logits.shape, end_logits.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d9nvNTX8zLJ0"},"outputs":[],"source":["sequence_ids = inputs.sequence_ids()\n","# Masque tout sauf les tokens du contexte.\n","mask = [i != 1 for i in sequence_ids]\n","# Démasquer le token [CLS]\n","mask[0] = False\n","# Masquer tous les tokens [PAD]\n","mask = tf.math.logical_or(tf.constant(mask)[None], inputs[\"attention_mask\"] == 0)\n","\n","start_logits = tf.where(mask, -10000, start_logits)\n","end_logits = tf.where(mask, -10000, end_logits)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pcdYiEJ_zLJ0"},"outputs":[],"source":["start_probabilities = tf.math.softmax(start_logits, axis=-1).numpy()\n","end_probabilities = tf.math.softmax(end_logits, axis=-1).numpy()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qHXwG3swzLJ1"},"outputs":[],"source":["candidates = []\n","for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n","    scores = start_probs[:, None] * end_probs[None, :]\n","    idx = np.triu(scores).argmax().item()\n","\n","    start_idx = idx // scores.shape[0]\n","    end_idx = idx % scores.shape[0]\n","    score = scores[start_idx, end_idx].item()\n","    candidates.append((start_idx, end_idx, score))\n","\n","print(candidates)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IrlNq9gUzLJ1"},"outputs":[],"source":["for candidate, offset in zip(candidates, offsets):\n","    start_token, end_token, score = candidate\n","    start_char, _ = offset[start_token]\n","    _, end_char = offset[end_token]\n","    answer = long_context[start_char:end_char]\n","    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n","    print(result)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section4.ipynb b/course/fr/chapter6/section4.ipynb
deleted file mode 100644
index 060b0c3f..00000000
--- a/course/fr/chapter6/section4.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"bTw04AYj0l6S"},"source":["# Normalisation et prétokenization."]},{"cell_type":"markdown","metadata":{"id":"4zRF9UbG0l6V"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dL_jzPty0l6V"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yOnGzPYg0l6W"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\") \n","print(type(tokenizer.backend_tokenizer))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y4jyXLqF0l6X"},"outputs":[],"source":["print(tokenizer.backend_tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xso6HUAw0l6Y"},"outputs":[],"source":["# Ne semble pas marcher sur le français\n","tokenizer_fr = AutoTokenizer.from_pretrained(\"camembert-base\") \n","tokenizer_fr.backend_tokenizer.normalizer.normalize_str(\"Bönjoùr commènt vas tü ?\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5m9PDlzl0l6Y"},"outputs":[],"source":["tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BsCx-xC20l6Z"},"outputs":[],"source":["tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n","tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9bOxCK5q0l6a"},"outputs":[],"source":["tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n","tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section5.ipynb b/course/fr/chapter6/section5.ipynb
deleted file mode 100644
index 315f796b..00000000
--- a/course/fr/chapter6/section5.ipynb
+++ /dev/null
@@ -1,333 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "L5tGkC_j050w"
-   },
-   "source": [
-    "# Tokenisation *Byte-Pair Encoding*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fjmzCwMq0504"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "D75bQauc0505"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "s4G8-gDG0506"
-   },
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"C'est le cours d'Hugging Face.\",\n",
-    "    \"Ce chapitre traite de la tokenisation.\",\n",
-    "    \"Cette section présente plusieurs algorithmes de tokenizer.\",\n",
-    "    \"Avec un peu de chance, vous serez en mesure de comprendre comment ils sont entraînés et génèrent des tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "CRb5o8vS0507"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"asi/gpt-fr-cased-small\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "K_szN_Y-0508"
-   },
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "print(word_freqs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ko-UJpnt0508"
-   },
-   "outputs": [],
-   "source": [
-    "alphabet = []\n",
-    "\n",
-    "for word in word_freqs.keys():\n",
-    "    for letter in word:\n",
-    "        if letter not in alphabet:\n",
-    "            alphabet.append(letter)\n",
-    "alphabet.sort()\n",
-    "\n",
-    "print(alphabet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "IR_iQ6Vq0509"
-   },
-   "outputs": [],
-   "source": [
-    "vocab = [\"<|endoftext|>\"] + alphabet.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "u5Avma5F050-"
-   },
-   "outputs": [],
-   "source": [
-    "splits = {word: [c for c in word] for word in word_freqs.keys()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "PVHu72DI050_"
-   },
-   "outputs": [],
-   "source": [
-    "def compute_pair_freqs(splits):\n",
-    "    pair_freqs = defaultdict(int)\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "        for i in range(len(split) - 1):\n",
-    "            pair = (split[i], split[i + 1])\n",
-    "            pair_freqs[pair] += freq\n",
-    "    return pair_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "48S6KmcB050_"
-   },
-   "outputs": [],
-   "source": [
-    "pair_freqs = compute_pair_freqs(splits)\n",
-    "\n",
-    "for i, key in enumerate(pair_freqs.keys()):\n",
-    "    print(f\"{key}: {pair_freqs[key]}\")\n",
-    "    if i >= 5:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "kVgFt81n051A"
-   },
-   "outputs": [],
-   "source": [
-    "best_pair = \"\"\n",
-    "max_freq = None\n",
-    "\n",
-    "for pair, freq in pair_freqs.items():\n",
-    "    if max_freq is None or max_freq < freq:\n",
-    "        best_pair = pair\n",
-    "        max_freq = freq\n",
-    "\n",
-    "print(best_pair, max_freq)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NVuJAlmU051B"
-   },
-   "outputs": [],
-   "source": [
-    "merges = {(\"Ġ\", \"t\"): \"Ġt\"}\n",
-    "vocab.append(\"Ġt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NWhbhqJU051C"
-   },
-   "outputs": [],
-   "source": [
-    "def merge_pair(a, b, splits):\n",
-    "    for word in word_freqs:\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "\n",
-    "        i = 0\n",
-    "        while i < len(split) - 1:\n",
-    "            if split[i] == a and split[i + 1] == b:\n",
-    "                split = split[:i] + [a + b] + split[i + 2 :]\n",
-    "            else:\n",
-    "                i += 1\n",
-    "        splits[word] = split\n",
-    "    return splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rJN4TLmm051C"
-   },
-   "outputs": [],
-   "source": [
-    "splits = merge_pair(\"Ġ\", \"t\", splits)\n",
-    "splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ElXK18p5051D"
-   },
-   "outputs": [],
-   "source": [
-    "vocab_size = 50\n",
-    "\n",
-    "while len(vocab) < vocab_size:\n",
-    "    pair_freqs = compute_pair_freqs(splits)\n",
-    "    best_pair = \"\"\n",
-    "    max_freq = None\n",
-    "    for pair, freq in pair_freqs.items():\n",
-    "        if max_freq is None or max_freq < freq:\n",
-    "            best_pair = pair\n",
-    "            max_freq = freq\n",
-    "    splits = merge_pair(*best_pair, splits)\n",
-    "    merges[best_pair] = best_pair[0] + best_pair[1]\n",
-    "    vocab.append(best_pair[0] + best_pair[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ehoZwJgB051D"
-   },
-   "outputs": [],
-   "source": [
-    "print(merges)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Fn5zl1AC051E"
-   },
-   "outputs": [],
-   "source": [
-    "print(vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tCCJYkXU051E"
-   },
-   "outputs": [],
-   "source": [
-    "def tokenize(text):\n",
-    "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
-    "    splits = [[l for l in word] for word in pre_tokenized_text]\n",
-    "    for pair, merge in merges.items():\n",
-    "        for idx, split in enumerate(splits):\n",
-    "            i = 0\n",
-    "            while i < len(split) - 1:\n",
-    "                if split[i] == pair[0] and split[i + 1] == pair[1]:\n",
-    "                    split = split[:i] + [merge] + split[i + 2 :]\n",
-    "                else:\n",
-    "                    i += 1\n",
-    "            splits[idx] = split\n",
-    "\n",
-    "    return sum(splits, [])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ri4HzbPD051E"
-   },
-   "outputs": [],
-   "source": [
-    "tokenize(\"Ce n'est pas un token.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter6/section6.ipynb b/course/fr/chapter6/section6.ipynb
deleted file mode 100644
index ea9acb42..00000000
--- a/course/fr/chapter6/section6.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"wkvsrEMU06Q_"},"source":["# WordPiece tokenization\n","Aucun modèle en français utilise WordPiece. Nous utilisons ici CamemBERT utilise SentencePiece."]},{"cell_type":"markdown","metadata":{"id":"cpjgfr_k06RB"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kctpypJn06RC"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WrhLg7Xm06RD"},"outputs":[],"source":["corpus = [\n","    \"C'est le cours d'Hugging Face.\",\n","    \"Ce chapitre traite de la tokenisation.\",\n","    \"Cette section présente plusieurs algorithmes de tokenizer.\",\n","    \"Avec un peu de chance, vous serez en mesure de comprendre comment ils sont entraînés et génèrent des tokens.\",\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MN6DOnQu06RF"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_Fw7oyhW06RF"},"outputs":[],"source":["from collections import defaultdict\n","\n","word_freqs = defaultdict(int)\n","for text in corpus:\n","    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n","    new_words = [word for word, offset in words_with_offsets]\n","    for word in new_words:\n","        word_freqs[word] += 1\n","\n","word_freqs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c9p0iRdR06RI"},"outputs":[],"source":["alphabet = []\n","for word in word_freqs.keys():\n","    if word[0] not in alphabet:\n","        alphabet.append(word[0])\n","    for letter in word[1:]:\n","        if f\"##{letter}\" not in alphabet:\n","            alphabet.append(f\"##{letter}\")\n","\n","alphabet.sort()\n","alphabet\n","\n","print(alphabet)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LbXxxvu006RJ"},"outputs":[],"source":["vocab = [\"[PAD]\", \"[UNK]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"] + alphabet.copy()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PnvupQzg06RK"},"outputs":[],"source":["splits = {\n","    word: [c if i == 0 else f\"##{c}\" for i, c in enumerate(word)]\n","    for word in word_freqs.keys()\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EWMQgBOg06RK"},"outputs":[],"source":["def compute_pair_scores(splits):\n","    letter_freqs = defaultdict(int)\n","    pair_freqs = defaultdict(int)\n","    for word, freq in word_freqs.items():\n","        split = splits[word]\n","        if len(split) == 1:\n","            letter_freqs[split[0]] += freq\n","            continue\n","        for i in range(len(split) - 1):\n","            pair = (split[i], split[i + 1])\n","            letter_freqs[split[i]] += freq\n","            pair_freqs[pair] += freq\n","        letter_freqs[split[-1]] += freq\n","\n","    scores = {\n","        pair: freq / (letter_freqs[pair[0]] * letter_freqs[pair[1]])\n","        for pair, freq in pair_freqs.items()\n","    }\n","    return scores"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0j8XJljD06RL"},"outputs":[],"source":["pair_scores = compute_pair_scores(splits)\n","for i, key in enumerate(pair_scores.keys()):\n","    print(f\"{key}: {pair_scores[key]}\")\n","    if i >= 5:\n","        break"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PEAT0sU_06RM"},"outputs":[],"source":["best_pair = \"\"\n","max_score = None\n","for pair, score in pair_scores.items():\n","    if max_score is None or max_score < score:\n","        best_pair = pair\n","        max_score = score\n","\n","print(best_pair, max_score)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lnOXkODY06RN"},"outputs":[],"source":["vocab.append(\"ab\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Hnlgcvxr06RO"},"outputs":[],"source":["def merge_pair(a, b, splits):\n","    for word in word_freqs:\n","        split = splits[word]\n","        if len(split) == 1:\n","            continue\n","        i = 0\n","        while i < len(split) - 1:\n","            if split[i] == a and split[i + 1] == b:\n","                merge = a + b[2:] if b.startswith(\"##\") else a + b\n","                split = split[:i] + [merge] + split[i + 2 :]\n","            else:\n","                i += 1\n","        splits[word] = split\n","    return splits"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0UeDsGSh06RO"},"outputs":[],"source":["splits = merge_pair(\"a\", \"##b\", splits)\n","splits"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WvDCpx6_06RP"},"outputs":[],"source":["vocab_size = 70\n","while len(vocab) < vocab_size:\n","    scores = compute_pair_scores(splits)\n","    best_pair, max_score = \"\", None\n","    for pair, score in scores.items():\n","        if max_score is None or max_score < score:\n","            best_pair = pair\n","            max_score = score\n","    splits = merge_pair(*best_pair, splits)\n","    new_token = (\n","        best_pair[0] + best_pair[1][2:]\n","        if best_pair[1].startswith(\"##\")\n","        else best_pair[0] + best_pair[1]\n","    )\n","    vocab.append(new_token)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gyeoGUD606RP"},"outputs":[],"source":["print(vocab)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"epdKzYJK06RQ"},"outputs":[],"source":["def encode_word(word):\n","    tokens = []\n","    while len(word) > 0:\n","        i = len(word)\n","        while i > 0 and word[:i] not in vocab:\n","            i -= 1\n","        if i == 0:\n","            return [\"[UNK]\"]\n","        tokens.append(word[:i])\n","        word = word[i:]\n","        if len(word) > 0:\n","            word = f\"##{word}\"\n","    return tokens"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x3IjZTjr06RQ"},"outputs":[],"source":["print(encode_word(\"Hugging\"))\n","print(encode_word(\"HOgging\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vAnKjXfg06RQ"},"outputs":[],"source":["def tokenize(text):\n","    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n","    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n","    encoded_words = [encode_word(word) for word in pre_tokenized_text]\n","    return sum(encoded_words, [])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XcDmJaVr06RR"},"outputs":[],"source":["tokenize(\"C'est le cours d'Hugging Face !\")"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section7.ipynb b/course/fr/chapter6/section7.ipynb
deleted file mode 100644
index d7f67c0f..00000000
--- a/course/fr/chapter6/section7.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"Ioa9vLGB065t"},"source":["# Unigram tokenization\n","\n","Nous gardons un modèle en anglais ici car il n'existe pas de modèle en français utilisant la tokenisation Unigram."]},{"cell_type":"markdown","metadata":{"id":"h_28r_zV065v"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N04G03ix0659"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5ddtmjjT065-"},"outputs":[],"source":["corpus = [\n","    \"This is the Hugging Face Course.\",\n","    \"This chapter is about tokenization.\",\n","    \"This section shows several tokenizer algorithms.\",\n","    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mRHoA0SB065-"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"xlnet-base-cased\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"K_Ua6ANy065_"},"outputs":[],"source":["from collections import defaultdict\n","\n","word_freqs = defaultdict(int)\n","for text in corpus:\n","    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n","    new_words = [word for word, offset in words_with_offsets]\n","    for word in new_words:\n","        word_freqs[word] += 1\n","\n","word_freqs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oCWyxMJL065_"},"outputs":[],"source":["char_freqs = defaultdict(int)\n","subwords_freqs = defaultdict(int)\n","for word, freq in word_freqs.items():\n","    for i in range(len(word)):\n","        char_freqs[word[i]] += freq\n","        # Boucle à travers les sous-mots de longueur au moins égale à 2\n","        for j in range(i + 2, len(word) + 1):\n","            subwords_freqs[word[i:j]] += freq\n","\n","# Trier les sous-mots par fréquence\n","sorted_subwords = sorted(subwords_freqs.items(), key=lambda x: x[1], reverse=True)\n","sorted_subwords[:10]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mAQA11lr066A"},"outputs":[],"source":["token_freqs = list(char_freqs.items()) + sorted_subwords[: 300 - len(char_freqs)]\n","token_freqs = {token: freq for token, freq in token_freqs}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FVFQVHJ0066A"},"outputs":[],"source":["from math import log\n","\n","total_sum = sum([freq for token, freq in token_freqs.items()])\n","model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hq8iaZTd066B"},"outputs":[],"source":["def encode_word(word, model):\n","    best_segmentations = [{\"start\": 0, \"score\": 1}] + [\n","        {\"start\": None, \"score\": None} for _ in range(len(word))\n","    ]\n","    for start_idx in range(len(word)):\n","        # Il doit être correctement rempli par les étapes précédentes de la boucle.\n","        best_score_at_start = best_segmentations[start_idx][\"score\"]\n","        for end_idx in range(start_idx + 1, len(word) + 1):\n","            token = word[start_idx:end_idx]\n","            if token in model and best_score_at_start is not None:\n","                score = model[token] + best_score_at_start\n","                # Si nous avons trouvé une meilleure segmentation se terminant à end_idx, nous mettons à jour\n","                if (\n","                    best_segmentations[end_idx][\"score\"] is None\n","                    or best_segmentations[end_idx][\"score\"] > score\n","                ):\n","                    best_segmentations[end_idx] = {\"start\": start_idx, \"score\": score}\n","\n","    segmentation = best_segmentations[-1]\n","    if segmentation[\"score\"] is None:\n","        # Nous n'avons pas trouvé de tokenization du mot -> inconnu\n","        return [\"<unk>\"], None\n","\n","    score = segmentation[\"score\"]\n","    start = segmentation[\"start\"]\n","    end = len(word)\n","    tokens = []\n","    while start != 0:\n","        tokens.insert(0, word[start:end])\n","        next_start = best_segmentations[start][\"start\"]\n","        end = start\n","        start = next_start\n","    tokens.insert(0, word[start:end])\n","    return tokens, score"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dm9IqKEo066C"},"outputs":[],"source":["print(encode_word(\"Hopefully\", model))\n","print(encode_word(\"This\", model))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"snJI7OWW066D"},"outputs":[],"source":["def compute_loss(model):\n","    loss = 0\n","    for word, freq in word_freqs.items():\n","        _, word_loss = encode_word(word, model)\n","        loss += freq * word_loss\n","    return loss"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KTWZENVe066D"},"outputs":[],"source":["compute_loss(model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qnJwWynL066E"},"outputs":[],"source":["import copy\n","\n","\n","def compute_scores(model):\n","    scores = {}\n","    model_loss = compute_loss(model)\n","    for token, score in model.items():\n","        # Nous gardons toujours les tokens de longueur 1.\n","        if len(token) == 1:\n","            continue\n","        model_without_token = copy.deepcopy(model)\n","        _ = model_without_token.pop(token)\n","        scores[token] = compute_loss(model_without_token) - model_loss\n","    return scores"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aMryFMGk066E"},"outputs":[],"source":["scores = compute_scores(model)\n","print(scores[\"ll\"])\n","print(scores[\"his\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NWSe5qDH066E"},"outputs":[],"source":["percent_to_remove = 0.1\n","while len(model) > 100:\n","    scores = compute_scores(model)\n","    sorted_scores = sorted(scores.items(), key=lambda x: x[1])\n","    # Supprime les tokens percent_to_remove ayant les scores les plus bas.\n","    for i in range(int(len(model) * percent_to_remove)):\n","        _ = token_freqs.pop(sorted_scores[i][0])\n","\n","    total_sum = sum([freq for token, freq in token_freqs.items()])\n","    model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"34dJBRIr066G"},"outputs":[],"source":["def tokenize(text, model):\n","    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n","    pre_tokenized_text = [word for word, offset in words_with_offsets]\n","    encoded_words = [encode_word(word, model)[0] for word in pre_tokenized_text]\n","    return sum(encoded_words, [])\n","\n","\n","tokenize(\"This is the Hugging Face course.\", model)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter6/section8.ipynb b/course/fr/chapter6/section8.ipynb
deleted file mode 100644
index d6cee4d3..00000000
--- a/course/fr/chapter6/section8.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"T5FIcVH607Ng"},"source":["# Construction d'un *tokenizer*, bloc par bloc"]},{"cell_type":"markdown","metadata":{"id":"T8opLuzk07Nh"},"source":["Installez les bibliothèques 🤗 *Transformers* et 🤗 *Datasets* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Eefo6q1t07Nj"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kQ5gnob207Nj"},"outputs":[],"source":["from datasets import load_dataset\n","\n","dataset = load_dataset(\"wikitext\", name=\"wikitext-2-raw-v1\", split=\"train\")\n","\n","\n","def get_training_corpus():\n","    for i in range(0, len(dataset), 1000):\n","        yield dataset[i : i + 1000][\"text\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FtdeEGNp07Nl"},"outputs":[],"source":["with open(\"wikitext-2.txt\", \"w\", encoding=\"utf-8\") as f:\n","    for i in range(len(dataset)):\n","        f.write(dataset[i][\"text\"] + \"\\n\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qp2r3WvE07Nm"},"outputs":[],"source":["from tokenizers import (\n","    decoders,\n","    models,\n","    normalizers,\n","    pre_tokenizers,\n","    processors,\n","    trainers,\n","    Tokenizer,\n",")\n","\n","tokenizer = Tokenizer(models.WordPiece(unk_token=\"[UNK]\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_MNi4F9P07Nm"},"outputs":[],"source":["tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2Bnu6rV507Nn"},"outputs":[],"source":["tokenizer.normalizer = normalizers.Sequence(\n","    [normalizers.NFD(), normalizers.Lowercase(), normalizers.StripAccents()]\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Q7ACdwVb07No"},"outputs":[],"source":["print(tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ukH4cfLm07No"},"outputs":[],"source":["tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vA4zUAzA07Np"},"outputs":[],"source":["tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XVxC0Gt-07Np"},"outputs":[],"source":["tokenizer.pre_tokenizer.pre_tokenize_str(\"Testons le prétokeniseur.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_nTpJRMc07Nq"},"outputs":[],"source":["pre_tokenizer = pre_tokenizers.WhitespaceSplit()\n","pre_tokenizer.pre_tokenize_str(\"Testons le prétokeniseur.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uhHfccXl07Nq"},"outputs":[],"source":["pre_tokenizer = pre_tokenizers.Sequence(\n","    [pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Punctuation()]\n",")\n","pre_tokenizer.pre_tokenize_str(\"Testons le prétokeniseur.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wpQVQCks07Ns"},"outputs":[],"source":["special_tokens = [\"[UNK]\", \"[PAD]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"]\n","trainer = trainers.WordPieceTrainer(vocab_size=25000, special_tokens=special_tokens)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"K35cGUZ507Ns"},"outputs":[],"source":["tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GleCeJZO07Ns"},"outputs":[],"source":["tokenizer.model = models.WordPiece(unk_token=\"[UNK]\")\n","tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C_Gk2Pi207Nt"},"outputs":[],"source":["encoding = tokenizer.encode(\"Testons le prétokeniseur.\")\n","print(encoding.tokens)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HlxliwiP07Nu"},"outputs":[],"source":["cls_token_id = tokenizer.token_to_id(\"[CLS]\")\n","sep_token_id = tokenizer.token_to_id(\"[SEP]\")\n","print(cls_token_id, sep_token_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bwAX6AJH07Nu"},"outputs":[],"source":["tokenizer.post_processor = processors.TemplateProcessing(\n","    single=f\"[CLS]:0 $A:0 [SEP]:0\",\n","    pair=f\"[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1\",\n","    special_tokens=[(\"[CLS]\", cls_token_id), (\"[SEP]\", sep_token_id)],\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QLaSbns907Nv"},"outputs":[],"source":["encoding = tokenizer.encode(\"Testons le prétokeniseur.\")\n","print(encoding.tokens)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pKPhH6X307Nv"},"outputs":[],"source":["encoding = tokenizer.encode(\"Testons le prétokeniseur...\", \"sur des phrases.\")\n","print(encoding.tokens)\n","print(encoding.type_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-TOrOB7307Nw"},"outputs":[],"source":["tokenizer.decoder = decoders.WordPiece(prefix=\"##\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fM4LSVX507Nw"},"outputs":[],"source":["tokenizer.decode(encoding.ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SdLkZeRX07Nx"},"outputs":[],"source":["tokenizer.save(\"tokenizer.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BpELHznB07Nx"},"outputs":[],"source":["new_tokenizer = Tokenizer.from_file(\"tokenizer.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qlOU0FUR07Nx"},"outputs":[],"source":["from transformers import PreTrainedTokenizerFast\n","\n","wrapped_tokenizer = PreTrainedTokenizerFast(\n","    tokenizer_object=tokenizer,\n","    # tokenizer_file=\"tokenizer.json\", # Vous pouvez charger à partir du fichier tokenizer, alternativement\n","    unk_token=\"[UNK]\",\n","    pad_token=\"[PAD]\",\n","    cls_token=\"[CLS]\",\n","    sep_token=\"[SEP]\",\n","    mask_token=\"[MASK]\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZIAHV3UI07Nx"},"outputs":[],"source":["from transformers import BertTokenizerFast\n","\n","wrapped_tokenizer = BertTokenizerFast(tokenizer_object=tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vzmSTKC007Ny"},"outputs":[],"source":["tokenizer = Tokenizer(models.BPE())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8k6rYiCI07Ny"},"outputs":[],"source":["tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YmTCop9Y07Ny"},"outputs":[],"source":["tokenizer.pre_tokenizer.pre_tokenize_str(\"Testons la prétokenisation !\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4Oqo_MBy07Nz"},"outputs":[],"source":["trainer = trainers.BpeTrainer(vocab_size=25000, special_tokens=[\"<|endoftext|>\"])\n","tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-T280IzH07Nz"},"outputs":[],"source":["tokenizer.model = models.BPE()\n","tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"armLUMSH07N0"},"outputs":[],"source":["encoding = tokenizer.encode(\"Testons ce tokeniseur.\")\n","print(encoding.tokens)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"41y3LS8H07N1"},"outputs":[],"source":["tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hv3PhDhJ07N2"},"outputs":[],"source":["sentence = \"Testons ce tokeniseur.\"\n","encoding = tokenizer.encode(sentence)\n","start, end = encoding.offsets[4]\n","sentence[start:end]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uxikGGQy07N2"},"outputs":[],"source":["tokenizer.decoder = decoders.ByteLevel()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ikkQxO_V07N2"},"outputs":[],"source":["tokenizer.decode(encoding.ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MqPeY6cT07N2"},"outputs":[],"source":["from transformers import PreTrainedTokenizerFast\n","\n","wrapped_tokenizer = PreTrainedTokenizerFast(\n","    tokenizer_object=tokenizer,\n","    bos_token=\"<|endoftext|>\",\n","    eos_token=\"<|endoftext|>\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tvXFpL5p07N3"},"outputs":[],"source":["from transformers import GPT2TokenizerFast\n","\n","wrapped_tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7D7g-hS007N3"},"outputs":[],"source":["tokenizer = Tokenizer(models.Unigram())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eWbSREiG07N3"},"outputs":[],"source":["from tokenizers import Regex\n","\n","tokenizer.normalizer = normalizers.Sequence(\n","    [\n","        normalizers.Replace(\"``\", '\"'),\n","        normalizers.Replace(\"''\", '\"'),\n","        normalizers.NFKD(),\n","        normalizers.StripAccents(),\n","        normalizers.Replace(Regex(\" {2,}\"), \" \"),\n","    ]\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SArHTC7907N4"},"outputs":[],"source":["tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TciBqdkX07N4"},"outputs":[],"source":["tokenizer.pre_tokenizer.pre_tokenize_str(\"Testons ce prétokeniseur !\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Y-E1vvnT07N4"},"outputs":[],"source":["special_tokens = [\"<cls>\", \"<sep>\", \"<unk>\", \"<pad>\", \"<mask>\", \"<s>\", \"</s>\"]\n","trainer = trainers.UnigramTrainer(\n","    vocab_size=25000, special_tokens=special_tokens, unk_token=\"<unk>\"\n",")\n","tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hMMrOhpk07N4"},"outputs":[],"source":["tokenizer.model = models.Unigram()\n","tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wCMrJJdz07N4"},"outputs":[],"source":["encoding = tokenizer.encode(\"Testons ce prétokeniseur.\")\n","print(encoding.tokens)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W3vaoO1i07N5"},"outputs":[],"source":["cls_token_id = tokenizer.token_to_id(\"<cls>\")\n","sep_token_id = tokenizer.token_to_id(\"<sep>\")\n","print(cls_token_id, sep_token_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LdbbJ-ZF07N5"},"outputs":[],"source":["tokenizer.post_processor = processors.TemplateProcessing(\n","    single=\"$A:0 <sep>:0 <cls>:2\",\n","    pair=\"$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2\",\n","    special_tokens=[(\"<sep>\", sep_token_id), (\"<cls>\", cls_token_id)],\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"588bSCIk07N6"},"outputs":[],"source":["encoding = tokenizer.encode(\"Testons ce tokeniseur...\", \"sur des phrases !\")\n","print(encoding.tokens)\n","print(encoding.type_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"h0eHTDAm07N6"},"outputs":[],"source":["tokenizer.decoder = decoders.Metaspace()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"URLwRgSp07N7"},"outputs":[],"source":["from transformers import PreTrainedTokenizerFast\n","\n","wrapped_tokenizer = PreTrainedTokenizerFast(\n","    tokenizer_object=tokenizer,\n","    bos_token=\"<s>\",\n","    eos_token=\"</s>\",\n","    unk_token=\"<unk>\",\n","    pad_token=\"<pad>\",\n","    cls_token=\"<cls>\",\n","    sep_token=\"<sep>\",\n","    mask_token=\"<mask>\",\n","    padding_side=\"left\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z2Z0EeqE07N7"},"outputs":[],"source":["from transformers import XLNetTokenizerFast\n","\n","wrapped_tokenizer = XLNetTokenizerFast(tokenizer_object=tokenizer)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section2_pt.ipynb b/course/fr/chapter7/section2_pt.ipynb
deleted file mode 100644
index d7840d81..00000000
--- a/course/fr/chapter7/section2_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"eEsMkSWVeJfL"},"source":["# Classification de token (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"3JFBjaIteJfN"},"source":["Installez les bibliothèques 🤗 *Datasets*, 🤗 *Transformers* et 🤗 *Accelerate*  pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PISJ37zgeJfO"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install accelerate\n","# Pour exécuter l'entraînement sur TPU, vous devrez décommenter la ligne suivante:\n","# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"tiT2suJreJfQ"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e7imC8-WeJfR"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"ZN1JLYtYeJfS"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cQcxVJzceJfT"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sedh4Vb_eJfT"},"outputs":[],"source":["from datasets import load_dataset\n","\n","raw_datasets = load_dataset(\"wikiann\",\"fr\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MwBo_RygeJfU"},"outputs":[],"source":["raw_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vBTciPcoeJfV"},"outputs":[],"source":["raw_datasets[\"train\"][0][\"tokens\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UTzPbqUReJfW"},"outputs":[],"source":["raw_datasets[\"train\"][0][\"ner_tags\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DN8V28xJeJfX"},"outputs":[],"source":["ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n","ner_feature"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Nt_FsTiIeJfX"},"outputs":[],"source":["label_names = ner_feature.feature.names\n","label_names"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hcDlL4ETeJfY"},"outputs":[],"source":["words = raw_datasets[\"train\"][0][\"tokens\"]\n","labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n","line1 = \"\"\n","line2 = \"\"\n","for word, label in zip(words, labels):\n","    full_label = label_names[label]\n","    max_length = max(len(word), len(full_label))\n","    line1 += word + \" \" * (max_length - len(word) + 1)\n","    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n","\n","print(line1)\n","print(line2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3OHEuCSgeJfZ"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","model_checkpoint = \"camembert-base\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"69sYNARPeJfa"},"outputs":[],"source":["tokenizer.is_fast"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6CPVemzoeJfa"},"outputs":[],"source":["inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n","inputs.tokens()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"w9NB-CAueJfb"},"outputs":[],"source":["inputs.word_ids()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Db7Hh92reJfc"},"outputs":[],"source":["def align_labels_with_tokens(labels, word_ids):\n","    new_labels = []\n","    current_word = None\n","    for word_id in word_ids:\n","        if word_id != current_word:\n","            # Début d'un nouveau mot !\n","            current_word = word_id\n","            label = -100 if word_id is None else labels[word_id]\n","            new_labels.append(label)\n","        elif word_id is None:\n","            # Token special\n","            new_labels.append(-100)\n","        else:\n","            # Même mot que le token précédent\n","            label = labels[word_id]\n","            # Si l'étiquette est B-XXX, nous la changeons en I-XXX\n","            if label % 2 == 1:\n","                label += 1\n","            new_labels.append(label)\n","\n","    return new_labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bJB6w2QmeJfc"},"outputs":[],"source":["labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n","word_ids = inputs.word_ids()\n","print(labels)\n","print(align_labels_with_tokens(labels, word_ids))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lJTYD6OOeJfd"},"outputs":[],"source":["def tokenize_and_align_labels(examples):\n","    tokenized_inputs = tokenizer(\n","        examples[\"tokens\"], truncation=True, is_split_into_words=True\n","    )\n","    all_labels = examples[\"ner_tags\"]\n","    new_labels = []\n","    for i, labels in enumerate(all_labels):\n","        word_ids = tokenized_inputs.word_ids(i)\n","        new_labels.append(align_labels_with_tokens(labels, word_ids))\n","\n","    tokenized_inputs[\"labels\"] = new_labels\n","    return tokenized_inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HqHhsQwzeJfd"},"outputs":[],"source":["tokenized_datasets = raw_datasets.map(\n","    tokenize_and_align_labels,\n","    batched=True,\n","    remove_columns=raw_datasets[\"train\"].column_names,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5Hc4bEuleJfe"},"outputs":[],"source":["from transformers import DataCollatorForTokenClassification\n","\n","data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-uq9UsMweJfe"},"outputs":[],"source":["batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n","batch[\"labels\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0rJLK42xeJff"},"outputs":[],"source":["for i in range(2):\n","    print(tokenized_datasets[\"train\"][i][\"labels\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YCpX_bX7eJff"},"outputs":[],"source":["!pip install seqeval"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sCRupfofeJff"},"outputs":[],"source":["from datasets import load_metric\n","\n","metric = load_metric(\"seqeval\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aZgi8X-VeJfg"},"outputs":[],"source":["labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n","labels = [label_names[i] for i in labels]\n","labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vAlCjzL5eJfg"},"outputs":[],"source":["predictions = labels.copy()\n","predictions[2] = \"O\"\n","metric.compute(predictions=[predictions], references=[labels])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hNvAgiq-eJfh"},"outputs":[],"source":["import numpy as np\n","\n","\n","def compute_metrics(eval_preds):\n","    logits, labels = eval_preds\n","    predictions = np.argmax(logits, axis=-1)\n","\n","    # Suppression de l'index ignoré (tokens spéciaux) et conversion en étiquettes\n","    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n","    true_predictions = [\n","        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n","        for prediction, label in zip(predictions, labels)\n","    ]\n","    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n","    return {\n","        \"precision\": all_metrics[\"overall_precision\"],\n","        \"recall\": all_metrics[\"overall_recall\"],\n","        \"f1\": all_metrics[\"overall_f1\"],\n","        \"accuracy\": all_metrics[\"overall_accuracy\"],\n","    }"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wfHpt9JbeJfh"},"outputs":[],"source":["id2label = {str(i): label for i, label in enumerate(label_names)}\n","label2id = {v: k for k, v in id2label.items()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ducCPxoceJfh"},"outputs":[],"source":["from transformers import AutoModelForTokenClassification\n","\n","model = AutoModelForTokenClassification.from_pretrained(\n","    model_checkpoint,\n","    id2label=id2label,\n","    label2id=label2id,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JVpSikKBeJfi"},"outputs":[],"source":["model.config.num_labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U2ocjiXpeJfi"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7Q4dm05oeJfj"},"outputs":[],"source":["from transformers import TrainingArguments\n","\n","args = TrainingArguments(\n","    \"camembert-finetuned-ner\",\n","    evaluation_strategy=\"epoch\",\n","    save_strategy=\"epoch\",\n","    learning_rate=2e-5,\n","    num_train_epochs=3,\n","    weight_decay=0.01,\n","    push_to_hub=True,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cc23FLcEeJfj"},"outputs":[],"source":["from transformers import Trainer\n","\n","trainer = Trainer(\n","    model=model,\n","    args=args,\n","    train_dataset=tokenized_datasets[\"train\"],\n","    eval_dataset=tokenized_datasets[\"validation\"],\n","    data_collator=data_collator,\n","    compute_metrics=compute_metrics,\n","    tokenizer=tokenizer,\n",")\n","trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x5RN0JhheJfk"},"outputs":[],"source":["trainer.push_to_hub(commit_message=\"Training complete\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UXivXl7OeJfk"},"outputs":[],"source":["from torch.utils.data import DataLoader\n","\n","train_dataloader = DataLoader(\n","    tokenized_datasets[\"train\"],\n","    shuffle=True,\n","    collate_fn=data_collator,\n","    batch_size=8,\n",")\n","eval_dataloader = DataLoader(\n","    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p3taPiJLeJfk"},"outputs":[],"source":["model = AutoModelForTokenClassification.from_pretrained(\n","    model_checkpoint,\n","    id2label=id2label,\n","    label2id=label2id,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b7L1mMNdeJfl"},"outputs":[],"source":["from torch.optim import AdamW\n","\n","optimizer = AdamW(model.parameters(), lr=2e-5)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s-obCcy-eJfl"},"outputs":[],"source":["from accelerate import Accelerator\n","\n","accelerator = Accelerator()\n","model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n","    model, optimizer, train_dataloader, eval_dataloader\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q8TW-W4reJfm"},"outputs":[],"source":["from transformers import get_scheduler\n","\n","num_train_epochs = 3\n","num_update_steps_per_epoch = len(train_dataloader)\n","num_training_steps = num_train_epochs * num_update_steps_per_epoch\n","\n","lr_scheduler = get_scheduler(\n","    \"linear\",\n","    optimizer=optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=num_training_steps,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XP8lOYQoeJfm"},"outputs":[],"source":["from huggingface_hub import Repository, get_full_repo_name\n","\n","model_name = \"camembert-finetuned-ner-accelerate\"\n","repo_name = get_full_repo_name(model_name)\n","repo_name"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"upU7Qm5YeJfn"},"outputs":[],"source":["output_dir = \"camembert-finetuned-ner-accelerate\"\n","repo = Repository(output_dir, clone_from=repo_name)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nM0cVZqleJfn"},"outputs":[],"source":["def postprocess(predictions, labels):\n","    predictions = predictions.detach().cpu().clone().numpy()\n","    labels = labels.detach().cpu().clone().numpy()\n","\n","    # Suppression de l'index ignoré (tokens spéciaux) et conversion en étiquettes\n","    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n","    true_predictions = [\n","        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n","        for prediction, label in zip(predictions, labels)\n","    ]\n","    return true_labels, true_predictions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tdtWjaaPeJfn"},"outputs":[],"source":["from tqdm.auto import tqdm\n","import torch\n","\n","progress_bar = tqdm(range(num_training_steps))\n","\n","for epoch in range(num_train_epochs):\n","    # Entraînement\n","    model.train()\n","    for batch in train_dataloader:\n","        outputs = model(**batch)\n","        loss = outputs.loss\n","        accelerator.backward(loss)\n","\n","        optimizer.step()\n","        lr_scheduler.step()\n","        optimizer.zero_grad()\n","        progress_bar.update(1)\n","\n","    # Evaluation\n","    model.eval()\n","    for batch in eval_dataloader:\n","        with torch.no_grad():\n","            outputs = model(**batch)\n","\n","        predictions = outputs.logits.argmax(dim=-1)\n","        labels = batch[\"labels\"]\n","\n","        # Nécessaire pour rembourrer les prédictions et les étiquettes à rassembler\n","        predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)\n","        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n","\n","        predictions_gathered = accelerator.gather(predictions)\n","        labels_gathered = accelerator.gather(labels)\n","\n","        true_predictions, true_labels = postprocess(predictions_gathered, labels_gathered)\n","        metric.add_batch(predictions=true_predictions, references=true_labels)\n","\n","    results = metric.compute()\n","    print(\n","        f\"epoch {epoch}:\",\n","        {\n","            key: results[f\"overall_{key}\"]\n","            for key in [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n","        },\n","    )\n","\n","    # Sauvegarder et télécharger\n","    accelerator.wait_for_everyone()\n","    unwrapped_model = accelerator.unwrap_model(model)\n","    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n","    if accelerator.is_main_process:\n","        tokenizer.save_pretrained(output_dir)\n","        repo.push_to_hub(\n","            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n","        )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MrdBclQ4eJfo"},"outputs":[],"source":["accelerator.wait_for_everyone()\n","unwrapped_model = accelerator.unwrap_model(model)\n","unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RNOI8uPkeJfo"},"outputs":[],"source":["from transformers import pipeline\n","\n","# Remplacez par votre propre checkpoint\n","model_checkpoint = \"huggingface-course/camembert-finetuned-ner\"\n","token_classifier = pipeline(\n","    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",")\n","token_classifier(\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\")"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section2_tf.ipynb b/course/fr/chapter7/section2_tf.ipynb
deleted file mode 100644
index 9b6ed250..00000000
--- a/course/fr/chapter7/section2_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"66BqbnsyeJhy"},"source":["# Classification de token (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"jYFSj10BeJh0"},"source":["Installez les bibliothèques 🤗 *Datasets*, 🤗 *Transformers* et 🤗 *Accelerate*  pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2nlx8iiUeJh2"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"1zy96qUbeJh5"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"REUDT7bEeJh7"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"e9w47W4aeJh8"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"87pR9AHWeJh9"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DKUp34igeJh9"},"outputs":[],"source":["from datasets import load_dataset\n","\n","raw_datasets = load_dataset(\"wikiann\",\"fr\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JOSGslMOeJh-"},"outputs":[],"source":["raw_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2LIzeE4deJiA"},"outputs":[],"source":["raw_datasets[\"train\"][0][\"tokens\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pE7bU0fOeJiA"},"outputs":[],"source":["raw_datasets[\"train\"][0][\"ner_tags\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kAC5v8DHeJiB"},"outputs":[],"source":["ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n","ner_feature"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QQuJ6sOSeJiC"},"outputs":[],"source":["label_names = ner_feature.feature.names\n","label_names"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dhzU6DQgeJiD"},"outputs":[],"source":["words = raw_datasets[\"train\"][0][\"tokens\"]\n","labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n","line1 = \"\"\n","line2 = \"\"\n","for word, label in zip(words, labels):\n","    full_label = label_names[label]\n","    max_length = max(len(word), len(full_label))\n","    line1 += word + \" \" * (max_length - len(word) + 1)\n","    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n","\n","print(line1)\n","print(line2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ukb6erAeJiE"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","model_checkpoint = \"camembert-base\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hldTWv0-eJiF"},"outputs":[],"source":["tokenizer.is_fast"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3K9rk_DMeJiF"},"outputs":[],"source":["inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n","inputs.tokens()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9y2rOgoXeJiH"},"outputs":[],"source":["inputs.word_ids()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qbuxqHKJeJiH"},"outputs":[],"source":["def align_labels_with_tokens(labels, word_ids):\n","    new_labels = []\n","    current_word = None\n","    for word_id in word_ids:\n","        if word_id != current_word:\n","            # Début d'un nouveau mot !\n","            current_word = word_id\n","            label = -100 if word_id is None else labels[word_id]\n","            new_labels.append(label)\n","        elif word_id is None:\n","            # Token special\n","            new_labels.append(-100)\n","        else:\n","            # Même mot que le token précédent\n","            label = labels[word_id]\n","            # Si l'étiquette est B-XXX, nous la changeons en I-XXX\n","            if label % 2 == 1:\n","                label += 1\n","            new_labels.append(label)\n","\n","    return new_labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7BSr1BhoeJiI"},"outputs":[],"source":["labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n","word_ids = inputs.word_ids()\n","print(labels)\n","print(align_labels_with_tokens(labels, word_ids))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ny0elDVCeJiI"},"outputs":[],"source":["def tokenize_and_align_labels(examples):\n","    tokenized_inputs = tokenizer(\n","        examples[\"tokens\"], truncation=True, is_split_into_words=True\n","    )\n","    all_labels = examples[\"ner_tags\"]\n","    new_labels = []\n","    for i, labels in enumerate(all_labels):\n","        word_ids = tokenized_inputs.word_ids(i)\n","        new_labels.append(align_labels_with_tokens(labels, word_ids))\n","\n","    tokenized_inputs[\"labels\"] = new_labels\n","    return tokenized_inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DegRuotBeJiJ"},"outputs":[],"source":["tokenized_datasets = raw_datasets.map(\n","    tokenize_and_align_labels,\n","    batched=True,\n","    remove_columns=raw_datasets[\"train\"].column_names,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i4m1_SpZeJiJ"},"outputs":[],"source":["from transformers import DataCollatorForTokenClassification\n","\n","data_collator = DataCollatorForTokenClassification(\n","    tokenizer=tokenizer, return_tensors=\"tf\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FhmQ-gNfeJiK"},"outputs":[],"source":["batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n","batch[\"labels\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qH9hhenQeJiL"},"outputs":[],"source":["for i in range(2):\n","    print(tokenized_datasets[\"train\"][i][\"labels\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BIZ3zwumeJiL"},"outputs":[],"source":["tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n","    columns=[\"attention_mask\", \"input_ids\", \"labels\"],\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=16,\n",")\n","\n","tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n","    columns=[\"attention_mask\", \"input_ids\", \"labels\"],\n","    collate_fn=data_collator,\n","    shuffle=False,\n","    batch_size=16,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HlLRZuczeJiL"},"outputs":[],"source":["id2label = {str(i): label for i, label in enumerate(label_names)}\n","label2id = {v: k for k, v in id2label.items()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HRg3NkpieJiM"},"outputs":[],"source":["from transformers import TFAutoModelForTokenClassification\n","\n","model = TFAutoModelForTokenClassification.from_pretrained(\n","    model_checkpoint,\n","    id2label=id2label,\n","    label2id=label2id,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DDmxHRz_eJiM"},"outputs":[],"source":["model.config.num_labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nzXgLZYveJiN"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eq8ORfWEeJiN"},"outputs":[],"source":["from transformers import create_optimizer\n","import tensorflow as tf\n","\n","# Train in mixed-precision float16\n","# Commentez cette ligne si vous utilisez un GPU qui ne bénéficiera pas de cette fonction.\n","tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n","\n","# Le nombre d'étapes d'entraînement est le nombre d'échantillons dans le jeu de données, divisé par la taille du batch puis multiplié\n","# par le nombre total d'époques. Notez que le jeu de données tf_train_dataset est ici un lot de données tf.data.Dataset,\n","# pas le jeu de données original Hugging Face, donc son len() est déjà num_samples // batch_size.\n","num_epochs = 3\n","num_train_steps = len(tf_train_dataset) * num_epochs\n","\n","optimizer, schedule = create_optimizer(\n","    init_lr=2e-5,\n","    num_warmup_steps=0,\n","    num_train_steps=num_train_steps,\n","    weight_decay_rate=0.01,\n",")\n","model.compile(optimizer=optimizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PcwfRwJQeJiN"},"outputs":[],"source":["from transformers.keras_callbacks import PushToHubCallback\n","\n","callback = PushToHubCallback(output_dir=\"camembert-finetuned-ner\", tokenizer=tokenizer)\n","\n","model.fit(\n","    tf_train_dataset,\n","    validation_data=tf_eval_dataset,\n","    callbacks=[callback],\n","    epochs=num_epochs,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pvCebi_5eJiO"},"outputs":[],"source":["!pip install seqeval"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UsD5VprAeJiP"},"outputs":[],"source":["from datasets import load_metric\n","\n","metric = load_metric(\"seqeval\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N4aw3s1neJiP"},"outputs":[],"source":["labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n","labels = [label_names[i] for i in labels]\n","labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kr6RQD5KeJiQ"},"outputs":[],"source":["predictions = labels.copy()\n","predictions[2] = \"O\"\n","metric.compute(predictions=[predictions], references=[labels])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"025gksjzeJiQ"},"outputs":[],"source":["import numpy as np\n","\n","all_predictions = []\n","all_labels = []\n","for batch in tf_eval_dataset:\n","    logits = model.predict_on_batch(batch)[\"logits\"]\n","    labels = batch[\"labels\"]\n","    predictions = np.argmax(logits, axis=-1)\n","    for prediction, label in zip(predictions, labels):\n","        for predicted_idx, label_idx in zip(prediction, label):\n","            if label_idx == -100:\n","                continue\n","            all_predictions.append(label_names[predicted_idx])\n","            all_labels.append(label_names[label_idx])\n","metric.compute(predictions=[all_predictions], references=[all_labels])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H4caxFcfeJiQ"},"outputs":[],"source":["from transformers import pipeline\n","\n","# Remplacez par votre propre checkpoint\n","model_checkpoint = \"huggingface-course/camembert-finetuned-ner\"\n","token_classifier = pipeline(\n","    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",")\n","oken_classifier(\"Je m'appelle Sylvain et je travaille à Hugging Face à Brooklyn.\")"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section3_pt.ipynb b/course/fr/chapter7/section3_pt.ipynb
deleted file mode 100644
index 990f33b2..00000000
--- a/course/fr/chapter7/section3_pt.ipynb
+++ /dev/null
@@ -1,770 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uVoMtekMk-8i"
-   },
-   "source": [
-    "# Finetuner un modèle de language masqué (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "97UMn_IHk-8n"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 *Datasets*,  🤗 *Transformers* et 🤗 *Accelerate* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JTP7moZJk-8r"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "W8b-VVo2k-8w"
-   },
-   "source": [
-    "Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "nKcOiNkCk-8z"
-   },
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kDYcdgKqk-81"
-   },
-   "source": [
-    "Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FaqLiMYJk-85"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bnwlqB03k-87"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM\n",
-    "\n",
-    "model_checkpoint = \"camembert-base\"\n",
-    "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bREfPqNMk-89"
-   },
-   "outputs": [],
-   "source": [
-    "text = \"C'est une grande <mask>.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5EjKRk9Bk-9C"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "15KV-A67k-9E"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "token_logits = model(**inputs).logits\n",
-    "# Trouver l'emplacement du <mask> et extraire ses logits\n",
-    "mask_token_index = torch.where(inputs[\"input_ids\"] == tokenizer.mask_token_id)[1]\n",
-    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
-    "# Choisir les <mask> candidats avec les logits les plus élevés\n",
-    "top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()\n",
-    "\n",
-    "for token in top_5_tokens:\n",
-    "    print(f\"'>>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "lQuMuSuwk-9J"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "imdb_dataset = load_dataset(\"allocine\")\n",
-    "imdb_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "svNO5I9lk-9M"
-   },
-   "outputs": [],
-   "source": [
-    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
-    "\n",
-    "for row in sample:\n",
-    "    print(f\"\\n'>>> Review: {row['review']}'\")\n",
-    "    print(f\"'>>> Label: {row['label']}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tm0x7w6Gk-9P"
-   },
-   "outputs": [],
-   "source": [
-    "def tokenize_function(examples):\n",
-    "    result = tokenizer(examples[\"review\"])\n",
-    "    if tokenizer.is_fast:\n",
-    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Utilisez batched=True pour activer le multithreading rapide !\n",
-    "tokenized_datasets = imdb_dataset.map(\n",
-    "    tokenize_function, batched=True, remove_columns=[\"review\", \"label\"]\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5zJvlXd2k-9R"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.model_max_length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tk3TfASAk-9T"
-   },
-   "outputs": [],
-   "source": [
-    "chunk_size = 128"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "y0ZMbjwQk-9U"
-   },
-   "outputs": [],
-   "source": [
-    "# Le découpage produit une liste de listes pour chaque caractéristique\n",
-    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
-    "\n",
-    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
-    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Mg2PlCl8k-9X"
-   },
-   "outputs": [],
-   "source": [
-    "concatenated_examples = {\n",
-    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
-    "}\n",
-    "total_length = len(concatenated_examples[\"input_ids\"])\n",
-    "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "BU_uR_dLk-9Y"
-   },
-   "outputs": [],
-   "source": [
-    "chunks = {\n",
-    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "    for k, t in concatenated_examples.items()\n",
-    "}\n",
-    "\n",
-    "for chunk in chunks[\"input_ids\"]:\n",
-    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Y9Fwylomk-9b"
-   },
-   "outputs": [],
-   "source": [
-    "def group_texts(examples):\n",
-    "    # Concaténation de tous les textes\n",
-    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
-    "    # Calculer la longueur des textes concaténés\n",
-    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
-    "    # Nous laissons tomber le dernier morceau s'il est plus petit que chunk_size\n",
-    "    total_length = (total_length // chunk_size) * chunk_size\n",
-    "    # Fractionnement par morceaux de max_len\n",
-    "    result = {\n",
-    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "        for k, t in concatenated_examples.items()\n",
-    "    }\n",
-    "    # Créer une nouvelle colonne d'étiquettes\n",
-    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "0Ez61wwAk-9e"
-   },
-   "outputs": [],
-   "source": [
-    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
-    "lm_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "TPeyRGujk-9f"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "8Qz1pwNAk-9h"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "sQIG43glk-9j"
-   },
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "for sample in samples:\n",
-    "    _ = sample.pop(\"word_ids\")\n",
-    "\n",
-    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "YHsPa9_Uk-9k"
-   },
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "import numpy as np\n",
-    "\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "wwm_probability = 0.2\n",
-    "\n",
-    "\n",
-    "def whole_word_masking_data_collator(features):\n",
-    "    for feature in features:\n",
-    "        word_ids = feature.pop(\"word_ids\")\n",
-    "\n",
-    "        # Création d'une correspondance entre les mots et les indices des tokens correspondants\n",
-    "        mapping = collections.defaultdict(list)\n",
-    "        current_word_index = -1\n",
-    "        current_word = None\n",
-    "        for idx, word_id in enumerate(word_ids):\n",
-    "            if word_id is not None:\n",
-    "                if word_id != current_word:\n",
-    "                    current_word = word_id\n",
-    "                    current_word_index += 1\n",
-    "                mapping[current_word_index].append(idx)\n",
-    "\n",
-    "        # Masquer des mots de façon aléatoire\n",
-    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
-    "        input_ids = feature[\"input_ids\"]\n",
-    "        labels = feature[\"labels\"]\n",
-    "        new_labels = [-100] * len(labels)\n",
-    "        for word_id in np.where(mask)[0]:\n",
-    "            word_id = word_id.item()\n",
-    "            for idx in mapping[word_id]:\n",
-    "                new_labels[idx] = labels[idx]\n",
-    "                input_ids[idx] = tokenizer.mask_token_id\n",
-    "        feature[\"labels\"] = new_labels\n",
-    "\n",
-    "    return default_data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "p3CVF4egk-9n"
-   },
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "batch = whole_word_masking_data_collator(samples)\n",
-    "\n",
-    "for chunk in batch[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Z2Ez4ONCk-9o"
-   },
-   "outputs": [],
-   "source": [
-    "train_size = 10_000\n",
-    "test_size = int(0.1 * train_size)\n",
-    "\n",
-    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
-    "    train_size=train_size, test_size=test_size, seed=42\n",
-    ")\n",
-    "downsampled_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "IUO-qIllk-9r"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "batch_size = 64\n",
-    "# Montrer la perte d'entraînement à chaque époque\n",
-    "logging_steps = len(downsampled_dataset[\"train\"]) // batch_size\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=f\"{model_name}-finetuned-allocine\",\n",
-    "    overwrite_output_dir=True,\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    weight_decay=0.01,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    push_to_hub=True,\n",
-    "    fp16=True,\n",
-    "    logging_steps=logging_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ipKYzt6sk-9t"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=training_args,\n",
-    "    train_dataset=downsampled_dataset[\"train\"],\n",
-    "    eval_dataset=downsampled_dataset[\"test\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ruFr2J1sk-9u"
-   },
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "\n",
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-fuBwvqek-9w"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "22r8rDYTk-9x"
-   },
-   "outputs": [],
-   "source": [
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cv0CwVamk-9z"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-GRfjc37k-91"
-   },
-   "outputs": [],
-   "source": [
-    "def insert_random_mask(batch):\n",
-    "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
-    "    masked_inputs = data_collator(features)\n",
-    "    # Créer une nouvelle colonne \"masquée\" pour chaque colonne du jeu de données\n",
-    "    return {\"masked_\" + k: v.numpy() for k, v in masked_inputs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RZbvYGpBk-91"
-   },
-   "outputs": [],
-   "source": [
-    "downsampled_dataset = downsampled_dataset.remove_columns([\"word_ids\"])\n",
-    "eval_dataset = downsampled_dataset[\"test\"].map(\n",
-    "    insert_random_mask,\n",
-    "    batched=True,\n",
-    "    remove_columns=downsampled_dataset[\"test\"].column_names,\n",
-    ")\n",
-    "eval_dataset = eval_dataset.rename_columns(\n",
-    "    {\n",
-    "        \"masked_input_ids\": \"input_ids\",\n",
-    "        \"masked_attention_mask\": \"attention_mask\",\n",
-    "        \"masked_labels\": \"labels\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "B953lLCEk-93"
-   },
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "batch_size = 64\n",
-    "train_dataloader = DataLoader(\n",
-    "    downsampled_dataset[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    batch_size=batch_size,\n",
-    "    collate_fn=data_collator,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    eval_dataset, batch_size=batch_size, collate_fn=default_data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "qk987sI3k-95"
-   },
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mgKQR4Aok-96"
-   },
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "p7iQC9nZk-97"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "3b8pk5NRk-98"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import get_full_repo_name\n",
-    "\n",
-    "model_name = \"camembert-base-finetuned-allocine-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "0dPTV7r6k-9-"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "output_dir = model_name\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mekFif4nk-9_"
-   },
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "import math\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Entraînement\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        loss = outputs.loss\n",
-    "        losses.append(accelerator.gather(loss.repeat(batch_size)))\n",
-    "\n",
-    "    losses = torch.cat(losses)\n",
-    "    losses = losses[: len(eval_dataset)]\n",
-    "    try:\n",
-    "        perplexity = math.exp(torch.mean(losses))\n",
-    "    except OverflowError:\n",
-    "        perplexity = float(\"inf\")\n",
-    "\n",
-    "    print(f\">>> Epoch {epoch}: Perplexity: {perplexity}\")\n",
-    "\n",
-    "    # Sauvegarder et télécharger\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "qpGfXsN3k--B"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "mask_filler = pipeline(\n",
-    "    \"fill-mask\", model=\"huggingface-course/camembert-base-finetuned-allocine\", tokenizer=\"huggingface-course/camembert-base-finetuned-allocine\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "q0EDeIUJk--C"
-   },
-   "outputs": [],
-   "source": [
-    "preds = mask_filler(text)\n",
-    "\n",
-    "for pred in preds:\n",
-    "    print(f\">>> {pred['sequence']}\")"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.15 (main, Oct 11 2022, 22:27:25) \n[Clang 14.0.0 (clang-1400.0.29.102)]"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "397704579725e15f5c7cb49fe5f0341eb7531c82d19f2c29d197e8b64ab5776b"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter7/section3_tf.ipynb b/course/fr/chapter7/section3_tf.ipynb
deleted file mode 100644
index f353bbef..00000000
--- a/course/fr/chapter7/section3_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"NP3kY0VRk_q3"},"source":["# Finetuner un modèle de language masqué (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"-nhghf6zk_q6"},"source":["Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aSA7nROtk_q7"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"88-awwYgk_q9"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9QhXD5YLk_q-"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"b7Xy2LYTk_q-"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"r0O-zY8Hk_q_"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3Ss0GdVjk_rA"},"outputs":[],"source":["from transformers import TFAutoModelForMaskedLM\n","\n","model_checkpoint = \"camembert-base\"\n","model = TFAutoModelForMaskedLM.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sy2CGqpZk_rB"},"outputs":[],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5cw3GKGQk_rC"},"outputs":[],"source":["text = \"C'est une grande <mask>.\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N2j2HEMTk_rD"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"]},{"cell_type":"code","source":["inputs = tokenizer(text, return_tensors=\"np\")\n","inputs"],"metadata":{"id":"hijxtvvpI6lq"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lEHNrimXk_rF"},"outputs":[],"source":["import numpy as np\n","import tensorflow as tf\n","\n","inputs = tokenizer(text, return_tensors=\"np\")\n","token_logits = model(**inputs).logits\n","# Trouver l'emplacement du [MASK] et extraire ses logits\n","mask_token_index = np.argwhere(inputs[\"input_ids\"] == tokenizer.mask_token_id)[0, 1]\n","mask_token_logits = token_logits[0, mask_token_index, :]\n","# Choisir les <mask> candidats avec les logits les plus élevés\n","# Nous annulons le tableau avant argsort pour obtenir le plus grand, pas le plus petit, logits\n","top_5_tokens = np.argsort(-mask_token_logits)[:5].tolist()\n","\n","for token in top_5_tokens:\n","    print(f\">>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-9YVj6Hwk_rH"},"outputs":[],"source":["from datasets import load_dataset\n","\n","imdb_dataset = load_dataset(\"allocine\")\n","imdb_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C5s8Gcsqk_rI"},"outputs":[],"source":["sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n","\n","for row in sample:\n","    print(f\"\\n'>>> Review: {row['review']}'\")\n","    print(f\"'>>> Label: {row['label']}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"S9AxY7vRk_rI"},"outputs":[],"source":["def tokenize_function(examples):\n","    result = tokenizer(examples[\"review\"])\n","    if tokenizer.is_fast:\n","        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n","    return result\n","\n","\n","# Utilisez batched=True pour activer le multithreading rapide !\n","tokenized_datasets = imdb_dataset.map(\n","    tokenize_function, batched=True, remove_columns=[\"review\", \"label\"]\n",")\n","tokenized_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lfmRzTV3k_rJ"},"outputs":[],"source":["tokenizer.model_max_length"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uUTay7zpk_rK"},"outputs":[],"source":["chunk_size = 128"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H99-scvsk_rL"},"outputs":[],"source":["# Le découpage produit une liste de listes pour chaque caractéristique\n","tokenized_samples = tokenized_datasets[\"train\"][:3]\n","\n","for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n","    print(f\"'>>> Review {idx} length: {len(sample)}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ydTwpQQFk_rM"},"outputs":[],"source":["concatenated_examples = {\n","    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n","}\n","total_length = len(concatenated_examples[\"input_ids\"])\n","print(f\"'>>> Concatenated reviews length: {total_length}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"r6DgEBW0k_rM"},"outputs":[],"source":["chunks = {\n","    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n","    for k, t in concatenated_examples.items()\n","}\n","\n","for chunk in chunks[\"input_ids\"]:\n","    print(f\"'>>> Chunk length: {len(chunk)}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Rgas_sQYk_rO"},"outputs":[],"source":["def group_texts(examples):\n","    # Concaténation de tous les textes\n","    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n","    # Calculer la longueur des textes concaténés\n","    total_length = len(concatenated_examples[list(examples.keys())[0]])\n","    # Nous laissons tomber le dernier morceau s'il est plus petit que chunk_size\n","    total_length = (total_length // chunk_size) * chunk_size\n","    # Fractionnement par morceaux de max_len\n","    result = {\n","        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n","        for k, t in concatenated_examples.items()\n","    }\n","    # Créer une nouvelle colonne d'étiquettes\n","    result[\"labels\"] = result[\"input_ids\"].copy()\n","    return result"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D8vM6BTVk_rP"},"outputs":[],"source":["lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n","lm_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1QWEpUUPk_rP"},"outputs":[],"source":["tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"curV42cyk_rQ"},"outputs":[],"source":["from transformers import DataCollatorForLanguageModeling\n","\n","data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lqlBaPNUk_rQ"},"outputs":[],"source":["samples = [lm_datasets[\"train\"][i] for i in range(2)]\n","for sample in samples:\n","    _ = sample.pop(\"word_ids\")\n","\n","for chunk in data_collator(samples)[\"input_ids\"]:\n","    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rADgwaoSk_rR"},"outputs":[],"source":["import collections\n","import numpy as np\n","\n","from transformers.data.data_collator import tf_default_data_collator\n","\n","wwm_probability = 0.2\n","\n","\n","def whole_word_masking_data_collator(features):\n","    for feature in features:\n","        word_ids = feature.pop(\"word_ids\")\n","\n","        # Création d'une correspondance entre les mots et les indices des tokens correspondants\n","        mapping = collections.defaultdict(list)\n","        current_word_index = -1\n","        current_word = None\n","        for idx, word_id in enumerate(word_ids):\n","            if word_id is not None:\n","                if word_id != current_word:\n","                    current_word = word_id\n","                    current_word_index += 1\n","                mapping[current_word_index].append(idx)\n","\n","        # Masquer des mots de façon aléatoire\n","        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n","        input_ids = feature[\"input_ids\"]\n","        labels = feature[\"labels\"]\n","        new_labels = [-100] * len(labels)\n","        for word_id in np.where(mask)[0]:\n","            word_id = word_id.item()\n","            for idx in mapping[word_id]:\n","                new_labels[idx] = labels[idx]\n","                input_ids[idx] = tokenizer.mask_token_id\n","        feature[\"labels\"] = new_labels\n","\n","    return tf_default_data_collator(features)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eoHKocTDk_rR"},"outputs":[],"source":["samples = [lm_datasets[\"train\"][i] for i in range(2)]\n","batch = whole_word_masking_data_collator(samples)\n","\n","for chunk in batch[\"input_ids\"]:\n","    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Arvt2n1Vk_rS"},"outputs":[],"source":["train_size = 10_000\n","test_size = int(0.1 * train_size)\n","\n","downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n","    train_size=train_size, test_size=test_size, seed=42\n",")\n","downsampled_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-xFiuzqLk_rS"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qlyHhY42k_rT"},"outputs":[],"source":["tf_train_dataset = model.prepare_tf_dataset(\n","    downsampled_dataset[\"train\"],\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=32,\n",")\n","\n","tf_eval_dataset = model.prepare_tf_dataset(\n","    downsampled_dataset[\"test\"],\n","    collate_fn=data_collator,\n","    shuffle=False,\n","    batch_size=32,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PRn2ezWLk_rT"},"outputs":[],"source":["from transformers import create_optimizer\n","from transformers.keras_callbacks import PushToHubCallback\n","import tensorflow as tf\n","\n","num_train_steps = len(tf_train_dataset)\n","optimizer, schedule = create_optimizer(\n","    init_lr=2e-5,\n","    num_warmup_steps=1_000,\n","    num_train_steps=num_train_steps,\n","    weight_decay_rate=0.01,\n",")\n","model.compile(optimizer=optimizer)\n","\n","# Entraîner en mixed-precision float16\n","tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n","\n","callback = PushToHubCallback(\n","    output_dir=f\"{model_name}-finetuned-allocine\", tokenizer=tokenizer\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XKdteETBk_rU"},"outputs":[],"source":["import math\n","\n","eval_loss = model.evaluate(tf_eval_dataset)\n","print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yJ0XW1EDk_rU"},"outputs":[],"source":["model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2nNw5yitk_rV"},"outputs":[],"source":["eval_loss = model.evaluate(tf_eval_dataset)\n","print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OppVCVdJk_rV"},"outputs":[],"source":["from transformers import pipeline\n","\n","mask_filler = pipeline(\n","    \"fill-mask\", model=\"camembert-base-finetuned-allocine\", framework=\"tf\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6JoqdMy5k_rV"},"outputs":[],"source":["preds = mask_filler(text)\n","\n","for pred in preds:\n","    print(f\">>> {pred['sequence']}\")"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section4_pt.ipynb b/course/fr/chapter7/section4_pt.ipynb
deleted file mode 100644
index da4e7326..00000000
--- a/course/fr/chapter7/section4_pt.ipynb
+++ /dev/null
@@ -1,796 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "a9FLXSQwKy07"
-   },
-   "source": [
-    "# Traduction (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KBQHiJybKy0-"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mBCiD3lkKy0_"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "jPmvTXi1Ky1A"
-   },
-   "source": [
-    "Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "WRi1-zwbKy1B"
-   },
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6_uvLx_cKy1C"
-   },
-   "source": [
-    "Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6A8j7U20Ky1D"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "f7jZ0kj2Ky1D"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "\n",
-    "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JPzEbB9CKy1E"
-   },
-   "outputs": [],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Y4XIrw4MKy1G"
-   },
-   "outputs": [],
-   "source": [
-    "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
-    "split_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "pOlt43_VKy1H"
-   },
-   "outputs": [],
-   "source": [
-    "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jvfc4756Ky1H"
-   },
-   "outputs": [],
-   "source": [
-    "split_datasets[\"train\"][1][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Oq8MNDKKKy1I"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "G5J86Q66Ky1J"
-   },
-   "outputs": [],
-   "source": [
-    "split_datasets[\"train\"][172][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "77Ra7T93Ky1J"
-   },
-   "outputs": [],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uyAiddy7Ky1K"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "J1LKf1zlKy1K"
-   },
-   "outputs": [],
-   "source": [
-    "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
-    "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
-    "\n",
-    "inputs = tokenizer(en_sentence)\n",
-    "with tokenizer.as_target_tokenizer():\n",
-    "    targets = tokenizer(fr_sentence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "56xilSMiKy1L"
-   },
-   "outputs": [],
-   "source": [
-    "wrong_targets = tokenizer(fr_sentence)\n",
-    "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
-    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "xY6wDyYmKy1M"
-   },
-   "outputs": [],
-   "source": [
-    "max_input_length = 128\n",
-    "max_target_length = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
-    "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
-    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
-    "\n",
-    "    # Configurer le tokenizer pour les cibles\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fklE6dSFKy1N"
-   },
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = split_datasets.map(\n",
-    "    preprocess_function,\n",
-    "    batched=True,\n",
-    "    remove_columns=split_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "3OPavkGXKy1N"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5hblEiLDKy1N"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6UlKyTObKy1N"
-   },
-   "outputs": [],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
-    "batch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_wrd_1laKy1O"
-   },
-   "outputs": [],
-   "source": [
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "QGTJBtLxKy1P"
-   },
-   "outputs": [],
-   "source": [
-    "batch[\"decoder_input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "g1Nu16LkKy1P"
-   },
-   "outputs": [],
-   "source": [
-    "for i in range(1, 3):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "D-V_-SfwKy1P"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install sacrebleu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "USx5whYfKy1Q"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"sacrebleu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rOwFYZxmKy1Q"
-   },
-   "outputs": [],
-   "source": [
-    "predictions = [\n",
-    "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
-    "]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uIWAWo5mKy1Q"
-   },
-   "outputs": [],
-   "source": [
-    "predictions = [\"This This This This\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bLqFm4fFKy1R"
-   },
-   "outputs": [],
-   "source": [
-    "predictions = [\"This plugin\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rx8u9pDsKy1R"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_preds):\n",
-    "    preds, labels = eval_preds\n",
-    "    # Dans le cas où le modèle retourne plus que les logits de prédiction\n",
-    "    if isinstance(preds, tuple):\n",
-    "        preds = preds[0]\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
-    "\n",
-    "    # Remplacer les -100 dans les étiquettes car nous ne pouvons pas les décoder\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Quelques post-traitements simples\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "\n",
-    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
-    "    return {\"bleu\": result[\"score\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "VJrt4wY3Ky1S"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6rvcbMenKy1S"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainingArguments\n",
-    "\n",
-    "args = Seq2SeqTrainingArguments(\n",
-    "    f\"marian-finetuned-kde4-en-to-fr\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    per_device_train_batch_size=32,\n",
-    "    per_device_eval_batch_size=64,\n",
-    "    weight_decay=0.01,\n",
-    "    save_total_limit=3,\n",
-    "    num_train_epochs=3,\n",
-    "    predict_with_generate=True,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "M7scgmEvKy1T"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainer\n",
-    "\n",
-    "trainer = Seq2SeqTrainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cgZHNFuCKy1T"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JTcbgpizKy1T"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "a5PukEUnKy1U"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FPcIyn99Ky1U"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(tags=\"translation\", commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fnkezZjWKy1V"
-   },
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "N8HHRxGfKy1V"
-   },
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RWKO6klrKy1V"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "PCtEyEauKy1W"
-   },
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "PlMSEpINKy1W"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "BpcWbqnbKy1X"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "erxPV1tXKy1X"
-   },
-   "outputs": [],
-   "source": [
-    "output_dir = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cSrda_kiKy1X"
-   },
-   "outputs": [],
-   "source": [
-    "def postprocess(predictions, labels):\n",
-    "    predictions = predictions.cpu().numpy()\n",
-    "    labels = labels.cpu().numpy()\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "\n",
-    "    # Remplacez -100 dans les étiquettes car nous ne pouvons pas les décoder\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Quelques post-traitements simples\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "    return decoded_preds, decoded_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "hs-rK7J7Ky1Y"
-   },
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Entraînement\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            generated_tokens = accelerator.unwrap_model(model).generate(\n",
-    "                batch[\"input_ids\"],\n",
-    "                attention_mask=batch[\"attention_mask\"],\n",
-    "                max_length=128,\n",
-    "            )\n",
-    "        labels = batch[\"labels\"]\n",
-    "\n",
-    "        # Nécessaire pour rembourrer les prédictions et les étiquettes à rassembler\n",
-    "        generated_tokens = accelerator.pad_across_processes(\n",
-    "            generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n",
-    "        )\n",
-    "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
-    "\n",
-    "        predictions_gathered = accelerator.gather(generated_tokens)\n",
-    "        labels_gathered = accelerator.gather(labels)\n",
-    "\n",
-    "        decoded_preds, decoded_labels = postprocess(predictions_gathered, labels_gathered)\n",
-    "        metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
-    "\n",
-    "    results = metric.compute()\n",
-    "    print(f\"epoch {epoch}, BLEU score: {results['score']:.2f}\")\n",
-    "\n",
-    "    # Sauvegarder et télécharger\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5q2sbBk6Ky1Y"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Remplacer par votre propre checkpoint\n",
-    "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "oO_8WGkfKy1Z"
-   },
-   "outputs": [],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter7/section4_tf.ipynb b/course/fr/chapter7/section4_tf.ipynb
deleted file mode 100644
index d916ae55..00000000
--- a/course/fr/chapter7/section4_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"lQwwCPeVK7lU"},"source":["# Traduction (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"jID8fKanK7lX"},"source":["Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"n_9ZNCn0K7lZ"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting datasets\n","  Downloading datasets-2.6.1-py3-none-any.whl (441 kB)\n","\u001b[K     |████████████████████████████████| 441 kB 5.2 MB/s \n","\u001b[?25hCollecting transformers[sentencepiece]\n","  Downloading transformers-4.23.1-py3-none-any.whl (5.3 MB)\n","\u001b[K     |████████████████████████████████| 5.3 MB 38.1 MB/s \n","\u001b[?25hRequirement already satisfied: dill\u003c0.3.6 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.5.1)\n","Collecting responses\u003c0.19\n","  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n","Requirement already satisfied: pyarrow\u003e=6.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (6.0.1)\n","Requirement already satisfied: numpy\u003e=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n","Requirement already satisfied: fsspec[http]\u003e=2021.11.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (2022.8.2)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from datasets) (3.8.3)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from datasets) (4.13.0)\n","Collecting xxhash\n","  Downloading xxhash-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n","\u001b[K     |████████████████████████████████| 212 kB 45.6 MB/s \n","\u001b[?25hCollecting multiprocess\n","  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/usr/local/lib/python3.7/dist-packages (from aiohttp-\u003edatasets) (2.1.1)\n","Requirement already satisfied: aiosignal\u003e=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp-\u003edatasets) (1.2.0)\n","Requirement already satisfied: async-timeout\u003c5.0,\u003e=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp-\u003edatasets) (4.0.2)\n","Requirement already satisfied: yarl\u003c2.0,\u003e=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp-\u003edatasets) (1.8.1)\n","Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp-\u003edatasets) (0.13.0)\n","Requirement already satisfied: attrs\u003e=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp-\u003edatasets) (22.1.0)\n","Requirement already satisfied: multidict\u003c7.0,\u003e=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp-\u003edatasets) (6.0.2)\n","Requirement already satisfied: typing-extensions\u003e=3.7.4 in 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(2022.9.24)\n","Requirement already satisfied: idna\u003c3,\u003e=2.5 in /usr/local/lib/python3.7/dist-packages (from requests\u003e=2.19.0-\u003edatasets) (2.10)\n","Collecting urllib3!=1.25.0,!=1.25.1,\u003c1.26,\u003e=1.21.1\n","  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n","\u001b[K     |████████████████████████████████| 127 kB 14.0 MB/s \n","\u001b[?25hRequirement already satisfied: zipp\u003e=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata-\u003edatasets) (3.9.0)\n","Collecting multiprocess\n","  Downloading multiprocess-0.70.13-py37-none-any.whl (115 kB)\n","\u001b[K     |████████████████████████████████| 115 kB 37.5 MB/s \n","\u001b[?25hRequirement already satisfied: python-dateutil\u003e=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas-\u003edatasets) (2.8.2)\n","Requirement already satisfied: pytz\u003e=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas-\u003edatasets) (2022.4)\n","Requirement already satisfied: six\u003e=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil\u003e=2.7.3-\u003epandas-\u003edatasets) (1.15.0)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers[sentencepiece]) (2022.6.2)\n","Collecting tokenizers!=0.11.3,\u003c0.14,\u003e=0.11.1\n","  Downloading tokenizers-0.13.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n","\u001b[K     |████████████████████████████████| 7.6 MB 39.5 MB/s \n","\u001b[?25hCollecting sentencepiece!=0.1.92,\u003e=0.1.91\n","  Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n","\u001b[K     |████████████████████████████████| 1.3 MB 44.7 MB/s \n","\u001b[?25hRequirement already satisfied: protobuf\u003c=3.20.2 in /usr/local/lib/python3.7/dist-packages (from transformers[sentencepiece]) (3.17.3)\n","Installing collected packages: urllib3, tokenizers, huggingface-hub, xxhash, transformers, sentencepiece, responses, multiprocess, datasets\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.6.1 huggingface-hub-0.10.1 multiprocess-0.70.13 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.1 transformers-4.23.1 urllib3-1.25.11 xxhash-3.1.0\n","Reading package lists... Done\n","Building dependency tree       \n","Reading state information... Done\n","git-lfs is already the newest version (2.3.4-1).\n","The following package was automatically installed and is no longer required:\n","  libnvidia-common-460\n","Use 'apt autoremove' to remove it.\n","0 upgraded, 0 newly installed, 0 to remove and 27 not upgraded.\n"]}],"source":["!pip install datasets transformers[sentencepiece]\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"WxFtihYXK7lc"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"woP6GDVCK7le"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"MLMwx807K7lf"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3P9UXPJsK7lf"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W0YziRB-K7lg"},"outputs":[],"source":["from datasets import load_dataset, load_metric\n","\n","raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tFCg2ntAK7li"},"outputs":[],"source":["raw_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V09kRyLyK7ll"},"outputs":[],"source":["split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n","split_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JvvrBODbK7lm"},"outputs":[],"source":["split_datasets[\"validation\"] = split_datasets.pop(\"test\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_GROMRExK7ln"},"outputs":[],"source":["split_datasets[\"train\"][1][\"translation\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mpAXVL-EK7lo"},"outputs":[],"source":["from transformers import pipeline\n","\n","model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n","translator = pipeline(\"translation\", model=model_checkpoint)\n","translator(\"Default to expanded threads\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"epLNmY_HK7lp"},"outputs":[],"source":["split_datasets[\"train\"][172][\"translation\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bY-XZVJCK7lp"},"outputs":[],"source":["translator(\n","    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tGpi3GlmK7lq"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Wtw_yJF2K7lr"},"outputs":[],"source":["en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n","fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n","\n","inputs = tokenizer(en_sentence)\n","with tokenizer.as_target_tokenizer():\n","    targets = tokenizer(fr_sentence)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2ZV7SMUWK7ls"},"outputs":[],"source":["wrong_targets = tokenizer(fr_sentence)\n","print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n","print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"j2YcgzcaK7lt"},"outputs":[],"source":["max_input_length = 128\n","max_target_length = 128\n","\n","\n","def preprocess_function(examples):\n","    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n","    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n","    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n","\n","    # Configurer le tokenizer pour les cibles\n","    with tokenizer.as_target_tokenizer():\n","        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n","\n","    model_inputs[\"labels\"] = labels[\"input_ids\"]\n","    return model_inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HRpke7NdK7lt"},"outputs":[],"source":["tokenized_datasets = split_datasets.map(\n","    preprocess_function,\n","    batched=True,\n","    remove_columns=split_datasets[\"train\"].column_names,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Me0u8VVvK7lu"},"outputs":[],"source":["from transformers import TFAutoModelForSeq2SeqLM\n","\n","model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, from_pt=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X6lZL_LWK7lu"},"outputs":[],"source":["from transformers import DataCollatorForSeq2Seq\n","\n","data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uf9FgnBFK7lv"},"outputs":[],"source":["batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n","batch.keys()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Axr6br0pK7lv"},"outputs":[],"source":["batch[\"labels\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Nw1IhmWRK7lw"},"outputs":[],"source":["batch[\"decoder_input_ids\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q0Q725XgK7lw"},"outputs":[],"source":["for i in range(1, 3):\n","    print(tokenized_datasets[\"train\"][i][\"labels\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l8uOxCo-K7ly"},"outputs":[],"source":["tf_train_dataset = model.prepare_tf_dataset(\n","    tokenized_datasets[\"train\"],\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=32,\n",")\n","\n","tf_eval_dataset = model.prepare_tf_dataset(\n","    tokenized_datasets[\"validation\"],\n","    collate_fn=data_collator,\n","    shuffle=False,\n","    batch_size=16,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X6SFfWWHK7ly"},"outputs":[],"source":["!pip install sacrebleu"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hDadfs1JK7ly"},"outputs":[],"source":["from datasets import load_metric\n","\n","metric = load_metric(\"sacrebleu\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u9Moa_SuK7lz"},"outputs":[],"source":["predictions = [\n","    \"This plugin lets you translate web pages between several languages automatically.\"\n","]\n","references = [\n","    [\n","        \"This plugin allows you to automatically translate web pages between several languages.\"\n","    ]\n","]\n","metric.compute(predictions=predictions, references=references)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ozUUl2TxK7lz"},"outputs":[],"source":["predictions = [\"This This This This\"]\n","references = [\n","    [\n","        \"This plugin allows you to automatically translate web pages between several languages.\"\n","    ]\n","]\n","metric.compute(predictions=predictions, references=references)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dszazssQK7l0"},"outputs":[],"source":["predictions = [\"This plugin\"]\n","references = [\n","    [\n","        \"This plugin allows you to automatically translate web pages between several languages.\"\n","    ]\n","]\n","metric.compute(predictions=predictions, references=references)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZmHOtSV4K7l0"},"outputs":[],"source":["import numpy as np\n","import tensorflow as tf\n","from tqdm import tqdm\n","generation_data_collator = DataCollatorForSeq2Seq(\n","    tokenizer, model=model, return_tensors=\"tf\", pad_to_multiple_of=128\n",")\n","tf_generate_dataset = model.prepare_tf_dataset(\n","    tokenized_datasets[\"validation\"],\n","    collate_fn=generation_data_collator,\n","    shuffle=False,\n","    batch_size=8,\n",")\n","@tf.function(jit_compile=True)\n","def generate_with_xla(batch):\n","    return model.generate(\n","        input_ids=batch[\"input_ids\"],\n","        attention_mask=batch[\"attention_mask\"],\n","        max_new_tokens=128,\n","    )\n","\n","def compute_metrics():\n","    all_preds = []\n","    all_labels = []\n","\n","    for batch, labels in tqdm(tf_generate_dataset):\n","        predictions = generate_with_xla(batch)\n","\n","    for batch in tf_generate_dataset:\n","        predictions = model.generate(\n","            input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n","        )\n","        labels = labels.numpy()\n","        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","        decoded_preds = [pred.strip() for pred in decoded_preds]\n","        decoded_labels = [[label.strip()] for label in decoded_labels]\n","        all_preds.extend(decoded_preds)\n","        all_labels.extend(decoded_labels)\n","    result = metric.compute(predictions=all_preds, references=all_labels)\n","    return {\"bleu\": result[\"score\"]}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X11nbLUtK7l1"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hmSjXUvBK7l2"},"outputs":[],"source":["print(compute_metrics())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-7ciXHiMK7l2"},"outputs":[],"source":["from transformers import create_optimizer\n","from transformers.keras_callbacks import PushToHubCallback\n","import tensorflow as tf\n","\n","# Le nombre d'étapes d'entraînement est le nombre d'échantillons dans le jeu de données, divisé par la taille du batch puis multiplié\n","# par le nombre total d'époques. Notez que le jeu de données tf_train_dataset est ici un lot de données tf.data.Dataset,\n","# pas le jeu de données original Hugging Face, donc son len() est déjà num_samples // batch_size.\n","num_epochs = 3\n","num_train_steps = len(tf_train_dataset) * num_epochs\n","\n","optimizer, schedule = create_optimizer(\n","    init_lr=5e-5,\n","    num_warmup_steps=0,\n","    num_train_steps=num_train_steps,\n","    weight_decay_rate=0.01,\n",")\n","model.compile(optimizer=optimizer)\n","\n","# Entraîner en mixed-precision float16\n","tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zpd9e2R1K7l3"},"outputs":[],"source":["from transformers.keras_callbacks import PushToHubCallback\n","\n","callback = PushToHubCallback(\n","    output_dir=\"marian-finetuned-kde4-en-to-fr\", tokenizer=tokenizer\n",")\n","\n","model.fit(\n","    tf_train_dataset,\n","    validation_data=tf_eval_dataset,\n","    callbacks=[callback],\n","    epochs=num_epochs,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VGGvxYbUK7l3"},"outputs":[],"source":["print(compute_metrics())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XBfYvd5qK7l4"},"outputs":[],"source":["from transformers import pipeline\n","\n","# Remplacer par votre propre checkpoint\n","model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n","translator = pipeline(\"translation\", model=model_checkpoint)\n","translator(\"Default to expanded threads\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"18mI4K4PK7l5"},"outputs":[],"source":["translator(\n","    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",")"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"name":"","version":""},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
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deleted file mode 100644
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--- a/course/fr/chapter7/section5_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"Pa8a2g5mK-N9"},"source":["# Résumé (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"fJvNiRTdK-N_"},"source":["Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hNHv1f6IK-OB"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install accelerate\n","# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :\n","# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"Xx-gfTuTK-OD"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f9B3GDbhK-OE"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"ur5BwnyKK-OF"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1WQHJadxK-OG"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V10eZAq-K-OH"},"outputs":[],"source":["from datasets import load_dataset\n","\n","french_dataset = load_dataset(\"amazon_reviews_multi\", \"fr\")\n","english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n","french_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gVHoJ0aNK-OJ"},"outputs":[],"source":["def show_samples(dataset, num_samples=3, seed=42):\n","    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n","    for example in sample:\n","        print(f\"\\n'>> Title: {example['review_title']}'\")\n","        print(f\"'>> Review: {example['review_body']}'\")\n","\n","show_samples(french_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1NPG9ebhK-OL"},"outputs":[],"source":["french_dataset.set_format(\"pandas\")\n","french_df = french_dataset[\"train\"][:]\n","# Afficher les comptes des 20 premiers produits\n","french_df[\"product_category\"].value_counts()[:20]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BkFJ3vxdK-OM"},"outputs":[],"source":["def filter_books(example):\n","    return (\n","        example[\"product_category\"] == \"book\"\n","        or example[\"product_category\"] == \"digital_ebook_purchase\"\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Wq7ako5PK-ON"},"outputs":[],"source":["french_dataset.reset_format()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XQ3xI0fCK-OO"},"outputs":[],"source":["french_books = french_dataset.filter(filter_books)\n","english_books = english_dataset.filter(filter_books)\n","show_samples(french_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4fdfxmRjK-OP"},"outputs":[],"source":["from datasets import concatenate_datasets, DatasetDict\n","\n","books_dataset = DatasetDict()\n","\n","for split in english_books.keys():\n","    books_dataset[split] = concatenate_datasets(\n","        [english_books[split], french_books[split]]\n","    )\n","    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n","\n","# Quelques exemples\n","show_samples(books_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Cd-FUPHMK-OP"},"outputs":[],"source":["books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"93NzoKWPK-OQ"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","model_checkpoint = \"google/mt5-small\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3u9KwyFNK-OR"},"outputs":[],"source":["inputs = tokenizer(\"J'ai adoré lire les Hunger Games !\")\n","inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bZLmFI1vK-OS"},"outputs":[],"source":["tokenizer.convert_ids_to_tokens(inputs.input_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HCboaKIeK-OT"},"outputs":[],"source":["max_input_length = 512\n","max_target_length = 30\n","\n","\n","def preprocess_function(examples):\n","    model_inputs = tokenizer(\n","        examples[\"review_body\"], max_length=max_input_length, truncation=True\n","    )\n","    # Configurer le tokenizer pour les cibles\n","    with tokenizer.as_target_tokenizer():\n","        labels = tokenizer(\n","            examples[\"review_title\"], max_length=max_target_length, truncation=True\n","        )\n","\n","    model_inputs[\"labels\"] = labels[\"input_ids\"]\n","    return model_inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E2YvimJQK-OT"},"outputs":[],"source":["tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DMtI8TCaK-OU"},"outputs":[],"source":["generated_summary = \"J'ai absolument adoré lire les Hunger Games\"\n","reference_summary = \"J'ai adoré lire les Hunger Games\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4V2wHKLKK-OV"},"outputs":[],"source":["!pip install rouge_score"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4kubvqkdK-OW"},"outputs":[],"source":["from datasets import load_metric\n","\n","rouge_score = load_metric(\"rouge\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EEzmHqVlK-OW"},"outputs":[],"source":["scores = rouge_score.compute(\n","    predictions=[generated_summary], references=[reference_summary]\n",")\n","scores"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n0J84AULK-OX"},"outputs":[],"source":["scores[\"rouge1\"].mid"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e4kQhIjXK-OY"},"outputs":[],"source":["!pip install nltk"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jPdNK5qoK-OY"},"outputs":[],"source":["import nltk\n","\n","nltk.download(\"punkt\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3gFUAtOhK-OY"},"outputs":[],"source":["from nltk.tokenize import sent_tokenize\n","\n","\n","def three_sentence_summary(text):\n","    return \"\\n\".join(sent_tokenize(text)[:3])\n","\n","\n","print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rtLaA9GrK-OZ"},"outputs":[],"source":["def evaluate_baseline(dataset, metric):\n","    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n","    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XdtbG9A_K-OZ"},"outputs":[],"source":["import pandas as pd\n","\n","score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n","rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n","rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n","rouge_dict"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cIQlTsaoK-Oa"},"outputs":[],"source":["from transformers import AutoModelForSeq2SeqLM\n","\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SeVaDGwoK-Ob"},"outputs":[],"source":["from transformers import Seq2SeqTrainingArguments\n","\n","batch_size = 8\n","num_train_epochs = 8\n","# Montre la perte d'entraînement à chaque époque\n","logging_steps = len(tokenized_datasets[\"train\"]) // batch_size\n","model_name = model_checkpoint.split(\"/\")[-1]\n","\n","args = Seq2SeqTrainingArguments(\n","    output_dir=f\"{model_name}-finetuned-amazon-en-fr\",\n","    evaluation_strategy=\"epoch\",\n","    learning_rate=5.6e-5,\n","    per_device_train_batch_size=batch_size,\n","    per_device_eval_batch_size=batch_size,\n","    weight_decay=0.01,\n","    save_total_limit=3,\n","    num_train_epochs=num_train_epochs,\n","    predict_with_generate=True,\n","    logging_steps=logging_steps,\n","    push_to_hub=True,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H_ddkeuUK-Oc"},"outputs":[],"source":["import numpy as np\n","\n","\n","def compute_metrics(eval_pred):\n","    predictions, labels = eval_pred\n","    # Décoder les résumés générés en texte\n","    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n","    # Remplacer -100 dans les étiquettes car nous ne pouvons pas les décoder\n","    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    # Décoder les résumés de référence en texte\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","    # ROUGE attend une nouvelle ligne après chaque phrase\n","    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n","    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n","    # Calculer les scores ROUGE\n","    result = rouge_score.compute(\n","        predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n","    )\n","    # Extraire les scores médians\n","    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n","    return {k: round(v, 4) for k, v in result.items()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Of9WBMiIK-Od"},"outputs":[],"source":["from transformers import DataCollatorForSeq2Seq\n","\n","data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qmhWCSNcK-Od"},"outputs":[],"source":["tokenized_datasets = tokenized_datasets.remove_columns(\n","    books_dataset[\"train\"].column_names\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aPxkhz01K-Od"},"outputs":[],"source":["features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n","data_collator(features)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RVemQHl4K-Oe"},"outputs":[],"source":["from transformers import Seq2SeqTrainer\n","\n","trainer = Seq2SeqTrainer(\n","    model,\n","    args,\n","    train_dataset=tokenized_datasets[\"train\"],\n","    eval_dataset=tokenized_datasets[\"validation\"],\n","    data_collator=data_collator,\n","    tokenizer=tokenizer,\n","    compute_metrics=compute_metrics,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LeovMinHK-Oe"},"outputs":[],"source":["trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qif2SSS1K-Oe"},"outputs":[],"source":["trainer.evaluate()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IvF9-7WiK-Of"},"outputs":[],"source":["tokenized_datasets.set_format(\"torch\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lDXXUDUSK-Of"},"outputs":[],"source":["model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vr2uehAzK-Og"},"outputs":[],"source":["from torch.utils.data import DataLoader\n","\n","batch_size = 8\n","train_dataloader = DataLoader(\n","    tokenized_datasets[\"train\"],\n","    shuffle=True,\n","    collate_fn=data_collator,\n","    batch_size=batch_size,\n",")\n","eval_dataloader = DataLoader(\n","    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=batch_size\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Co3Kh6KkK-Og"},"outputs":[],"source":["from torch.optim import AdamW\n","\n","optimizer = AdamW(model.parameters(), lr=2e-5)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wB8Vb3O4K-Og"},"outputs":[],"source":["from accelerate import Accelerator\n","\n","accelerator = Accelerator()\n","model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n","    model, optimizer, train_dataloader, eval_dataloader\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rc-HG8orK-Oh"},"outputs":[],"source":["from transformers import get_scheduler\n","\n","num_train_epochs = 10\n","num_update_steps_per_epoch = len(train_dataloader)\n","num_training_steps = num_train_epochs * num_update_steps_per_epoch\n","\n","lr_scheduler = get_scheduler(\n","    \"linear\",\n","    optimizer=optimizer,\n","    num_warmup_steps=0,\n","    num_training_steps=num_training_steps,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lcZUY4u1K-Oh"},"outputs":[],"source":["def postprocess_text(preds, labels):\n","    preds = [pred.strip() for pred in preds]\n","    labels = [label.strip() for label in labels]\n","\n","    # ROUGE attend une nouvelle ligne après chaque phrase\n","    preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n","    labels = [\"\\n\".join(nltk.sent_tokenize(label)) for label in labels]\n","\n","    return preds, labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MBsEsY2CK-Oi"},"outputs":[],"source":["from huggingface_hub import get_full_repo_name\n","\n","model_name = \"test-bert-finetuned-squad-accelerate\"\n","repo_name = get_full_repo_name(model_name)\n","repo_name"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lBr2mNruK-Oi"},"outputs":[],"source":["from huggingface_hub import Repository\n","\n","output_dir = \"results-mt5-finetuned-squad-accelerate\"\n","repo = Repository(output_dir, clone_from=repo_name)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZbkZgFlPK-Oj"},"outputs":[],"source":["from tqdm.auto import tqdm\n","import torch\n","import numpy as np\n","\n","progress_bar = tqdm(range(num_training_steps))\n","\n","for epoch in range(num_train_epochs):\n","    # Entraînement\n","    model.train()\n","    for step, batch in enumerate(train_dataloader):\n","        outputs = model(**batch)\n","        loss = outputs.loss\n","        accelerator.backward(loss)\n","\n","        optimizer.step()\n","        lr_scheduler.step()\n","        optimizer.zero_grad()\n","        progress_bar.update(1)\n","\n","    # Evaluation\n","    model.eval()\n","    for step, batch in enumerate(eval_dataloader):\n","        with torch.no_grad():\n","            generated_tokens = accelerator.unwrap_model(model).generate(\n","                batch[\"input_ids\"],\n","                attention_mask=batch[\"attention_mask\"],\n","            )\n","\n","            generated_tokens = accelerator.pad_across_processes(\n","                generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n","            )\n","            labels = batch[\"labels\"]\n","\n","            # Si nous n'avons pas rempli la longueur maximale, nous devons également remplir les étiquettes\n","            labels = accelerator.pad_across_processes(\n","                batch[\"labels\"], dim=1, pad_index=tokenizer.pad_token_id\n","            )\n","\n","            generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()\n","            labels = accelerator.gather(labels).cpu().numpy()\n","\n","            # Remplacer -100 dans les étiquettes car nous ne pouvons pas les décoder\n","            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","            if isinstance(generated_tokens, tuple):\n","                generated_tokens = generated_tokens[0]\n","            decoded_preds = tokenizer.batch_decode(\n","                generated_tokens, skip_special_tokens=True\n","            )\n","            decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","\n","            decoded_preds, decoded_labels = postprocess_text(\n","                decoded_preds, decoded_labels\n","            )\n","\n","            rouge_score.add_batch(predictions=decoded_preds, references=decoded_labels)\n","\n","    # Calculer les métriques\n","    result = rouge_score.compute()\n","    # Extraire les scores médians de ROUGE\n","    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n","    result = {k: round(v, 4) for k, v in result.items()}\n","    print(f\"Epoch {epoch}:\", result)\n","\n","    # Sauvegarder et télécharger\n","    accelerator.wait_for_everyone()\n","    unwrapped_model = accelerator.unwrap_model(model)\n","    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n","    if accelerator.is_main_process:\n","        tokenizer.save_pretrained(output_dir)\n","        repo.push_to_hub(\n","            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n","        )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sh71Ed8SK-Ok"},"outputs":[],"source":["from transformers import pipeline\n","\n","hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-fr\"\n","summarizer = pipeline(\"summarization\", model=hub_model_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oobImt2RK-Ok"},"outputs":[],"source":["def print_summary(idx):\n","    review = books_dataset[\"test\"][idx][\"review_body\"]\n","    title = books_dataset[\"test\"][idx][\"review_title\"]\n","    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n","    print(f\"'>>> Review: {review}'\")\n","    print(f\"\\n'>>> Title: {title}'\")\n","    print(f\"\\n'>>> Summary: {summary}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dDCLY4B-K-Ol"},"outputs":[],"source":["print_summary(100)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wFLsEqzhK-Ol"},"outputs":[],"source":["print_summary(0)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section5_tf.ipynb b/course/fr/chapter7/section5_tf.ipynb
deleted file mode 100644
index 3571d6b7..00000000
--- a/course/fr/chapter7/section5_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"jG98FMziLjbD"},"source":["# Résumé (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"j1EzLdvULjbG"},"source":["Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UlpIXgERLjbH"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"Dzbd1whOLjbI"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AZmbgaXWLjbJ"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"PX7L5OC-LjbK"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VnuC-6JkLjbK"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ryk9qy94LjbL"},"outputs":[],"source":["from datasets import load_dataset\n","\n","french_dataset = load_dataset(\"amazon_reviews_multi\", \"fr\")\n","english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n","french_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_vwOJLZSLjbM"},"outputs":[],"source":["def show_samples(dataset, num_samples=3, seed=42):\n","    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n","    for example in sample:\n","        print(f\"\\n'>> Title: {example['review_title']}'\")\n","        print(f\"'>> Review: {example['review_body']}'\")\n","\n","show_samples(french_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qcCBbvZqLjbN"},"outputs":[],"source":["french_dataset.set_format(\"pandas\")\n","french_df = french_dataset[\"train\"][:]\n","# Afficher les comptes des 20 premiers produits\n","french_df[\"product_category\"].value_counts()[:20]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OisaVzqOLjbO"},"outputs":[],"source":["def filter_books(example):\n","    return (\n","        example[\"product_category\"] == \"book\"\n","        or example[\"product_category\"] == \"digital_ebook_purchase\"\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pY5ibaMKLjbO"},"outputs":[],"source":["french_dataset.reset_format()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bPzB3l8VLjbP"},"outputs":[],"source":["french_books = french_dataset.filter(filter_books)\n","english_books = english_dataset.filter(filter_books)\n","show_samples(french_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mza0AMWTLjbP"},"outputs":[],"source":["from datasets import concatenate_datasets, DatasetDict\n","\n","books_dataset = DatasetDict()\n","\n","for split in english_books.keys():\n","    books_dataset[split] = concatenate_datasets(\n","        [english_books[split], french_books[split]]\n","    )\n","    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n","\n","# Quelques exemples\n","show_samples(books_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pfHOmORaLjbQ"},"outputs":[],"source":["books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8lMPcVQBLjbQ"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","model_checkpoint = \"google/mt5-small\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TwMHUWuPLjbR"},"outputs":[],"source":["inputs = tokenizer(\"J'ai adoré lire les Hunger Games !\")\n","inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lx-68MEwLjbS"},"outputs":[],"source":["tokenizer.convert_ids_to_tokens(inputs.input_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9oWMQ2VnLjbS"},"outputs":[],"source":["max_input_length = 512\n","max_target_length = 30\n","\n","\n","def preprocess_function(examples):\n","    model_inputs = tokenizer(\n","        examples[\"review_body\"], max_length=max_input_length, truncation=True\n","    )\n","    # Configurer le tokenizer pour les cibles\n","    with tokenizer.as_target_tokenizer():\n","        labels = tokenizer(\n","            examples[\"review_title\"], max_length=max_target_length, truncation=True\n","        )\n","\n","    model_inputs[\"labels\"] = labels[\"input_ids\"]\n","    return model_inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6yMK9IapLjbS"},"outputs":[],"source":["tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zOxZ-NAsLjbS"},"outputs":[],"source":["generated_summary = \"J'ai absolument adoré lire les Hunger Games\"\n","reference_summary = \"J'ai adoré lire les Hunger Games\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2TrZeyGPLjbT"},"outputs":[],"source":["!pip install rouge_score"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"niN-Z2vHLjbT"},"outputs":[],"source":["from datasets import load_metric\n","\n","rouge_score = load_metric(\"rouge\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5-djxNr1LjbT"},"outputs":[],"source":["scores = rouge_score.compute(\n","    predictions=[generated_summary], references=[reference_summary]\n",")\n","scores"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qe1sgeytLjbU"},"outputs":[],"source":["scores[\"rouge1\"].mid"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"grik0pxnLjbU"},"outputs":[],"source":["!pip install nltk"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zLIWB_d1LjbU"},"outputs":[],"source":["import nltk\n","\n","nltk.download(\"punkt\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V_lfamytLjbU"},"outputs":[],"source":["from nltk.tokenize import sent_tokenize\n","\n","\n","def three_sentence_summary(text):\n","    return \"\\n\".join(sent_tokenize(text)[:3])\n","\n","\n","print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TS8JpXCxLjbV"},"outputs":[],"source":["def evaluate_baseline(dataset, metric):\n","    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n","    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2R1lbWdwLjbV"},"outputs":[],"source":["import pandas as pd\n","\n","score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n","rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n","rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n","rouge_dict"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xcTuYa94LjbW"},"outputs":[],"source":["from transformers import TFAutoModelForSeq2SeqLM\n","\n","model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PJiZ8tfrLjbX"},"outputs":[],"source":["from transformers import DataCollatorForSeq2Seq\n","\n","data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1pVtzzIqLjbX"},"outputs":[],"source":["tokenized_datasets = tokenized_datasets.remove_columns(\n","    books_dataset[\"train\"].column_names\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6jRSAttgLjbX"},"outputs":[],"source":["features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n","data_collator(features)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MLBExnEsLjbX"},"outputs":[],"source":["tf_train_dataset = model.prepare_tf_dataset(\n","    tokenized_datasets[\"train\"],\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=8,\n",")\n","tf_eval_dataset = model.prepare_tf_dataset(\n","    tokenized_datasets[\"validation\"],\n","    collate_fn=data_collator,\n","    shuffle=False,\n","    batch_size=8)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JHiBjD6HLjbY"},"outputs":[],"source":["from transformers import create_optimizer\n","import tensorflow as tf\n","\n","# Le nombre d'étapes d'entraînement est le nombre d'échantillons dans le jeu de données, divisé par la taille du batch puis multiplié\n","# par le nombre total d'époques. Notez que le jeu de données tf_train_dataset est ici un lot de données tf.data.Dataset,\n","# pas le jeu de données original Hugging Face, donc son len() est déjà num_samples // batch_size.\n","num_train_epochs = 8\n","num_train_steps = len(tf_train_dataset) * num_train_epochs\n","model_name = model_checkpoint.split(\"/\")[-1]\n","\n","optimizer, schedule = create_optimizer(\n","    init_lr=5.6e-5,\n","    num_warmup_steps=0,\n","    num_train_steps=num_train_steps,\n","    weight_decay_rate=0.01,\n",")\n","\n","model.compile(optimizer=optimizer)\n","\n","# Entraîner en mixed-precision float16\n","tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"]},{"cell_type":"code","source":["tf.config.run_functions_eagerly(True)"],"metadata":{"id":"S0NiETooYX4L"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"6HgN-cfzU2Sv"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zT9W3oboLjbY"},"outputs":[],"source":["from transformers.keras_callbacks import PushToHubCallback\n","\n","callback = PushToHubCallback(\n","    output_dir=f\"{model_name}-finetuned-amazon-en-fr\", tokenizer=tokenizer\n",")\n","\n","model.fit(\n","    tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback], epochs=2\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cYdpX7MgLjbY"},"outputs":[],"source":["from tqdm import tqdm\n","import numpy as np\n","\n","generation_data_collator = DataCollatorForSeq2Seq(\n","    tokenizer, model=model, return_tensors=\"tf\", pad_to_multiple_of=320\n",")\n","tf_generate_dataset = model.prepare_tf_dataset(\n","    tokenized_datasets[\"validation\"],\n","    collate_fn=generation_data_collator,\n","    shuffle=False,\n","    batch_size=8,\n","    drop_remainder=True,\n",")\n","@tf.function(jit_compile=True)\n","def generate_with_xla(batch):\n","    return model.generate(\n","        input_ids=batch[\"input_ids\"],\n","        attention_mask=batch[\"attention_mask\"],\n","        max_new_tokens=32,\n","    )\n","\n","all_preds = []\n","all_labels = []\n","for batch, labels in tqdm(tf_generate_dataset):\n","    predictions = generate_with_xla(batch)\n","    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n","    labels = labels.numpy()\n","    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n","    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n","    all_preds.extend(decoded_preds)\n","    all_labels.extend(decoded_labels)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FWPR-zYsLjbZ"},"outputs":[],"source":["result = rouge_score.compute(\n","    predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n",")\n","result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n","{k: round(v, 4) for k, v in result.items()}"]},{"cell_type":"code","source":["result"],"metadata":{"id":"SehcLly1dVLs"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GGyyfB8JLjbZ"},"outputs":[],"source":["from transformers import pipeline\n","\n","hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-fr\"\n","summarizer = pipeline(\"summarization\", model=hub_model_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PheTl6NjLjbZ"},"outputs":[],"source":["def print_summary(idx):\n","    review = books_dataset[\"test\"][idx][\"review_body\"]\n","    title = books_dataset[\"test\"][idx][\"review_title\"]\n","    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n","    print(f\"'>>> Review: {review}'\")\n","    print(f\"\\n'>>> Title: {title}'\")\n","    print(f\"\\n'>>> Summary: {summary}'\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zenn_E0vLjba"},"outputs":[],"source":["print_summary(100)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9HPvuyi8Ljba"},"outputs":[],"source":["print_summary(0)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section6_pt.ipynb b/course/fr/chapter7/section6_pt.ipynb
deleted file mode 100644
index 68a445ca..00000000
--- a/course/fr/chapter7/section6_pt.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"m07S1dPlQKQm"},"source":["# Entraîner un modèle de langage causal de zéro (PyTorch)\n","\n","Ici nous entraînons un modèle à générer du code Python. Le Python utilisant des fonctions basées sur des mots anglais, nous gardons un gpt-2 anglais dans l'optique d'obtenir de meilleures performances que ce que l'on pourrait s'attendre en utilisant un gpt-2 en français."]},{"cell_type":"markdown","metadata":{"id":"bKP4SXl3QKQp"},"source":["Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cyuZa998QKQr"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install accelerate\n","# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :\n","# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"YzSFfJGpQKQs"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vB3m5TPYQKQs"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"_ob6qox0QKQu"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AXJnDzGDQKQu"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aP_0EpW6QKQv"},"outputs":[],"source":["def any_keyword_in_string(string, keywords):\n","    for keyword in keywords:\n","        if keyword in string:\n","            return True\n","    return False"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rLq7A-r6QKQw"},"outputs":[],"source":["filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n","example_1 = \"import numpy as np\"\n","example_2 = \"import pandas as pd\"\n","\n","print(\n","    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3k_UirmcQKQx"},"outputs":[],"source":["from collections import defaultdict\n","from tqdm import tqdm\n","from datasets import Dataset\n","\n","\n","def filter_streaming_dataset(dataset, filters):\n","    filtered_dict = defaultdict(list)\n","    total = 0\n","    for sample in tqdm(iter(dataset)):\n","        total += 1\n","        if any_keyword_in_string(sample[\"content\"], filters):\n","            for k, v in sample.items():\n","                filtered_dict[k].append(v)\n","    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n","    return Dataset.from_dict(filtered_dict)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M-hrpOx5QKQy"},"outputs":[],"source":["# Cette cellule prendra beaucoup de temps à s'exécuter, donc vous devriez la sauter et aller à la suivante !\n","from datasets import load_dataset\n","\n","split = \"train\"  # \"valid\"\n","filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n","\n","data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n","filtered_data = filter_streaming_dataset(data, filters)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8PNCJUaeQKQ0"},"outputs":[],"source":["from datasets import load_dataset, DatasetDict\n","\n","ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n","ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"validation\")\n","\n","raw_datasets = DatasetDict(\n","    {\n","        \"train\": ds_train,  # .shuffle().select(range(50000)),\n","        \"valid\": ds_valid,  # .shuffle().select(range(500))\n","    }\n",")\n","\n","raw_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_6BR-PaaQKQ0"},"outputs":[],"source":["for key in raw_datasets[\"train\"][0]:\n","    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YAlu1s52QKQ1"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","context_length = 128\n","tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n","\n","outputs = tokenizer(\n","    raw_datasets[\"train\"][:2][\"content\"],\n","    truncation=True,\n","    max_length=context_length,\n","    return_overflowing_tokens=True,\n","    return_length=True,\n",")\n","\n","print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n","print(f\"Input chunk lengths: {(outputs['length'])}\")\n","print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ICXWAbuvQKQ1"},"outputs":[],"source":["def tokenize(element):\n","    outputs = tokenizer(\n","        element[\"content\"],\n","        truncation=True,\n","        max_length=context_length,\n","        return_overflowing_tokens=True,\n","        return_length=True,\n","    )\n","    input_batch = []\n","    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n","        if length == context_length:\n","            input_batch.append(input_ids)\n","    return {\"input_ids\": input_batch}\n","\n","\n","tokenized_datasets = raw_datasets.map(\n","    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",")\n","tokenized_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9SD9k9THQKQ2"},"outputs":[],"source":["from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig\n","\n","config = AutoConfig.from_pretrained(\n","    \"gpt2\",\n","    vocab_size=len(tokenizer),\n","    n_ctx=context_length,\n","    bos_token_id=tokenizer.bos_token_id,\n","    eos_token_id=tokenizer.eos_token_id,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4IkTnA84QKQ2"},"outputs":[],"source":["model = GPT2LMHeadModel(config)\n","model_size = sum(t.numel() for t in model.parameters())\n","print(f\"GPT-2 size: {model_size/1000**2:.1f}M parameters\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"odz9--BaQKQ3"},"outputs":[],"source":["from transformers import DataCollatorForLanguageModeling\n","\n","tokenizer.pad_token = tokenizer.eos_token\n","data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O4adgzHBQKQ3"},"outputs":[],"source":["out = data_collator([tokenized_datasets[\"train\"][i] for i in range(5)])\n","for key in out:\n","    print(f\"{key} shape: {out[key].shape}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sRbg3MfWQKQ3"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_ZpXbL93QKQ3"},"outputs":[],"source":["from transformers import Trainer, TrainingArguments\n","\n","args = TrainingArguments(\n","    output_dir=\"codeparrot-ds\",\n","    per_device_train_batch_size=32,\n","    per_device_eval_batch_size=32,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=5_000,\n","    logging_steps=5_000,\n","    gradient_accumulation_steps=8,\n","    num_train_epochs=1,\n","    weight_decay=0.1,\n","    warmup_steps=1_000,\n","    lr_scheduler_type=\"cosine\",\n","    learning_rate=5e-4,\n","    save_steps=5_000,\n","    fp16=True,\n","    push_to_hub=True,\n",")\n","\n","trainer = Trainer(\n","    model=model,\n","    tokenizer=tokenizer,\n","    args=args,\n","    data_collator=data_collator,\n","    train_dataset=tokenized_datasets[\"train\"],\n","    eval_dataset=tokenized_datasets[\"valid\"],\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W3b6qC8MQKQ4"},"outputs":[],"source":["trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DHYIq235QKQ4"},"outputs":[],"source":["trainer.push_to_hub()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"axEeL9fYQKQ4"},"outputs":[],"source":["import torch\n","from transformers import pipeline\n","\n","device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n","pipe = pipeline(\n","    \"text-generation\", model=\"huggingface-course/codeparrot-ds\", device=device\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ESFYcOGnQKQ4"},"outputs":[],"source":["txt = \"\"\"\\\n","# create some data\n","x = np.random.randn(100)\n","y = np.random.randn(100)\n","\n","# create scatter plot with x, y\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YZpPFFgcQKQ5"},"outputs":[],"source":["txt = \"\"\"\\\n","# create some data\n","x = np.random.randn(100)\n","y = np.random.randn(100)\n","\n","# create dataframe from x and y\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NLkOa_frQKQ5"},"outputs":[],"source":["txt = \"\"\"\\\n","# dataframe with profession, income and name\n","df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n","\n","# calculate the mean income per profession\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XbwbNcb1QKQ5"},"outputs":[],"source":["txt = \"\"\"\n","# import random forest regressor from scikit-learn\n","from sklearn.ensemble import RandomForestRegressor\n","\n","# fit random forest model with 300 estimators on X, y:\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EK13O4AXQKQ7"},"outputs":[],"source":["keytoken_ids = []\n","for keyword in [\n","    \"plt\",\n","    \"pd\",\n","    \"sk\",\n","    \"fit\",\n","    \"predict\",\n","    \" plt\",\n","    \" pd\",\n","    \" sk\",\n","    \" fit\",\n","    \" predict\",\n","    \"testtest\",\n","]:\n","    ids = tokenizer([keyword]).input_ids[0]\n","    if len(ids) == 1:\n","        keytoken_ids.append(ids[0])\n","    else:\n","        print(f\"Keyword has not single token: {keyword}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TR_ux6OYQKQ7"},"outputs":[],"source":["from torch.nn import CrossEntropyLoss\n","import torch\n","\n","def keytoken_weighted_loss(inputs, logits, keytoken_ids, alpha=1.0):\n","    # Décalage pour que tokens < n prédise n\n","    shift_labels = inputs[..., 1:].contiguous()\n","    shift_logits = logits[..., :-1, :].contiguous()\n","    # Calculer la perte par token\n","    loss_fct = CrossEntropyLoss(reduce=False)\n","    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n","    # Redimensionnement et perte moyenne par échantillon\n","    loss_per_sample = loss.view(shift_logits.size(0), shift_logits.size(1)).mean(axis=1)\n","    # Calculer et échelonner la pondération\n","    weights = torch.stack([(inputs == kt).float() for kt in keytoken_ids]).sum(\n","        axis=[0, 2]\n","    )\n","    weights = alpha * (1.0 + weights)\n","    # Calculer la moyenne pondérée\n","    weighted_loss = (loss_per_sample * weights).mean()\n","    return weighted_loss"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gZB8hHMzQKQ8"},"outputs":[],"source":["from torch.utils.data.dataloader import DataLoader\n","\n","tokenized_dataset.set_format(\"torch\")\n","train_dataloader = DataLoader(tokenized_dataset[\"train\"], batch_size=32, shuffle=True)\n","eval_dataloader = DataLoader(tokenized_dataset[\"valid\"], batch_size=32)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P9W9cw2MQKQ8"},"outputs":[],"source":["weight_decay = 0.1\n","\n","def get_grouped_params(model, no_decay=[\"bias\", \"LayerNorm.weight\"]):\n","    params_with_wd, params_without_wd = [], []\n","    for n, p in model.named_parameters():\n","        if any(nd in n for nd in no_decay):\n","            params_without_wd.append(p)\n","        else:\n","            params_with_wd.append(p)\n","    return [\n","        {\"params\": params_with_wd, \"weight_decay\": weight_decay},\n","        {\"params\": params_without_wd, \"weight_decay\": 0.0},\n","    ]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-yAZBmJuQKQ9"},"outputs":[],"source":["def evaluate():\n","    model.eval()\n","    losses = []\n","    for step, batch in enumerate(eval_dataloader):\n","        with torch.no_grad():\n","            outputs = model(batch[\"input_ids\"], labels=batch[\"input_ids\"])\n","\n","        losses.append(accelerator.gather(outputs.loss))\n","    loss = torch.mean(torch.cat(losses))\n","    try:\n","        perplexity = torch.exp(loss)\n","    except OverflowError:\n","        perplexity = float(\"inf\")\n","    return loss.item(), perplexity.item()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QIELwMF_QKQ9"},"outputs":[],"source":["model = GPT2LMHeadModel(config)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZTXSL-IEQKQ9"},"outputs":[],"source":["from torch.optim import AdamW\n","\n","optimizer = AdamW(get_grouped_params(model), lr=5e-4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P_QwCQc9QKQ-"},"outputs":[],"source":["from accelerate import Accelerator\n","\n","accelerator = Accelerator(fp16=True)\n","\n","model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n","    model, optimizer, train_dataloader, eval_dataloader\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8FFLlzWCQKQ-"},"outputs":[],"source":["from transformers import get_scheduler\n","\n","num_train_epochs = 1\n","num_update_steps_per_epoch = len(train_dataloader)\n","num_training_steps = num_train_epochs * num_update_steps_per_epoch\n","\n","lr_scheduler = get_scheduler(\n","    name=\"linear\",\n","    optimizer=optimizer,\n","    num_warmup_steps=1_000,\n","    num_training_steps=num_training_steps,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u5kT4VMdQKQ-"},"outputs":[],"source":["from huggingface_hub import Repository, get_full_repo_name\n","\n","model_name = \"codeparrot-ds-accelerate\"\n","repo_name = get_full_repo_name(model_name)\n","repo_name"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T39_vVQjQKQ_"},"outputs":[],"source":["output_dir = \"codeparrot-ds-accelerate\"\n","repo = Repository(output_dir, clone_from=repo_name)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GQyn5cb8QKQ_"},"outputs":[],"source":["evaluate()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fQywXOFjQKRA"},"outputs":[],"source":["from tqdm.notebook import tqdm\n","\n","gradient_accumulation_steps = 8\n","eval_steps = 5_000\n","\n","model.train()\n","completed_steps = 0\n","for epoch in range(num_train_epochs):\n","    for step, batch in tqdm(\n","        enumerate(train_dataloader, start=1), total=num_training_steps\n","    ):\n","        logits = model(batch[\"input_ids\"]).logits\n","        loss = keytoken_weighted_loss(batch[\"input_ids\"], logits, keytoken_ids)\n","        if step % 100 == 0:\n","            accelerator.print(\n","                {\n","                    \"lr\": get_lr(),\n","                    \"samples\": step * samples_per_step,\n","                    \"steps\": completed_steps,\n","                    \"loss/train\": loss.item() * gradient_accumulation_steps,\n","                }\n","            )\n","        loss = loss / gradient_accumulation_steps\n","        accelerator.backward(loss)\n","        if step % gradient_accumulation_steps == 0:\n","            accelerator.clip_grad_norm_(model.parameters(), 1.0)\n","            optimizer.step()\n","            lr_scheduler.step()\n","            optimizer.zero_grad()\n","            completed_steps += 1\n","        if (step % (eval_steps * gradient_accumulation_steps)) == 0:\n","            eval_loss, perplexity = evaluate()\n","            accelerator.print({\"loss/eval\": eval_loss, \"perplexity\": perplexity})\n","            model.train()\n","            accelerator.wait_for_everyone()\n","            unwrapped_model = accelerator.unwrap_model(model)\n","            unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n","            if accelerator.is_main_process:\n","                tokenizer.save_pretrained(output_dir)\n","                repo.push_to_hub(\n","                    commit_message=f\"Training in progress step {step}\", blocking=False\n","                )"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section6_tf.ipynb b/course/fr/chapter7/section6_tf.ipynb
deleted file mode 100644
index f165868b..00000000
--- a/course/fr/chapter7/section6_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"3fWUyFq31i8c"},"source":["# Entraîner un modèle de langage causal de zéro (TensorFlow)\n","\n","Ici nous entraînons un modèle à générer du code Python. Le Python utilisant des fonctions basées sur des mots anglais, nous gardons un gpt-2 anglais dans l'optique d'obtenir de meilleures performances que ce que l'on pourrait s'attendre en utilisant un gpt-2 en français."]},{"cell_type":"markdown","metadata":{"id":"o00Kesm41i8i"},"source":["Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"woZMLy_Y1i8m"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"pM3PZiS01i8q"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xy4iA02k1i8t"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"NtabcJoO1i8w"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"onVQiVBk1i8y"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nHHi8kOU1i80"},"outputs":[],"source":["def any_keyword_in_string(string, keywords):\n","    for keyword in keywords:\n","        if keyword in string:\n","            return True\n","    return False"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CmXQkhZJ1i83"},"outputs":[],"source":["filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n","example_1 = \"import numpy as np\"\n","example_2 = \"import pandas as pd\"\n","\n","print(\n","    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_r8DSR4Z1i87"},"outputs":[],"source":["from collections import defaultdict\n","from tqdm import tqdm\n","from datasets import Dataset\n","\n","\n","def filter_streaming_dataset(dataset, filters):\n","    filtered_dict = defaultdict(list)\n","    total = 0\n","    for sample in tqdm(iter(dataset)):\n","        total += 1\n","        if any_keyword_in_string(sample[\"content\"], filters):\n","            for k, v in sample.items():\n","                filtered_dict[k].append(v)\n","    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n","    return Dataset.from_dict(filtered_dict)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2sslYgwJ1i8-"},"outputs":[],"source":["# Cette cellule prendra beaucoup de temps à s'exécuter, donc vous devriez la sauter et aller à la suivante !\n","from datasets import load_dataset\n","\n","split = \"train\"  # \"valid\"\n","filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n","\n","data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n","filtered_data = filter_streaming_dataset(data, filters)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rqbotDaQ1i9C"},"outputs":[],"source":["from datasets import load_dataset, DatasetDict\n","\n","ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n","ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"validation\")\n","\n","raw_datasets = DatasetDict(\n","    {\n","        \"train\": ds_train,  # .shuffle().select(range(50000)),\n","        \"valid\": ds_valid,  # .shuffle().select(range(500))\n","    }\n",")\n","\n","raw_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X2rHdsuY1i9F"},"outputs":[],"source":["for key in raw_datasets[\"train\"][0]:\n","    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5qihsnRG1i9H"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","context_length = 128\n","tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n","\n","outputs = tokenizer(\n","    raw_datasets[\"train\"][:2][\"content\"],\n","    truncation=True,\n","    max_length=context_length,\n","    return_overflowing_tokens=True,\n","    return_length=True,\n",")\n","\n","print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n","print(f\"Input chunk lengths: {(outputs['length'])}\")\n","print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BCozdbMp1i9J"},"outputs":[],"source":["def tokenize(element):\n","    outputs = tokenizer(\n","        element[\"content\"],\n","        truncation=True,\n","        max_length=context_length,\n","        return_overflowing_tokens=True,\n","        return_length=True,\n","    )\n","    input_batch = []\n","    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n","        if length == context_length:\n","            input_batch.append(input_ids)\n","    return {\"input_ids\": input_batch}\n","\n","\n","tokenized_datasets = raw_datasets.map(\n","    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",")\n","tokenized_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"89SBmIRk1i9M"},"outputs":[],"source":["from transformers import AutoTokenizer, TFGPT2LMHeadModel, AutoConfig\n","\n","config = AutoConfig.from_pretrained(\n","    \"gpt2\",\n","    vocab_size=len(tokenizer),\n","    n_ctx=context_length,\n","    bos_token_id=tokenizer.bos_token_id,\n","    eos_token_id=tokenizer.eos_token_id,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0kkNZGnv1i9O"},"outputs":[],"source":["model = TFGPT2LMHeadModel(config)\n","model(model.dummy_inputs)  # Construit le modèle\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZFNvO17A1i9R"},"outputs":[],"source":["from transformers import DataCollatorForLanguageModeling\n","\n","tokenizer.pad_token = tokenizer.eos_token\n","data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors=\"tf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"waKdL0nD1i9S"},"outputs":[],"source":["out = data_collator([tokenized_datasets[\"train\"][i] for i in range(5)])\n","for key in out:\n","    print(f\"{key} shape: {out[key].shape}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u-YgICD21i9T"},"outputs":[],"source":["tf_train_dataset = model.prepare_tf_dataset(\n","    train_dataset,\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=16,\n",")\n","\n","tf_eval_dataset = model.prepare_tf_dataset(\n","    tokenized_dataset[\"valid\"],\n","    collate_fn=data_collator,\n","    shuffle=False,\n","    batch_size=32,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FYdbDiQB1i9V"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kftp9PHW1i9W"},"outputs":[],"source":["from transformers import create_optimizer\n","import tensorflow as tf\n","\n","num_train_steps = len(tf_train_dataset)\n","optimizer, schedule = create_optimizer(\n","    init_lr=5e-5,\n","    num_warmup_steps=1_000,\n","    num_train_steps=num_train_steps,\n","    weight_decay_rate=0.01,\n",")\n","model.compile(optimizer=optimizer)\n","\n","# Entraîner en mixed-precision float16\n","tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"coKUr5Pf1i9X"},"outputs":[],"source":["from transformers.keras_callbacks import PushToHubCallback\n","\n","callback = PushToHubCallback(output_dir=\"codeparrot-ds\", tokenizer=tokenizer)\n","\n","model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9J69EKkI1i9Y"},"outputs":[],"source":["from transformers import pipeline\n","\n","course_model = TFGPT2LMHeadModel.from_pretrained(\"huggingface-course/codeparrot-ds\")\n","course_tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/codeparrot-ds\")\n","pipe = pipeline(\n","    \"text-generation\", model=course_model, tokenizer=course_tokenizer, device=0\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fTR2iAOa1i9a"},"outputs":[],"source":["txt = \"\"\"\\\n","# create some data\n","x = np.random.randn(100)\n","y = np.random.randn(100)\n","\n","# create scatter plot with x, y\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C_hSO98A1i9b"},"outputs":[],"source":["txt = \"\"\"\\\n","# create some data\n","x = np.random.randn(100)\n","y = np.random.randn(100)\n","\n","# create dataframe from x and y\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lmUipKiX1i9e"},"outputs":[],"source":["txt = \"\"\"\\\n","# dataframe with profession, income and name\n","df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n","\n","# calculate the mean income per profession\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fcgR0Fry1i9g"},"outputs":[],"source":["txt = \"\"\"\n","# import random forest regressor from scikit-learn\n","from sklearn.ensemble import RandomForestRegressor\n","\n","# fit random forest model with 300 estimators on X, y:\n","\"\"\"\n","print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter7/section7_pt.ipynb b/course/fr/chapter7/section7_pt.ipynb
deleted file mode 100644
index 1debf6cf..00000000
--- a/course/fr/chapter7/section7_pt.ipynb
+++ /dev/null
@@ -1,1051 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LtL6pxC3QK6k"
-   },
-   "source": [
-    "# Réponses aux questions (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6BeDhSYwQK6m"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 *Datasets* et 🤗 *Transformers* pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "aFM3SNcQQK6o"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# Pour exécuter l'entraînement sur TPU, vous devez décommenter la ligne suivante :\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "scOm799eQK6p"
-   },
-   "source": [
-    "Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uBSqsaB2QK6q"
-   },
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cUwgvueJQK6r"
-   },
-   "source": [
-    "Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "IjUdvDURQK6r"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "4y0INx16_YJI"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "raw_datasets = load_dataset(\"piaf\")\n",
-    "\n",
-    "# Piaf n'ayant pas de jeu de données de test, nous en créons un\n",
-    "raw_datasets = raw_datasets['train']\n",
-    "raw_datasets = raw_datasets.train_test_split(test_size=0.2, shuffle=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "j8x478MCQK6t"
-   },
-   "outputs": [],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bvADO3btQK6u"
-   },
-   "outputs": [],
-   "source": [
-    "print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n",
-    "print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n",
-    "print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "LkgfLJzKAyL8"
-   },
-   "outputs": [],
-   "source": [
-    "raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "sSVNaNijQK6w"
-   },
-   "outputs": [],
-   "source": [
-    "print(raw_datasets[\"test\"][0][\"answers\"])\n",
-    "print(raw_datasets[\"test\"][2][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tqovc8IQQK6w"
-   },
-   "outputs": [],
-   "source": [
-    "print(raw_datasets[\"test\"][2][\"context\"])\n",
-    "print(raw_datasets[\"test\"][2][\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "1vIQVMVbQK6x"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"camembert-base\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "85KnO70zQK6x"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "MRNaEIRUQK6y"
-   },
-   "outputs": [],
-   "source": [
-    "context = raw_datasets[\"train\"][0][\"context\"]\n",
-    "question = raw_datasets[\"train\"][0][\"question\"]\n",
-    "\n",
-    "inputs = tokenizer(question, context)\n",
-    "tokenizer.decode(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "P7-ROeq8QK6y"
-   },
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5a-69XqDQK6z"
-   },
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "inputs.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "qEIj9Wg4QK6z"
-   },
-   "outputs": [],
-   "source": [
-    "inputs[\"overflow_to_sample_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JUami5uPQK60"
-   },
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][2:6][\"question\"],\n",
-    "    raw_datasets[\"train\"][2:6][\"context\"],\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n",
-    "print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "JsGGWylQQK60"
-   },
-   "outputs": [],
-   "source": [
-    "answers = raw_datasets[\"train\"][2:6][\"answers\"]\n",
-    "start_positions = []\n",
-    "end_positions = []\n",
-    "\n",
-    "for i, offset in enumerate(inputs[\"offset_mapping\"]):\n",
-    "    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n",
-    "    answer = answers[sample_idx]\n",
-    "    start_char = answer[\"answer_start\"][0]\n",
-    "    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "    sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "    # Trouver le début et la fin du contexte\n",
-    "    idx = 0\n",
-    "    while sequence_ids[idx] != 1:\n",
-    "        idx += 1\n",
-    "    context_start = idx\n",
-    "    while sequence_ids[idx] == 1:\n",
-    "        idx += 1\n",
-    "    context_end = idx - 1\n",
-    "\n",
-    "    # Si la réponse n'est pas entièrement dans le contexte, l'étiquette est (0, 0)\n",
-    "    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "        start_positions.append(0)\n",
-    "        end_positions.append(0)\n",
-    "    else:\n",
-    "        # Sinon, ce sont les positions de début et de fin du token\n",
-    "        idx = context_start\n",
-    "        while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "            idx += 1\n",
-    "        start_positions.append(idx - 1)\n",
-    "\n",
-    "        idx = context_end\n",
-    "        while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "            idx -= 1\n",
-    "        end_positions.append(idx + 1)\n",
-    "\n",
-    "start_positions, end_positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "T-2yLtjlQK61"
-   },
-   "outputs": [],
-   "source": [
-    "idx = 0\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "start = start_positions[idx]\n",
-    "end = end_positions[idx]\n",
-    "labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n",
-    "\n",
-    "print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fAgj10qeQK62"
-   },
-   "outputs": [],
-   "source": [
-    "idx = 4\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n",
-    "print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "WNhoHB2CQK62"
-   },
-   "outputs": [],
-   "source": [
-    "max_length = 384\n",
-    "stride = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_training_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    answers = examples[\"answers\"]\n",
-    "    start_positions = []\n",
-    "    end_positions = []\n",
-    "\n",
-    "    for i, offset in enumerate(offset_mapping):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        answer = answers[sample_idx]\n",
-    "        start_char = answer[\"answer_start\"][0]\n",
-    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "        # Trouver le début et la fin du contexte\n",
-    "        idx = 0\n",
-    "        while sequence_ids[idx] != 1:\n",
-    "            idx += 1\n",
-    "        context_start = idx\n",
-    "        while sequence_ids[idx] == 1:\n",
-    "            idx += 1\n",
-    "        context_end = idx - 1\n",
-    "\n",
-    "        # Si la réponse n'est pas entièrement dans le contexte, l'étiquette est (0, 0)\n",
-    "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "            start_positions.append(0)\n",
-    "            end_positions.append(0)\n",
-    "        else:\n",
-    "            # Sinon, ce sont les positions de début et de fin du token\n",
-    "            idx = context_start\n",
-    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "                idx += 1\n",
-    "            start_positions.append(idx - 1)\n",
-    "\n",
-    "            idx = context_end\n",
-    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "                idx -= 1\n",
-    "            end_positions.append(idx + 1)\n",
-    "\n",
-    "    inputs[\"start_positions\"] = start_positions\n",
-    "    inputs[\"end_positions\"] = end_positions\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "k4JdhH85QK63"
-   },
-   "outputs": [],
-   "source": [
-    "train_dataset = raw_datasets[\"train\"].map(\n",
-    "    preprocess_training_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"train\"]), len(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "idi_R9oBQK63"
-   },
-   "outputs": [],
-   "source": [
-    "def preprocess_validation_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    example_ids = []\n",
-    "\n",
-    "    for i in range(len(inputs[\"input_ids\"])):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        example_ids.append(examples[\"id\"][sample_idx])\n",
-    "\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "        offset = inputs[\"offset_mapping\"][i]\n",
-    "        inputs[\"offset_mapping\"][i] = [\n",
-    "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
-    "        ]\n",
-    "\n",
-    "    inputs[\"example_id\"] = example_ids\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "onDfkeWPQK64"
-   },
-   "outputs": [],
-   "source": [
-    "validation_dataset = raw_datasets[\"test\"].map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"test\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"test\"]), len(validation_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "K68F9QgOQK64"
-   },
-   "outputs": [],
-   "source": [
-    "small_eval_set = raw_datasets[\"test\"].select(range(100))\n",
-    "trained_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n",
-    "eval_set = small_eval_set.map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"test\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6rKHB7LNQK64"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Qzm-iQN0QK65"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoModelForQuestionAnswering\n",
-    "\n",
-    "eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "eval_set_for_model.set_format(\"torch\")\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: eval_set_for_model[k].to(device) for k in eval_set_for_model.column_names}\n",
-    "trained_model = AutoModelForQuestionAnswering.from_pretrained(trained_checkpoint).to(\n",
-    "    device\n",
-    ")\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trained_model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bnfZPG6SQK65"
-   },
-   "outputs": [],
-   "source": [
-    "start_logits = outputs.start_logits.cpu().numpy()\n",
-    "end_logits = outputs.end_logits.cpu().numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "8LFCmyBBQK65"
-   },
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "\n",
-    "example_to_features = collections.defaultdict(list)\n",
-    "for idx, feature in enumerate(eval_set):\n",
-    "    example_to_features[feature[\"example_id\"]].append(idx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "6R-G01nKQK65"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "n_best = 20\n",
-    "max_answer_length = 30\n",
-    "predicted_answers = []\n",
-    "\n",
-    "for example in small_eval_set:\n",
-    "    example_id = example[\"id\"]\n",
-    "    context = example[\"context\"]\n",
-    "    answers = []\n",
-    "\n",
-    "    for feature_index in example_to_features[example_id]:\n",
-    "        start_logit = start_logits[feature_index]\n",
-    "        end_logit = end_logits[feature_index]\n",
-    "        offsets = eval_set[\"offset_mapping\"][feature_index]\n",
-    "\n",
-    "        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        for start_index in start_indexes:\n",
-    "            for end_index in end_indexes:\n",
-    "                # Ignorer les réponses qui ne sont pas entièrement dans le contexte\n",
-    "                if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                    continue\n",
-    "                # Ignorer les réponses dont la longueur est soit < 0 soit > max_answer_length\n",
-    "                if (\n",
-    "                    end_index < start_index\n",
-    "                    or end_index - start_index + 1 > max_answer_length\n",
-    "                ):\n",
-    "                    continue\n",
-    "\n",
-    "                answers.append(\n",
-    "                    {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                )\n",
-    "\n",
-    "    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "d6iQFh4hQK66"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "iE6yr01MQK66"
-   },
-   "outputs": [],
-   "source": [
-    "theoretical_answers = [\n",
-    "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "TAL68eNyQK66"
-   },
-   "outputs": [],
-   "source": [
-    "print(predicted_answers[0])\n",
-    "print(theoretical_answers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "SVa0WCW-QK66"
-   },
-   "outputs": [],
-   "source": [
-    "metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "hdPf3xs9QK67"
-   },
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "\n",
-    "def compute_metrics(start_logits, end_logits, features, examples):\n",
-    "    example_to_features = collections.defaultdict(list)\n",
-    "    for idx, feature in enumerate(features):\n",
-    "        example_to_features[feature[\"example_id\"]].append(idx)\n",
-    "\n",
-    "    predicted_answers = []\n",
-    "    for example in tqdm(examples):\n",
-    "        example_id = example[\"id\"]\n",
-    "        context = example[\"context\"]\n",
-    "        answers = []\n",
-    "\n",
-    "        # Parcourir en boucle toutes les fonctionnalités associées à cet exemple\n",
-    "        for feature_index in example_to_features[example_id]:\n",
-    "            start_logit = start_logits[feature_index]\n",
-    "            end_logit = end_logits[feature_index]\n",
-    "            offsets = features[feature_index][\"offset_mapping\"]\n",
-    "\n",
-    "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            for start_index in start_indexes:\n",
-    "                for end_index in end_indexes:\n",
-    "                    # Ignorez les réponses qui ne sont pas entièrement dans le contexte\n",
-    "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                        continue\n",
-    "                    # Sauter les réponses dont la longueur est soit < 0, soit > max_answer_length\n",
-    "                    if (\n",
-    "                        end_index < start_index\n",
-    "                        or end_index - start_index + 1 > max_answer_length\n",
-    "                    ):\n",
-    "                        continue\n",
-    "\n",
-    "                    answer = {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                    answers.append(answer)\n",
-    "\n",
-    "        # Sélectionnez la réponse avec le meilleur score\n",
-    "        if len(answers) > 0:\n",
-    "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "            predicted_answers.append(\n",
-    "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
-    "            )\n",
-    "        else:\n",
-    "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
-    "\n",
-    "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
-    "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bKYqSj6LQK67"
-   },
-   "outputs": [],
-   "source": [
-    "compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "VbQQgkhsQK67"
-   },
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "pJqyc7vrQK68"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    \"camembert-base-finetuned-piaf\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5lsISGbRQK68"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=train_dataset,\n",
-    "    eval_dataset=validation_dataset,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "BZCgc7sJQK68"
-   },
-   "outputs": [],
-   "source": [
-    "predictions, _, _ = trainer.predict(validation_dataset)\n",
-    "start_logits, end_logits = predictions\n",
-    "compute_metrics(start_logits, end_logits, validation_dataset, raw_datasets[\"test\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "OzXfIms3QK69"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub(commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "coNkcqleQK69"
-   },
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "train_dataset.set_format(\"torch\")\n",
-    "validation_set = validation_dataset.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "validation_set.set_format(\"torch\")\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    train_dataset,\n",
-    "    shuffle=True,\n",
-    "    collate_fn=default_data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    validation_set, collate_fn=default_data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cml81PT9QK6-"
-   },
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-i_E-Qr9QK6-"
-   },
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "b0jtsA_BQK6-"
-   },
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator(fp16=True)\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "88PpNpYfQK6_"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "9ctafoG6QK6_"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"camembert-base-finetuned-piaf-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "GkEGULCrQK6_"
-   },
-   "outputs": [],
-   "source": [
-    "output_dir = \"camembert-base-finetuned-piaf-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-t6DsGmsQK7A"
-   },
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Entraînement\n",
-    "    model.train()\n",
-    "    for step, batch in enumerate(train_dataloader):\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    start_logits = []\n",
-    "    end_logits = []\n",
-    "    accelerator.print(\"Evaluation!\")\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        start_logits.append(accelerator.gather(outputs.start_logits).cpu().numpy())\n",
-    "        end_logits.append(accelerator.gather(outputs.end_logits).cpu().numpy())\n",
-    "\n",
-    "    start_logits = np.concatenate(start_logits)\n",
-    "    end_logits = np.concatenate(end_logits)\n",
-    "    start_logits = start_logits[: len(validation_dataset)]\n",
-    "    end_logits = end_logits[: len(validation_dataset)]\n",
-    "\n",
-    "    metrics = compute_metrics(\n",
-    "        start_logits, end_logits, validation_dataset, raw_datasets[\"test\"]\n",
-    "    )\n",
-    "    print(f\"epoch {epoch}:\", metrics)\n",
-    "\n",
-    "    # Sauvegarder et télécharger\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "h7iQN81gQK7B"
-   },
-   "outputs": [],
-   "source": [
-    "accelerator.wait_for_everyone()\n",
-    "unwrapped_model = accelerator.unwrap_model(model)\n",
-    "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "u3EKyP6RQK7B"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Remplacez par votre propre checkpoint\n",
-    "model_checkpoint = \"huggingface-course/camembert-finetuned-piaf\"\n",
-    "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
-    "\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers est soutenu par les trois bibliothèques d'apprentissage profond les plus populaires - Jax, PyTorch et TensorFlow - avec une intégration transparente entre elles. Il est simple d'entraîner vos modèles avec l'une avant de les charger pour l'inférence avec l'autre.\n",
-    "\"\"\"\n",
-    "question = \"Quelles sont les bibliothèques d'apprentissage profond derrière 🤗 Transformers ?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter7/section7_tf.ipynb b/course/fr/chapter7/section7_tf.ipynb
deleted file mode 100644
index 5b271385..00000000
--- a/course/fr/chapter7/section7_tf.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"HGh1J194-6C1"},"source":["# Réponses aux questions (TensorFlow)"]},{"cell_type":"markdown","metadata":{"id":"_D0YJQLQ-6C8"},"source":["Installez les bibliothèques Transformers et Datasets pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PbuWhWut-6C_"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!apt install git-lfs"]},{"cell_type":"markdown","metadata":{"id":"doBzkj04-6DE"},"source":["Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RrftULwz-6DG"},"outputs":[],"source":["!git config --global user.email \"you@example.com\"\n","!git config --global user.name \"Your Name\""]},{"cell_type":"markdown","metadata":{"id":"i7JiXHEJ-6DJ"},"source":["Vous devrez également être connecté au Hub d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3WA9tZTr-6DL"},"outputs":[],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zXgL3tjH-6DO"},"outputs":[],"source":["from datasets import load_dataset\n","raw_datasets = load_dataset(\"piaf\")\n","\n","# piaf n'ayant pas de jeu de données de validation, nous en créons un\n","raw_datasets = raw_datasets['train']\n","raw_datasets = raw_datasets.train_test_split(test_size=0.2, shuffle=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yYbRuSXg-6DR"},"outputs":[],"source":["raw_datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PHvaIQJ8-6DV"},"outputs":[],"source":["print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n","print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n","print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AXrHxDE--6DY"},"outputs":[],"source":["raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"siPu7XwS-6Db"},"outputs":[],"source":["print(raw_datasets[\"test\"][0][\"answers\"])\n","print(raw_datasets[\"test\"][2][\"answers\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"01r7bYJ1-6Dd"},"outputs":[],"source":["print(raw_datasets[\"test\"][2][\"context\"])\n","print(raw_datasets[\"test\"][2][\"question\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CK1bY6-D-6Df"},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","model_checkpoint = \"camembert-base\"\n","tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"K-11H0Mj-6Di"},"outputs":[],"source":["tokenizer.is_fast"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-uzJDe1H-6Dk"},"outputs":[],"source":["context = raw_datasets[\"train\"][0][\"context\"]\n","question = raw_datasets[\"train\"][0][\"question\"]\n","\n","inputs = tokenizer(question, context)\n","tokenizer.decode(inputs[\"input_ids\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o3nJtrwn-6Dm"},"outputs":[],"source":["inputs = tokenizer(\n","    question,\n","    context,\n","    max_length=100,\n","    truncation=\"only_second\",\n","    stride=50,\n","    return_overflowing_tokens=True,\n",")\n","\n","for ids in inputs[\"input_ids\"]:\n","    print(tokenizer.decode(ids))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ci2W6YzC-6Dp"},"outputs":[],"source":["inputs = tokenizer(\n","    question,\n","    context,\n","    max_length=100,\n","    truncation=\"only_second\",\n","    stride=50,\n","    return_overflowing_tokens=True,\n","    return_offsets_mapping=True,\n",")\n","inputs.keys()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vGefiC_M-6Dq"},"outputs":[],"source":["inputs[\"overflow_to_sample_mapping\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RBKy01q8-6Ds"},"outputs":[],"source":["inputs = tokenizer(\n","    raw_datasets[\"train\"][2:6][\"question\"],\n","    raw_datasets[\"train\"][2:6][\"context\"],\n","    max_length=100,\n","    truncation=\"only_second\",\n","    stride=50,\n","    return_overflowing_tokens=True,\n","    return_offsets_mapping=True,\n",")\n","\n","print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n","print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1vSiF6et-6Du"},"outputs":[],"source":["answers = raw_datasets[\"train\"][2:6][\"answers\"]\n","start_positions = []\n","end_positions = []\n","\n","for i, offset in enumerate(inputs[\"offset_mapping\"]):\n","    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n","    answer = answers[sample_idx]\n","    start_char = answer[\"answer_start\"][0]\n","    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n","    sequence_ids = inputs.sequence_ids(i)\n","\n","    # Trouver le début et la fin du contexte\n","    idx = 0\n","    while sequence_ids[idx] != 1:\n","        idx += 1\n","    context_start = idx\n","    while sequence_ids[idx] == 1:\n","        idx += 1\n","    context_end = idx - 1\n","\n","    # Si la réponse n'est pas entièrement dans le contexte, l'étiquette est (0, 0)\n","    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n","        start_positions.append(0)\n","        end_positions.append(0)\n","    else:\n","        # Sinon, ce sont les positions de début et de fin du token\n","        idx = context_start\n","        while idx <= context_end and offset[idx][0] <= start_char:\n","            idx += 1\n","        start_positions.append(idx - 1)\n","\n","        idx = context_end\n","        while idx >= context_start and offset[idx][1] >= end_char:\n","            idx -= 1\n","        end_positions.append(idx + 1)\n","\n","start_positions, end_positions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MP2PGe0Z-6Dx"},"outputs":[],"source":["idx = 0\n","sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n","answer = answers[sample_idx][\"text\"][0]\n","\n","start = start_positions[idx]\n","end = end_positions[idx]\n","labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n","\n","print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fh7d6cRa-6Dy"},"outputs":[],"source":["idx = 4\n","sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n","answer = answers[sample_idx][\"text\"][0]\n","\n","decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n","print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hi3kJcOz-6D0"},"outputs":[],"source":["max_length = 384\n","stride = 128\n","\n","\n","def preprocess_training_examples(examples):\n","    questions = [q.strip() for q in examples[\"question\"]]\n","    inputs = tokenizer(\n","        questions,\n","        examples[\"context\"],\n","        max_length=max_length,\n","        truncation=\"only_second\",\n","        stride=stride,\n","        return_overflowing_tokens=True,\n","        return_offsets_mapping=True,\n","        padding=\"max_length\",\n","    )\n","\n","    offset_mapping = inputs.pop(\"offset_mapping\")\n","    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n","    answers = examples[\"answers\"]\n","    start_positions = []\n","    end_positions = []\n","\n","    for i, offset in enumerate(offset_mapping):\n","        sample_idx = sample_map[i]\n","        answer = answers[sample_idx]\n","        start_char = answer[\"answer_start\"][0]\n","        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n","        sequence_ids = inputs.sequence_ids(i)\n","\n","        # Trouver le début et la fin du contexte\n","        idx = 0\n","        while sequence_ids[idx] != 1:\n","            idx += 1\n","        context_start = idx\n","        while sequence_ids[idx] == 1:\n","            idx += 1\n","        context_end = idx - 1\n","\n","        # Si la réponse n'est pas entièrement dans le contexte, l'étiquette est (0, 0)\n","        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n","            start_positions.append(0)\n","            end_positions.append(0)\n","        else:\n","            # Sinon, ce sont les positions de début et de fin du token\n","            idx = context_start\n","            while idx <= context_end and offset[idx][0] <= start_char:\n","                idx += 1\n","            start_positions.append(idx - 1)\n","\n","            idx = context_end\n","            while idx >= context_start and offset[idx][1] >= end_char:\n","                idx -= 1\n","            end_positions.append(idx + 1)\n","\n","    inputs[\"start_positions\"] = start_positions\n","    inputs[\"end_positions\"] = end_positions\n","    return inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6SHIfALd-6D2"},"outputs":[],"source":["train_dataset = raw_datasets[\"train\"].map(\n","    preprocess_training_examples,\n","    batched=True,\n","    remove_columns=raw_datasets[\"train\"].column_names,\n",")\n","len(raw_datasets[\"train\"]), len(train_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rvRbrkC7-6D4"},"outputs":[],"source":["def preprocess_validation_examples(examples):\n","    questions = [q.strip() for q in examples[\"question\"]]\n","    inputs = tokenizer(\n","        questions,\n","        examples[\"context\"],\n","        max_length=max_length,\n","        truncation=\"only_second\",\n","        stride=stride,\n","        return_overflowing_tokens=True,\n","        return_offsets_mapping=True,\n","        padding=\"max_length\",\n","    )\n","\n","    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n","    example_ids = []\n","\n","    for i in range(len(inputs[\"input_ids\"])):\n","        sample_idx = sample_map[i]\n","        example_ids.append(examples[\"id\"][sample_idx])\n","\n","        sequence_ids = inputs.sequence_ids(i)\n","        offset = inputs[\"offset_mapping\"][i]\n","        inputs[\"offset_mapping\"][i] = [\n","            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n","        ]\n","\n","    inputs[\"example_id\"] = example_ids\n","    return inputs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sDYUTCDV-6D5"},"outputs":[],"source":["validation_dataset = raw_datasets[\"test\"].map(\n","    preprocess_validation_examples,\n","    batched=True,\n","    remove_columns=raw_datasets[\"test\"].column_names,\n",")\n","len(raw_datasets[\"test\"]), len(validation_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vhsEOZ8r-6D7"},"outputs":[],"source":["small_eval_set = raw_datasets[\"test\"].select(range(100))\n","trained_checkpoint = \"etalab-ia/camembert-base-squadFR-fquad-piaf\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n","eval_set = small_eval_set.map(\n","    preprocess_validation_examples,\n","    batched=True,\n","    remove_columns=raw_datasets[\"test\"].column_names,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RORmy2Wq-6D8"},"outputs":[],"source":["import tensorflow as tf\n","from transformers import TFAutoModelForQuestionAnswering\n","\n","eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n","eval_set_for_model.set_format(\"numpy\")\n","\n","batch = {k: eval_set_for_model[k] for k in eval_set_for_model.column_names}\n","trained_model = TFAutoModelForQuestionAnswering.from_pretrained(trained_checkpoint)\n","\n","outputs = trained_model(**batch)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BW4QUi-k-6D9"},"outputs":[],"source":["start_logits = outputs.start_logits.numpy()\n","end_logits = outputs.end_logits.numpy()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5hV8_sh9-6D-"},"outputs":[],"source":["import collections\n","\n","example_to_features = collections.defaultdict(list)\n","for idx, feature in enumerate(eval_set):\n","    example_to_features[feature[\"example_id\"]].append(idx)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-hKjStIc-6D_"},"outputs":[],"source":["import numpy as np\n","\n","n_best = 20\n","max_answer_length = 30\n","predicted_answers = []\n","\n","for example in small_eval_set:\n","    example_id = example[\"id\"]\n","    context = example[\"context\"]\n","    answers = []\n","\n","    for feature_index in example_to_features[example_id]:\n","        start_logit = start_logits[feature_index]\n","        end_logit = end_logits[feature_index]\n","        offsets = eval_set[\"offset_mapping\"][feature_index]\n","\n","        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n","        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n","        for start_index in start_indexes:\n","            for end_index in end_indexes:\n","                # Ignorez les réponses qui ne sont pas entièrement dans le contexte\n","                if offsets[start_index] is None or offsets[end_index] is None:\n","                    continue\n","                # Ignorer les réponses dont la longueur est soit < 0 soit > max_answer_length\n","                if (\n","                    end_index < start_index\n","                    or end_index - start_index + 1 > max_answer_length\n","                ):\n","                    continue\n","\n","                answers.append(\n","                    {\n","                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n","                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n","                    }\n","                )\n","\n","    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n","    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VVvRfp2S-6EA"},"outputs":[],"source":["from datasets import load_metric\n","\n","metric = load_metric(\"squad\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sJxavbdw-6EC"},"outputs":[],"source":["theoretical_answers = [\n","    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yGhhhyuG-6ED"},"outputs":[],"source":["print(predicted_answers[0])\n","print(theoretical_answers[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sucm3mpY-6EE"},"outputs":[],"source":["metric.compute(predictions=predicted_answers, references=theoretical_answers)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"siSSkgPr-6EF"},"outputs":[],"source":["from tqdm.auto import tqdm\n","\n","\n","def compute_metrics(start_logits, end_logits, features, examples):\n","    example_to_features = collections.defaultdict(list)\n","    for idx, feature in enumerate(features):\n","        example_to_features[feature[\"example_id\"]].append(idx)\n","\n","    predicted_answers = []\n","    for example in tqdm(examples):\n","        example_id = example[\"id\"]\n","        context = example[\"context\"]\n","        answers = []\n","\n","        # Parcourir en boucle toutes les fonctionnalités associées à cet exemple\n","        for feature_index in example_to_features[example_id]:\n","            start_logit = start_logits[feature_index]\n","            end_logit = end_logits[feature_index]\n","            offsets = features[feature_index][\"offset_mapping\"]\n","\n","            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n","            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n","            for start_index in start_indexes:\n","                for end_index in end_indexes:\n","                    # Ignorez les réponses qui ne sont pas entièrement dans le contexte\n","                    if offsets[start_index] is None or offsets[end_index] is None:\n","                        continue\n","                    # Sauter les réponses dont la longueur est soit < 0, soit > max_answer_length\n","                    if (\n","                        end_index < start_index\n","                        or end_index - start_index + 1 > max_answer_length\n","                    ):\n","                        continue\n","\n","                    answer = {\n","                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n","                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n","                    }\n","                    answers.append(answer)\n","\n","        # Sélectionnez la réponse avec le meilleur score\n","        if len(answers) > 0:\n","            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n","            predicted_answers.append(\n","                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n","            )\n","        else:\n","            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n","\n","    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n","    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iU38vi18-6EG"},"outputs":[],"source":["compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"]},{"cell_type":"code","source":["model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"],"metadata":{"id":"LGfylMwTWQTo"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dMhxGyY8-6EI"},"outputs":[],"source":["from transformers import DefaultDataCollator\n","\n","data_collator = DefaultDataCollator(return_tensors=\"tf\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pBxcX4uk-6EJ"},"outputs":[],"source":["tf_train_dataset = model.prepare_tf_dataset(\n","    train_dataset,\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=16)\n","\n","tf_eval_dataset = model.prepare_tf_dataset(\n","    validation_dataset,\n","    collate_fn=data_collator,\n","    shuffle=False,\n","    batch_size=16)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YvG7qgX0-6EK"},"outputs":[],"source":["from transformers import create_optimizer\n","from transformers.keras_callbacks import PushToHubCallback\n","import tensorflow as tf\n","\n","# Le nombre d'étapes d'entraînement est le nombre d'échantillons dans le jeu de données, divisé par la taille du batch puis multiplié\n","# par le nombre total d'époques. Notez que le jeu de données tf_train_dataset est ici un batch de données tf.data.Dataset,\n","# pas le jeu de données original Hugging Face, donc son len() est déjà num_samples // batch_size.\n","num_train_epochs = 3\n","num_train_steps = len(tf_train_dataset) * num_train_epochs\n","optimizer, schedule = create_optimizer(\n","    init_lr=2e-5,\n","    num_warmup_steps=0,\n","    num_train_steps=num_train_steps,\n","    weight_decay_rate=0.01,\n",")\n","model.compile(optimizer=optimizer)\n","\n","# Entraîner en mixed-precision float16\n","tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JwQSfd8n-6EK"},"outputs":[],"source":["from transformers.keras_callbacks import PushToHubCallback\n","\n","callback = PushToHubCallback(output_dir=\"camembert-base-finetuned-piaf\", tokenizer=tokenizer)\n","\n","# Nous allons faire la validation après, donc pas de validation au milieu de l'entraînement\n","model.fit(tf_train_dataset, callbacks=[callback], epochs=num_train_epochs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QJrxPbil-6EL"},"outputs":[],"source":["predictions = model.predict(tf_eval_dataset)\n","compute_metrics(\n","    predictions[\"start_logits\"],\n","    predictions[\"end_logits\"],\n","    validation_dataset,\n","    raw_datasets[\"test\"],\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FreEVT3W-6EL"},"outputs":[],"source":["from transformers import pipeline\n","\n","# Remplacez par votre propre checkpoint\n","model_checkpoint = \"huggingface-course/camembert-finetuned-piaf\"\n","question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n","\n","context = \"\"\"\n","🤗 Transformers est soutenu par les trois bibliothèques d'apprentissage profond les plus populaires - Jax, PyTorch et TensorFlow - avec une intégration transparente entre elles. Il est simple d'entraîner vos modèles avec l'une avant de les charger pour l'inférence avec l'autre.\n","\"\"\"\n","question = \"Quelles sont les bibliothèques d'apprentissage profond derrière 🤗 Transformers ?\"\n","question_answerer(question=question, context=context)"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter8/section2.ipynb b/course/fr/chapter8/section2.ipynb
deleted file mode 100644
index 03537a26..00000000
--- a/course/fr/chapter8/section2.ipynb
+++ /dev/null
@@ -1,301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "u2AQa8mSutMi"
-   },
-   "source": [
-    "# Que faire quand vous obtenez une erreur\n",
-    "\n",
-    "Ce chapitre portant sur le débogage, la langue nous importe peu ici. Nous nous intéressons surtout à la logique du code pour comprendre d'où provient l'erreur."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "P2ni9cGhutMj"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "X6cCDJcYutMk"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IxfNJAyputMm"
-   },
-   "source": [
-    "Vous aurez besoin de configurer git, adaptez votre email et votre nom dans la cellule suivante."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "0jGA8gKSutMn"
-   },
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AKfiKg4_utMn"
-   },
-   "source": [
-    "Vous devrez également être connecté au *Hub* d'Hugging Face. Exécutez ce qui suit et entrez vos informations d'identification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "tzQgJTfrutMo"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "eE4HjzUiutMp"
-   },
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Cloner le dépôt et extraire le chemin local\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Créer un dépôt vide sur le Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Clonez le dépôt vide\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Copier les fichiers\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Pousser sur le Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "yCAxD5q2utMp",
-    "outputId": "af9064c9-2d5e-4344-8925-0444cdf5dbad"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "n9CaRxrqutMq",
-    "outputId": "ab9e1b9c-3c06-4df2-fbeb-0cce8a9feebe"
-   },
-   "outputs": [],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "pRlCOKgzutMq",
-    "outputId": "a1875547-98ad-445d-acf8-a40f538e16e1"
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "244m28l_utMs"
-   },
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fGLU4XLjutMs"
-   },
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_weqzIC7utMs",
-    "outputId": "3780c995-7148-48e7-9886-f772e4f1f9f9"
-   },
-   "outputs": [],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "s99eTrMJutMt"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "-8Dv8M3RutMt"
-   },
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "zO_yZE_SutMu",
-    "outputId": "bb218c79-77de-4e55-e3bb-48b9efd0d14f"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Obtenir le début de réponse le plus probable avec l'argmax du score\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Obtenir la fin de réponse la plus probable avec l'argmax du score\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FiT9Y5bCutMu",
-    "outputId": "e737e934-9611-4120-aa1c-97f498e19696"
-   },
-   "outputs": [],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rUuyiTdLutMv",
-    "outputId": "a5099876-f9da-452d-b607-cdb262db6eff"
-   },
-   "outputs": [],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter8/section3.ipynb b/course/fr/chapter8/section3.ipynb
deleted file mode 100644
index 2d36e4b5..00000000
--- a/course/fr/chapter8/section3.ipynb
+++ /dev/null
@@ -1,144 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Demander de l'aide sur les forums"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModel.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"\"\"\n",
-    "Generation One is a retroactive term for the Transformers characters that\n",
-    "appeared between 1984 and 1993. The Transformers began with the 1980s Japanese\n",
-    "toy lines Micro Change and Diaclone. They presented robots able to transform\n",
-    "into everyday vehicles, electronic items or weapons. Hasbro bought the Micro\n",
-    "Change and Diaclone toys, and partnered with Takara. Marvel Comics was hired by\n",
-    "Hasbro to create the backstory; editor-in-chief Jim Shooter wrote an overall\n",
-    "story, and gave the task of creating the characthers to writer Dennis O'Neil.\n",
-    "Unhappy with O'Neil's work (although O'Neil created the name \"Optimus Prime\"),\n",
-    "Shooter chose Bob Budiansky to create the characters.\n",
-    "\n",
-    "The Transformers mecha were largely designed by Shōji Kawamori, the creator of\n",
-    "the Japanese mecha anime franchise Macross (which was adapted into the Robotech\n",
-    "franchise in North America). Kawamori came up with the idea of transforming\n",
-    "mechs while working on the Diaclone and Macross franchises in the early 1980s\n",
-    "(such as the VF-1 Valkyrie in Macross and Robotech), with his Diaclone mechs\n",
-    "later providing the basis for Transformers.\n",
-    "\n",
-    "The primary concept of Generation One is that the heroic Optimus Prime, the\n",
-    "villainous Megatron, and their finest soldiers crash land on pre-historic Earth\n",
-    "in the Ark and the Nemesis before awakening in 1985, Cybertron hurtling through\n",
-    "the Neutral zone as an effect of the war. The Marvel comic was originally part\n",
-    "of the main Marvel Universe, with appearances from Spider-Man and Nick Fury,\n",
-    "plus some cameos, as well as a visit to the Savage Land.\n",
-    "\n",
-    "The Transformers TV series began around the same time. Produced by Sunbow\n",
-    "Productions and Marvel Productions, later Hasbro Productions, from the start it\n",
-    "contradicted Budiansky's backstories. The TV series shows the Autobots looking\n",
-    "for new energy sources, and crash landing as the Decepticons attack. Marvel\n",
-    "interpreted the Autobots as destroying a rogue asteroid approaching Cybertron.\n",
-    "Shockwave is loyal to Megatron in the TV series, keeping Cybertron in a\n",
-    "stalemate during his absence, but in the comic book he attempts to take command\n",
-    "of the Decepticons. The TV series would also differ wildly from the origins\n",
-    "Budiansky had created for the Dinobots, the Decepticon turned Autobot Jetfire\n",
-    "(known as Skyfire on TV), the Constructicons (who combine to form\n",
-    "Devastator),[19][20] and Omega Supreme. The Marvel comic establishes early on\n",
-    "that Prime wields the Creation Matrix, which gives life to machines. In the\n",
-    "second season, the two-part episode The Key to Vector Sigma introduced the\n",
-    "ancient Vector Sigma computer, which served the same original purpose as the\n",
-    "Creation Matrix (giving life to Transformers), and its guardian Alpha Trion.\n",
-    "\"\"\"\n",
-    "\n",
-    "text_fr = \"\"\"\n",
-    "Génération 1 est un terme rétroactif pour les personnages de Transformers qui sont apparus\n",
-    "entre 1984 et 1993. Les Transformers ont commencé avec les lignes de jouets japonaises \n",
-    "des années 1980, Micro Change et Diaclone. Elles présentaient des robots capables\n",
-    "de se transformer en véhicules de tous les jours, en objets électroniques ou en armes. \n",
-    "Hasbro a acheté les jouets Micro Change et Diaclone, et s'est associé à Takara. \n",
-    "Marvel Comics est engagé par Hasbro pour créer l'histoire de fond ; le rédacteur en chef \n",
-    "Jim Shooter a écrit une histoire générale et confie la tâche de créer les personnages au \n",
-    "scénariste Dennis O'Neil. Mécontent du travail d'O'Neil (bien que ce dernier ait créé \n",
-    "le nom \"Optimus Prime\"), Shooter choisit Bob Budiansky pour créer les personnages.\n",
-    "\n",
-    "Les mecha de Transformers ont été en grande partie conçus par Shōji Kawamori, le créateur\n",
-    "de l'anime japonais Macross (qui a été adapté en Robotech en Amérique du Nord). Kawamori\n",
-    "a eu l'idée de transformer des mechas transformables alors qu'il travaillait sur les\n",
-    "franchises Diaclone et Macross au début des années 1980 (comme le VF-1 Valkyrie dans \n",
-    "Macross et Robotech), et ses méchas Diaclone ont plus tard servi de base à Transformers.\n",
-    "\n",
-    "Le concept principal de la Génération 1 est que l'héroïque Optimus Prime, le méchant\n",
-    "Megatron, et leurs meilleurs soldats s'écrasent sur une Terre préhistorique dans l'Arche\n",
-    "et le Némésis avant de se réveiller en 1985, Cybertron traversant à toute allure la zone\n",
-    "neutre en raison de la guerre. La bande dessinée Marvel faisait à l'origine partie \n",
-    "de l'univers principal de Marvel, avec des apparitions de Spider-Man et Nick Fury, \n",
-    "plus quelques caméos, ainsi qu'une visite à la Terre Sauvage.\n",
-    "\n",
-    "La série télévisée Transformers a commencé à peu près à la même époque. \n",
-    "Produite par Sunbow Productions et Marvel Productions, puis Hasbro Productions, \n",
-    "dès le début elle a contredit les histoires de Budiansky. La série TV montre les Autobots\n",
-    "cherchant de nouvelles sources d'énergie et s'écrasent lors de l'attaque des Decepticons. \n",
-    "Marvel a interprété les Autobots comme la destruction d'un astéroïde malveillant \n",
-    "s'approchant de Cybertron. Shockwave est loyal envers Megatron dans la série TV, \n",
-    "et maintient Cybertron dans une impasse en son absence.\n",
-    "Cybertron dans une impasse pendant son absence, mais dans la BD, \n",
-    "il tente de prendre le commandement des Decepticons. \n",
-    "La série télévisée s'écarte aussi radicalement des origines que Budiansky avait \n",
-    "créé pour les Dinobots, le Decepticon devenu Autobot Jetfire\n",
-    "(connu sous le nom de Skyfire à la télévision), \n",
-    "les Constructicons (qui s'associent pour former Devastator) et Oméga Suprême. \n",
-    "La bande dessinée Marvel établit très tôt que Prime manie la matrice de création,\n",
-    "qui donne la vie aux machines. Dans la saison, l'épisode en deux parties \n",
-    "The Key to Vector Sigma a introduit l'ancien ordinateur l'ancien ordinateur\n",
-    "Vector Sigma, qui servait le même objectif original que la matrice de création \n",
-    "(donner la vie aux Transformers), et son gardien Alpha Trion.\n",
-    "\"\"\"\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "logits = model(**inputs).logits"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Demander de l'aide sur les forums",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter8/section4.ipynb b/course/fr/chapter8/section4.ipynb
deleted file mode 100644
index 04dc0db1..00000000
--- a/course/fr/chapter8/section4.ipynb
+++ /dev/null
@@ -1,727 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "f0x7V8yXuug3"
-   },
-   "source": [
-    "# Déboguer le pipeline d'entraînement\n",
-    "\n",
-    "Ce chapitre portant sur le débogage, la langue nous importe peu ici. Nous nous intéressons surtout à la logique du code pour comprendre d'où provient l'erreur."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "UEU_G8vPuug5"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Y-jlJv25uug5"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "1P4rPHpjuug7",
-    "outputId": "9187a2a7-dca6-4b94-bce9-95f4422b893f"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=raw_datasets[\"train\"],\n",
-    "    eval_dataset=raw_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RpbitvtGuug9",
-    "outputId": "d8ee2113-612a-4dd2-db95-b0c8ec4aa34b"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Jfb5bjfpuug-",
-    "outputId": "fe25eb2b-5f82-46fa-f0db-e542b51a2b02"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5hEziUUKuuhA",
-    "outputId": "e3824437-3921-4276-d080-80119eafe347"
-   },
-   "outputs": [],
-   "source": [
-    "tokenizer.decode(trainer.train_dataset[0][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "QmlaOe9vuuhB",
-    "outputId": "520dbef0-aba9-4a7e-f9d7-e58c674914bd"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train_dataset[0].keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "CUPHJfK7uuhB",
-    "outputId": "b50e1cdb-4c73-49c2-e804-759bfb3cb7ad"
-   },
-   "outputs": [],
-   "source": [
-    "type(trainer.model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "8z6fWGP3uuhC",
-    "outputId": "371b4974-abec-4702-e9dd-2c174030c87b"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train_dataset[0][\"attention_mask\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "8QaC9HaAuuhD",
-    "outputId": "5eea24ac-aa92-423e-cbee-3bd0cafe1da9"
-   },
-   "outputs": [],
-   "source": [
-    "len(trainer.train_dataset[0][\"attention_mask\"]) == len(\n",
-    "    trainer.train_dataset[0][\"input_ids\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "O_p1b0eFuuhD",
-    "outputId": "957ec941-7d95-44cd-e5e6-d1c13049c147"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train_dataset[0][\"label\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "A2gIbdMeuuhE",
-    "outputId": "7abc86fe-97d0-4b8b-e1b8-85a431b39f57"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.train_dataset.features[\"label\"].names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "BD78i7gduuhE",
-    "outputId": "df0ad6c6-2db1-46a2-d0ec-7644cb10021f"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "3XyCqZ6fuuhF",
-    "outputId": "95ec7610-cc00-466b-8ebe-68005fda0c4d"
-   },
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "data_collator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "wNaHrxC_uuhF",
-    "outputId": "5eec7369-2582-4812-dc01-0241f163f91a"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "kTdU1zHxuuhG"
-   },
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "batch = data_collator([trainer.train_dataset[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "puZHYwznuuhH"
-   },
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "actual_train_set = trainer._remove_unused_columns(trainer.train_dataset)\n",
-    "batch = data_collator([actual_train_set[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ZgkgG5wTuuhH"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RNbbigNxuuhH",
-    "outputId": "469b3dbb-b037-4ba1-8b89-9b931730757f"
-   },
-   "outputs": [],
-   "source": [
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "FoVsBT4juuhH",
-    "outputId": "229f92ef-696e-4500-a5a4-1f8fc964f56e"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "d4xgB4hluuhI"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "e_qqisTBuuhI"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "DSRfCJJluuhI"
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "outputs = trainer.model.to(device)(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "XdAPze1RuuhK"
-   },
-   "outputs": [],
-   "source": [
-    "loss = outputs.loss\n",
-    "loss.backward()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fo6iA0tkuuhK"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.create_optimizer()\n",
-    "trainer.optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mw2fCqo1uuhK",
-    "outputId": "b6dda37e-8458-4bb9-b632-fb1a954dc080"
-   },
-   "outputs": [],
-   "source": [
-    "# This will take a long time and error out, so you shouldn't run this cell\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cqOlsDu7uuhL",
-    "outputId": "34d59c53-4025-4bab-eb54-a030065ad33f"
-   },
-   "outputs": [],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NE0RLsNAuuhL"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_eval_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "QWSy7QSZuuhL",
-    "outputId": "fffbd1fa-d215-43dc-ebe1-94273d49bec2"
-   },
-   "outputs": [],
-   "source": [
-    "predictions = outputs.logits.cpu().numpy()\n",
-    "labels = batch[\"labels\"].cpu().numpy()\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "oim704wtuuhL",
-    "outputId": "f060baf3-91d1-4496-b77d-1bceaf1a1371"
-   },
-   "outputs": [],
-   "source": [
-    "predictions.shape, labels.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "EvvBlvf-uuhL",
-    "outputId": "ae10fe2c-3add-455e-e659-edbac1cfa24d"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fnGWpZsTuuhM"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KwImoBnJuuhM"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "trainer.create_optimizer()\n",
-    "\n",
-    "for _ in range(20):\n",
-    "    outputs = trainer.model(**batch)\n",
-    "    loss = outputs.loss\n",
-    "    loss.backward()\n",
-    "    trainer.optimizer.step()\n",
-    "    trainer.optimizer.zero_grad()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Odz1EwppuuhN",
-    "outputId": "7215dd2e-6951-4644-c9b9-31849e786434"
-   },
-   "outputs": [],
-   "source": [
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)\n",
-    "preds = outputs.logits\n",
-    "labels = batch[\"labels\"]\n",
-    "\n",
-    "compute_metrics((preds.cpu().numpy(), labels.cpu().numpy()))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter8/section4_tf.ipynb b/course/fr/chapter8/section4_tf.ipynb
deleted file mode 100644
index b512ea5e..00000000
--- a/course/fr/chapter8/section4_tf.ipynb
+++ /dev/null
@@ -1,260 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "aAa7CFu0ut_D"
-   },
-   "source": [
-    "# Déboguer le pipeline d'entraînement\n",
-    "\n",
-    "Ce chapitre portant sur le débogage, la langue nous importe peu ici. Nous nous intéressons surtout à la logique du code pour comprendre d'où provient l'erreur."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "66qjHa3Hut_G"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "z9pfqA-kut_I"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "fLcbZLV8ut_J",
-    "outputId": "7d6926ff-91c6-40e3-924b-c14a9f1a5e49"
-   },
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    TFAutoModelForSequenceClassification,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "\n",
-    "train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "validation_dataset = tokenized_datasets[\"validation_matched\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
-    "\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "HX5TWpVeut_M",
-    "outputId": "a3831754-f919-456d-c229-5cbe217b7cbe"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "iXW275EBut_O",
-    "outputId": "e7523e04-ce01-4127-832b-d7282eb6615f"
-   },
-   "outputs": [],
-   "source": [
-    "model.compile(optimizer=\"adam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "bNtmviGfut_P",
-    "outputId": "b3a64beb-2bd3-4593-fb62-a035a5062cf8"
-   },
-   "outputs": [],
-   "source": [
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "j9ocmoJOut_P",
-    "outputId": "4eafd9ef-3285-4d3c-fa96-b8c24b2e9896"
-   },
-   "outputs": [],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "y1b677ecut_Q",
-    "outputId": "5dc22488-04a2-4f89-e8e5-309361694d0a"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "loss = model(batch).loss.numpy()\n",
-    "indices = np.flatnonzero(np.isnan(loss))\n",
-    "indices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "7Db3uwJ8ut_S",
-    "outputId": "4c4e948e-3278-4c37-aa54-2a2ddb56342d"
-   },
-   "outputs": [],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "input_ids[indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "jYKiD7ewut_T",
-    "outputId": "e5ddf829-d022-41cf-c9e7-83ddf0ed2005"
-   },
-   "outputs": [],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RmzOOSMOut_U"
-   },
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model.compile(optimizer=Adam(5e-5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ewvva33out_V",
-    "outputId": "08f45348-e17d-4f1f-ad6a-53dca09f060b"
-   },
-   "outputs": [],
-   "source": [
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mZiAw85Uut_W"
-   },
-   "outputs": [],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "tokenizer.decode(input_ids[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "g3qWOkb1ut_W"
-   },
-   "outputs": [],
-   "source": [
-    "labels = batch[\"labels\"].numpy()\n",
-    "label = labels[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "vicbZa70ut_W"
-   },
-   "outputs": [],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break\n",
-    "\n",
-    "# Assurez-vous que vous avez exécuté model.compile() et défini votre optimiseur,\n",
-    "# et vos pertes/métriques si vous les utilisez\n",
-    "\n",
-    "model.fit(batch, epochs=20)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter8/section5.ipynb b/course/fr/chapter8/section5.ipynb
deleted file mode 100644
index dfdd65ea..00000000
--- a/course/fr/chapter8/section5.ipynb
+++ /dev/null
@@ -1,42 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Comment rédiger une bonne issue"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Comment rédiger une bonne issue",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter8/section7.ipynb b/course/fr/chapter8/section7.ipynb
deleted file mode 100644
index 8fd99b06..00000000
--- a/course/fr/chapter8/section7.ipynb
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz de fin de chapitre"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)\n",
-    "# ---------------------------------------------------------------------------\n",
-    "# ImportError                               Traceback (most recent call last)\n",
-    "# /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_30848/333858878.py in <module>\n",
-    "# ----> 1 from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz de fin de chapitre",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/fr/chapter9/section2.ipynb b/course/fr/chapter9/section2.ipynb
deleted file mode 100644
index dee79e37..00000000
--- a/course/fr/chapter9/section2.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"1lFkgE7neW69"},"source":["# Construire votre première démo"]},{"cell_type":"markdown","metadata":{"id":"ALQ2eOJheW6_"},"source":["Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H6ymeoE0eW7A"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install gradio"]},{"cell_type":"code","source":[],"metadata":{"id":"wmfDraXrm9QC"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PwNTt9OyeW7C"},"outputs":[],"source":["import gradio as gr\n","\n","def greet(name):\n","    return \"Bonjour \" + name\n","\n","demo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\")\n","\n","demo.launch()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0UVZveUCeW7D"},"outputs":[],"source":["import gradio as gr\n","\n","def greet(name):\n","    return \"Bonjour \" + name\n","\n","# Nous instancions la classe Textbox\n","textbox = gr.Textbox(label=\"Tapez votre nom ici :\", placeholder=\"Marie Martin\", lines=2)\n","\n","gr.Interface(fn=greet, inputs=textbox, outputs=\"text\").launch()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g84zO19zeW7E"},"outputs":[],"source":["from transformers import pipeline\n","\n","model = pipeline(\"text-generation\", model=\"asi/gpt-fr-cased-small\")\n","\n","\n","def predict(prompt):\n","    completion = model(prompt)[0][\"generated_text\"]\n","    return completion"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BRkoFlByeW7F"},"outputs":[],"source":["import gradio as gr\n","\n","gr.Interface(fn=predict, inputs=\"text\", outputs=\"text\").launch()"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter9/section3.ipynb b/course/fr/chapter9/section3.ipynb
deleted file mode 100644
index 3fe4a242..00000000
--- a/course/fr/chapter9/section3.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"P5ZMEbzuftx7"},"source":["# Comprendre la classe Interface"]},{"cell_type":"markdown","metadata":{"id":"ydkg_N6Dftx-"},"source":["Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4xgOhY2wftx_"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install gradio"]},{"cell_type":"code","source":[],"metadata":{"id":"isSjXjjVnGlp"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WsetNLYyftyA"},"outputs":[],"source":["import numpy as np\n","import gradio as gr\n","\n","\n","def reverse_audio(audio):\n","    sr, data = audio\n","    reversed_audio = (sr, np.flipud(data))\n","    return reversed_audio\n","\n","\n","mic = gr.Audio(source=\"microphone\", type=\"numpy\", label=\"Parler ici...\")\n","gr.Interface(reverse_audio, mic, \"audio\").launch()"]},{"cell_type":"code","source":[],"metadata":{"id":"TcegG4mFmphV"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"geguvsfpftyB"},"outputs":[],"source":["import numpy as np\n","import gradio as gr\n","\n","notes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n","\n","\n","def generate_tone(note, octave, duration):\n","    sr = 48000\n","    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n","    frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n","    duration = int(duration)\n","    audio = np.linspace(0, duration, duration * sr)\n","    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n","    return (sr, audio)\n","\n","\n","gr.Interface(\n","    generate_tone,\n","    [\n","        gr.Dropdown(notes, type=\"index\"),\n","        gr.Slider(minimum=4, maximum=6, step=1),\n","        gr.Textbox(type=\"number\", value=1, label=\"Durée en secondes\"),\n","    ],\n","    \"audio\",\n",").launch()"]},{"cell_type":"code","source":[],"metadata":{"id":"98IQCJnvmrpS"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AXV8ofmuftyC"},"outputs":[],"source":["from transformers import pipeline\n","import gradio as gr\n","\n","model = pipeline(\"automatic-speech-recognition\",model=\"facebook/wav2vec2-large-xlsr-53-french\")\n","\n","\n","def transcribe_audio(mic=None, file=None):\n","    if mic is not None:\n","        audio = mic\n","    elif file is not None:\n","        audio = file\n","    else:\n","        return \"Vous devez fournir soit un enregistrement micro ou un fichier\"\n","    transcription = model(audio)[\"text\"]\n","    return transcription\n","\n","\n","gr.Interface(\n","    fn=transcribe_audio,\n","    inputs=[\n","        gr.Audio(source=\"microphone\", type=\"filepath\", optional=True),\n","        gr.Audio(source=\"upload\", type=\"filepath\", optional=True),\n","    ],\n","    outputs=\"text\",\n",").launch()"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter9/section4.ipynb b/course/fr/chapter9/section4.ipynb
deleted file mode 100644
index 810b52b0..00000000
--- a/course/fr/chapter9/section4.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"7PJjHfmPfuVA"},"source":["# Partager ses démos avec d'autres"]},{"cell_type":"markdown","metadata":{"id":"jRWYhCTHfuVC"},"source":["Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nhJZ6vw1fuVD"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install gradio"]},{"cell_type":"code","source":[],"metadata":{"id":"nrI9JVsynK2f"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9I2ia0R1fuVE"},"outputs":[],"source":["import gradio as gr\n","\n","title = \"Poser une question (en anglais) à Rick\"\n","description = \"\"\"\n","Le bot a été entraîné à répondre à des questions basées sur les dialogues de Rick et Morty (en anglais). Demandez à Rick ce que vous voulez !\n","<img src=\"https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png\" width=200px>\n","\"\"\"\n","\n","article = \"Consultez [le bot original Rick et Morty](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) sur lequel cette démo est basée.\"\n","\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","import torch\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"ericzhou/DialoGPT-Medium-Rick_v2\")\n","model = AutoModelForCausalLM.from_pretrained(\"ericzhou/DialoGPT-Medium-Rick_v2\")\n","\n","def predict(input, history=[]):\n","    # tokenizer la nouvelle phrase d'entrée\n","    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')\n","\n","    # ajouter les nouveaux tokens d'entrée de l'utilisateur à l'historique de chat\n","    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)\n","\n","    # générer une réponse \n","    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()\n","\n","    # convertit les tokens en texte, puis divise les réponses dans le bon format.\n","    response = tokenizer.decode(history[0]).split(\"<|endoftext|>\")\n","    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convertir en tuples de liste\n","    return response, history\n","\n","gr.Interface(\n","    fn=predict,\n","    inputs=\"textbox\",\n","    outputs=\"text\",\n","    title=title,\n","    description=description,\n","    article=article,\n","    examples=[[\"What are you doing?\"], [\"Where should we time travel to?\"]],\n",").launch()"]},{"cell_type":"code","source":[],"metadata":{"id":"XDfMUCtb6Stg"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-FIvt7difuVG"},"outputs":[],"source":["# Vous devez récupérer le fichier pytorch_model.bin ici https://huggingface.co/spaces/course-demos/Sketch-Recognition/blob/main/pytorch_model.bin\n","\n","import torch\n","import gradio as gr\n","from torch import nn\n","import requests\n","from google.colab import drive\n","drive.mount('/content/MyDrive/pytorch_model.bin')\n","\n","LABELS = requests.get(\"https://huggingface.co/spaces/course-demos/Sketch-Recognition/raw/main/class_names.txt\").text.replace(\"\\n\",\"\").split(\"\\r\")\n","\n","model = nn.Sequential(\n","    nn.Conv2d(1, 32, 3, padding=\"same\"),\n","    nn.ReLU(),\n","    nn.MaxPool2d(2),\n","    nn.Conv2d(32, 64, 3, padding=\"same\"),\n","    nn.ReLU(),\n","    nn.MaxPool2d(2),\n","    nn.Conv2d(64, 128, 3, padding=\"same\"),\n","    nn.ReLU(),\n","    nn.MaxPool2d(2),\n","    nn.Flatten(),\n","    nn.Linear(1152, 256),\n","    nn.ReLU(),\n","    nn.Linear(256, len(LABELS)),\n",")\n","state_dict = torch.load(\"pytorch_model.bin\", map_location=\"cpu\")\n","model.load_state_dict(state_dict, strict=False)\n","model.eval()\n","\n","\n","def predict(im):\n","    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0\n","    with torch.no_grad():\n","        out = model(x)\n","    probabilities = torch.nn.functional.softmax(out[0], dim=0)\n","    values, indices = torch.topk(probabilities, 5)\n","    return {LABELS[i]: v.item() for i, v in zip(indices, values)}\n","\n","\n","interface = gr.Interface(\n","    predict,\n","    inputs=\"sketchpad\",\n","    outputs=\"label\",\n","    theme=\"huggingface\",\n","    title=\"Reconnaissance de croquis\",\n","    description=\"Qui veut jouer au Pictionary ? Dessinez un objet courant comme une pelle ou un ordinateur portable, et l'algorithme le devinera en temps réel !\",\n","    article=\"<p style='text-align: center'>Reconnaissance de croquis | Modèle de démonstration</p>\",\n","    live=True,\n",")\n","interface.launch(share=True)"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter9/section5.ipynb b/course/fr/chapter9/section5.ipynb
deleted file mode 100644
index 06756e04..00000000
--- a/course/fr/chapter9/section5.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"UI11xqgqfuw0"},"source":["# Integrations avec le *Hub* d'Hugging Face"]},{"cell_type":"markdown","metadata":{"id":"7aKFCLqRfuw2"},"source":["Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3KjArAH9fuw3"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install gradio"]},{"cell_type":"code","source":[],"metadata":{"id":"Fjpy7uQEnD45"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IAE0erI2fuw4"},"outputs":[],"source":["import gradio as gr\n","\n","title = \"GPT-J-6B (Boris)\"\n","description = \"Démo Gradio pour GPT-J 6B (Boris), un transformer entraîné à l'aide du Mesh Transformer JAX de Ben Wang. GPT-J' fait référence à la classe du modèle, tandis que '6B' représente le nombre de paramètres entraînables. Pour l'utiliser, il suffit d'ajouter votre texte, ou de cliquer sur l'un des exemples pour le charger. Pour en savoir plus, consultez les liens ci-dessous.\"\n","article = \"<p style='text-align: center'><a href='https://github.com/kingoflolz/mesh-transformer-jax' target='_blank'>GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model</a></p>\"\n","examples = [\n","    [\"La tour mesure 324 mètres (1 063 pieds) de haut,\"],\n","    [\"L'orbite de la Lune autour de la Terre a\"],\n","    [\"Le bassin lisse de Borealis dans l'hémisphère nord couvre 40 %\"],\n","]\n","gr.Interface.load(\n","    # \"huggingface/Cedille/fr-boris\", # C'est un très gros modèle, le temps d'éxécution peut être très long (voire planter) !\n","    \"huggingface/asi/gpt-fr-cased-small\", # Alternative beaucoup plus légère\n","    inputs=gr.Textbox(lines=5, label=\"Texte d'entrée\"),\n","    title=title,\n","    description=description,\n","    article=article,\n","    examples=examples,\n","    enable_queue=True,\n",").launch()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xFCA_kEGfuw5"},"outputs":[],"source":["gr.Interface.load(\"spaces/abidlabs/remove-bg\").launch()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Wa0iGcJyfuw6"},"outputs":[],"source":["gr.Interface.load(\n","    \"spaces/abidlabs/remove-bg\", inputs=\"webcam\", title=\"Supprimez l'arrière-plan de votre webcam !\"\n",").launch()"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/fr/chapter9/section6.ipynb b/course/fr/chapter9/section6.ipynb
deleted file mode 100644
index d1a05a08..00000000
--- a/course/fr/chapter9/section6.ipynb
+++ /dev/null
@@ -1,150 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "dCew_VZqfvff"
-   },
-   "source": [
-    "# Fonctions avancées d'Interface"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hXpTCl18fvfh"
-   },
-   "source": [
-    "Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "X_4rRGIofvfi"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install datasets transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "5qwMwbctnTNP"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "HKxbPoSqfvfk"
-   },
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def chat(message, history):\n",
-    "    history = history or []\n",
-    "    if message.startswith(\"Combien\"):\n",
-    "        response = random.randint(1, 10)\n",
-    "    elif message.startswith(\"Comment\"):\n",
-    "        response = random.choice([\"Super\", \"Bon\", \"Ok\", \"Mal\"])\n",
-    "    elif message.startswith(\"Où\"):\n",
-    "        response = random.choice([\"Ici\", \"Là\", \"Quelque part\"])\n",
-    "    else:\n",
-    "        response = \"Je ne sais pas.\"\n",
-    "    history.append((message, response))\n",
-    "    return history, history\n",
-    "\n",
-    "\n",
-    "iface = gr.Interface(\n",
-    "    chat,\n",
-    "    [\"text\", \"state\"],\n",
-    "    [\"chatbot\", \"state\"],\n",
-    "    allow_screenshot=False,\n",
-    "    allow_flagging=\"never\",\n",
-    ")\n",
-    "iface.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "rNS-z93HnVRk"
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "QSGjnAWpfvfn"
-   },
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "inception_net = tf.keras.applications.MobileNetV2()  # charger le modèle\n",
-    "\n",
-    "# Télécharger des étiquettes lisibles par l'homme pour ImageNet\n",
-    "response = requests.get(\"https://git.io/JJkYN\")\n",
-    "labels = response.text.split(\"\\n\")\n",
-    "\n",
-    "\n",
-    "def classify_image(inp):\n",
-    "    inp = inp.reshape((-1, 224, 224, 3))\n",
-    "    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n",
-    "    prediction = inception_net.predict(inp).flatten()\n",
-    "    return {labels[i]: float(prediction[i]) for i in range(1000)}\n",
-    "\n",
-    "\n",
-    "image = gr.Image(shape=(224, 224))\n",
-    "label = gr.Label(num_top_classes=3)\n",
-    "\n",
-    "title = \"Classification des images avec Gradio + Exemple d'interprétation\"\n",
-    "gr.Interface(\n",
-    "    fn=classify_image, inputs=image, outputs=label, interpretation=\"default\", title=title\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/course/fr/chapter9/section7.ipynb b/course/fr/chapter9/section7.ipynb
deleted file mode 100644
index c037e158..00000000
--- a/course/fr/chapter9/section7.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"xIpOVSo4fv1z"},"source":["# Introduction aux Blocks"]},{"cell_type":"markdown","metadata":{"id":"p_ey-sezfv11"},"source":["Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rocsIxYUfv12"},"outputs":[],"source":["!pip install datasets transformers[sentencepiece]\n","!pip install gradio"]},{"cell_type":"code","source":[],"metadata":{"id":"bQWSzLO8neEY"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rBauc72Gfv13"},"outputs":[],"source":["import gradio as gr\n","\n","def flip_text(x):\n","    return x[::-1]\n","\n","\n","demo = gr.Blocks()\n","\n","with demo:\n","    gr.Markdown(\n","        \"\"\"\n","        # Inverser le texte !\n","        Commencer à taper ci-dessous pour voir le résultat.\n","        \"\"\"\n","    )\n","    input = gr.Textbox(placeholder=\"Inverser ce texte\")\n","    output = gr.Textbox()\n","\n","    input.change(fn=flip_text, inputs=input, outputs=output)\n","\n","demo.launch()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TUXbQghefv14"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TH5obfLQfv15"},"outputs":[],"source":["import numpy as np\n","import gradio as gr\n","\n","demo = gr.Blocks()\n","\n","\n","def flip_text(x):\n","    return x[::-1]\n","\n","\n","def flip_image(x):\n","    return np.fliplr(x)\n","\n","\n","with demo:\n","    gr.Markdown(\"Inverser des fichiers texte ou image à l'aide de cette démo.\")\n","    with gr.Tabs():\n","        with gr.TabItem(\"Inverser le texte\"):\n","            with gr.Row():\n","                text_input = gr.Textbox()\n","                text_output = gr.Textbox()\n","            text_button = gr.Button(\"Inverser\")\n","        with gr.TabItem(\"Inverser l'image\"):\n","            with gr.Row():\n","                image_input = gr.Image()\n","                image_output = gr.Image()\n","            image_button = gr.Button(\"Inverser\")\n","\n","    text_button.click(flip_text, inputs=text_input, outputs=text_output)\n","    image_button.click(flip_image, inputs=image_input, outputs=image_output)\n","\n","demo.launch()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PbQTopkxfv16"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xdKEdom4fv17"},"outputs":[],"source":["import gradio as gr\n","\n","api = gr.Interface.load(\"huggingface/asi/gpt-fr-cased-small\")\n","\n","\n","def complete_with_gpt(text):\n","    # Utilisez les 50 derniers caractères du texte comme contexte.\n","    return text[:-50] + api(text[-50:])\n","\n","\n","with gr.Blocks() as demo:\n","    textbox = gr.Textbox(placeholder=\"Tapez ici et appuyez sur la touche Entrée...\", lines=4)\n","    btn = gr.Button(\"Générer\")\n","\n","    btn.click(complete_with_gpt, textbox, textbox)\n","\n","demo.launch()"]},{"cell_type":"code","source":[],"metadata":{"id":"ixjAPUVongMD"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zt-mHCwEfv18"},"outputs":[],"source":["from transformers import pipeline\n","\n","import gradio as gr\n","\n","asr = pipeline(\"automatic-speech-recognition\", \"wav2vec2-large-xlsr-53-french\")\n","classifier = pipeline(\"text-classification\", model=\"tblard/tf-allocine\")\n","\n","\n","def speech_to_text(speech):\n","    text = asr(speech)[\"text\"]\n","    return text\n","\n","\n","def text_to_sentiment(text):\n","    return classifier(text)[0][\"label\"]\n","\n","\n","demo = gr.Blocks()\n","\n","with demo:\n","    audio_file = gr.Audio(type=\"filepath\")\n","    text = gr.Textbox()\n","    label = gr.Label()\n","\n","    b1 = gr.Button(\"Reconnaître la parole\")\n","    b2 = gr.Button(\"Classifier le sentiment\")\n","\n","    b1.click(speech_to_text, inputs=audio_file, outputs=text)\n","    b2.click(text_to_sentiment, inputs=text, outputs=label)\n","\n","demo.launch()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DU4Nw6_tfv19"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b2QkNDCNfv19"},"outputs":[],"source":["import gradio as gr\n","\n","\n","def change_textbox(choice):\n","    if choice == \"court\":\n","        return gr.Textbox.update(lines=2, visible=True)\n","    elif choice == \"long\":\n","        return gr.Textbox.update(lines=8, visible=True)\n","    else:\n","        return gr.Textbox.update(visible=False)\n","\n","\n","with gr.Blocks() as block:\n","    radio = gr.Radio(\n","        [\"court\", \"long\", \"none\"], label=\"Quel genre d'essai souhaitez-vous écrire ?\"\n","    )\n","    text = gr.Textbox(lines=2, interactive=True)\n","\n","    radio.change(fn=change_textbox, inputs=radio, outputs=text)\n","    block.launch()"]}],"metadata":{"colab":{"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/course/hi/chapter1/section10.ipynb b/course/hi/chapter1/section10.ipynb
deleted file mode 100644
index 3ff8dda2..00000000
--- a/course/hi/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# अध्याय के अंत की प्रश्नोत्तरी"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "अध्याय के अंत की प्रश्नोत्तरी",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/hi/chapter1/section3.ipynb b/course/hi/chapter1/section3.ipynb
deleted file mode 100644
index 240dc6d6..00000000
--- a/course/hi/chapter1/section3.ipynb
+++ /dev/null
@@ -1,350 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ट्रांसफार्मर, वे क्या कर सकते हैं?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "ट्रांसफार्मर, वे क्या कर सकते हैं?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/hi/chapter1/section8.ipynb b/course/hi/chapter1/section8.ipynb
deleted file mode 100644
index d87a9f8c..00000000
--- a/course/hi/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# पूर्वाग्रह और सीमाएं"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "पूर्वाग्रह और सीमाएं",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/hi/chapter3/section2_pt.ipynb b/course/hi/chapter3/section2_pt.ipynb
deleted file mode 100644
index 834cf718..00000000
--- a/course/hi/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# डेटा संसाधित करना (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# This is new\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "डेटा संसाधित करना (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/hi/chapter3/section2_tf.ipynb b/course/hi/chapter3/section2_tf.ipynb
deleted file mode 100644
index b5a98eb6..00000000
--- a/course/hi/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# डेटा संसाधित करना (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "डेटा संसाधित करना (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/hi/chapter3/section3.ipynb b/course/hi/chapter3/section3.ipynb
deleted file mode 100644
index b3d97834..00000000
--- a/course/hi/chapter3/section3.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# मॉडल कि फाइन-ट्यूनिंग Trainer API या Keras के साथ"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "मॉडल कि फाइन-ट्यूनिंग Trainer API या Keras के साथ",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/hi/chapter3/section3_tf.ipynb b/course/hi/chapter3/section3_tf.ipynb
deleted file mode 100644
index 5f567d97..00000000
--- a/course/hi/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# मॉडल कि फाइन-ट्यूनिंग Trainer API या Keras के साथ"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "मॉडल कि फाइन-ट्यूनिंग Trainer API या Keras के साथ",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/hi/chapter3/section4.ipynb b/course/hi/chapter3/section4.ipynb
deleted file mode 100644
index 13edfc91..00000000
--- a/course/hi/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# एक पूर्ण प्रशिक्षण"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "एक पूर्ण प्रशिक्षण",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter1/section10.ipynb b/course/it/chapter1/section10.ipynb
deleted file mode 100644
index 808b6c69..00000000
--- a/course/it/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz di fine capitolo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz di fine capitolo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter1/section3.ipynb b/course/it/chapter1/section3.ipynb
deleted file mode 100644
index 6247e030..00000000
--- a/course/it/chapter1/section3.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cosa fanno i Transformer?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Cosa fanno i Transformer?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter1/section8.ipynb b/course/it/chapter1/section8.ipynb
deleted file mode 100644
index aa453b7c..00000000
--- a/course/it/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Bias e limiti"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Bias e limiti",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section2_pt.ipynb b/course/it/chapter2/section2_pt.ipynb
deleted file mode 100644
index f995a7ed..00000000
--- a/course/it/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dietro la pipeline (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Dietro la pipeline (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section2_tf.ipynb b/course/it/chapter2/section2_tf.ipynb
deleted file mode 100644
index 49c94720..00000000
--- a/course/it/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dietro la pipeline (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Dietro la pipeline (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section3_pt.ipynb b/course/it/chapter2/section3_pt.ipynb
deleted file mode 100644
index 7de7ef8e..00000000
--- a/course/it/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modelli (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Creazione della configurazione\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Creare il modello dalla configurazione\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# Il modello è inizializzato in modo casuale!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Modelli (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section3_tf.ipynb b/course/it/chapter2/section3_tf.ipynb
deleted file mode 100644
index 53fcc9f4..00000000
--- a/course/it/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modelli (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Creazione della configurazione\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Creare il modello dalla configurazione\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# Il modello è inizializzato in modo casuale!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Modelli (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section4_pt.ipynb b/course/it/chapter2/section4_pt.ipynb
deleted file mode 100644
index fadf2b34..00000000
--- a/course/it/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section4_tf.ipynb b/course/it/chapter2/section4_tf.ipynb
deleted file mode 100644
index 15343d71..00000000
--- a/course/it/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section5_pt.ipynb b/course/it/chapter2/section5_pt.ipynb
deleted file mode 100644
index 6b2548db..00000000
--- a/course/it/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gestione di sequenze multiple (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Gestione di sequenze multiple (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section5_tf.ipynb b/course/it/chapter2/section5_tf.ipynb
deleted file mode 100644
index 02b25540..00000000
--- a/course/it/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gestione di sequenze multiple (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Gestione di sequenze multiple (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section6_pt.ipynb b/course/it/chapter2/section6_pt.ipynb
deleted file mode 100644
index 184f424c..00000000
--- a/course/it/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Mettiamo insieme i pezzi (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Effettua il padding della sequenza fino allla massima lunghezza della sequenza\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Effettua il padding fino alla lunghezza massima del modello\n",
-    "# (512 per BERT o DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Effettua il padding fino alla lunghezza massima specificata\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Tronca le sequenze più lunghe della lunghezza massima del modello.\n",
-    "# (512 per BERT o DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Tronca le sequenze più lunghe della lunghezza massima specificata.\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Ritorna tensori PyTorch\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Ritorna tensori TensorFlow\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Ritorna NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Mettiamo insieme i pezzi (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter2/section6_tf.ipynb b/course/it/chapter2/section6_tf.ipynb
deleted file mode 100644
index 662e2428..00000000
--- a/course/it/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Mettiamo insieme i pezzi (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Effettua il padding della sequenza fino allla massima lunghezza della sequenza\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Effettua il padding fino alla lunghezza massima del modello\n",
-    "# (512 per BERT o DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Effettua il padding fino alla lunghezza massima specificata\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Tronca le sequenze più lunghe della lunghezza massima del modello.\n",
-    "# (512 per BERT o DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Tronca le sequenze più lunghe della lunghezza massima specificata.\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Ritorna tensori PyTorch\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Ritorna tensori TensorFlow\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Ritorna NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Mettiamo insieme i pezzi (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter3/section2_pt.ipynb b/course/it/chapter3/section2_pt.ipynb
deleted file mode 100644
index 1becaa62..00000000
--- a/course/it/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Processare i dati (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# This is new\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Processare i dati (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter3/section2_tf.ipynb b/course/it/chapter3/section2_tf.ipynb
deleted file mode 100644
index 08ed311b..00000000
--- a/course/it/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Processare i dati (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Processare i dati (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter3/section3.ipynb b/course/it/chapter3/section3.ipynb
deleted file mode 100644
index 0bfe92f9..00000000
--- a/course/it/chapter3/section3.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Affinare il modello con la Trainer API"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = load_metric(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Affinare il modello con la Trainer API",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter3/section3_tf.ipynb b/course/it/chapter3/section3_tf.ipynb
deleted file mode 100644
index 4e24a909..00000000
--- a/course/it/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Affinare il modello con la Trainer API"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Affinare il modello con la Trainer API",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter3/section4.ipynb b/course/it/chapter3/section4.ipynb
deleted file mode 100644
index 8ebbad4a..00000000
--- a/course/it/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Un addestramento completo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Un addestramento completo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter4/section2_pt.ipynb b/course/it/chapter4/section2_pt.ipynb
deleted file mode 100644
index d49889dc..00000000
--- a/course/it/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Usare modelli pre-addestrati (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Usare modelli pre-addestrati (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter4/section2_tf.ipynb b/course/it/chapter4/section2_tf.ipynb
deleted file mode 100644
index a184ca38..00000000
--- a/course/it/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Usare modelli pre-addestrati (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Usare modelli pre-addestrati (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter4/section3_pt.ipynb b/course/it/chapter4/section3_pt.ipynb
deleted file mode 100644
index f54caf62..00000000
--- a/course/it/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Condividere modelli pre-addestrati (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository creation and management\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # And some methods to retrieve/change information about the content\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Condividere modelli pre-addestrati (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter4/section3_tf.ipynb b/course/it/chapter4/section3_tf.ipynb
deleted file mode 100644
index 22cb4983..00000000
--- a/course/it/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Condividere modelli pre-addestrati (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository creation and management\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # And some methods to retrieve/change information about the content\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Condividere modelli pre-addestrati (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter5/section2.ipynb b/course/it/chapter5/section2.ipynb
deleted file mode 100644
index 289eeabc..00000000
--- a/course/it/chapter5/section2.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# E se il mio dataset non è sull'Hub?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz\n",
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gzip -dkv SQuAD_it-*.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=\"SQuAD_it-train.json\", field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    \"title\": \"Terremoto del Sichuan del 2008\",\n",
-       "    \"paragraphs\": [\n",
-       "        {\n",
-       "            \"context\": \"Il terremoto del Sichuan del 2008 o il terremoto...\",\n",
-       "            \"qas\": [\n",
-       "                {\n",
-       "                    \"answers\": [{\"answer_start\": 29, \"text\": \"2008\"}],\n",
-       "                    \"id\": \"56cdca7862d2951400fa6826\",\n",
-       "                    \"question\": \"In quale anno si è verificato il terremoto nel Sichuan?\",\n",
-       "                },\n",
-       "                ...\n",
-       "            ],\n",
-       "        },\n",
-       "        ...\n",
-       "    ],\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 48\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json\", \"test\": \"SQuAD_it-test.json\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")\n",
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json.gz\", \"test\": \"SQuAD_it-test.json.gz\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/crux82/squad-it/raw/master/\"\n",
-    "data_files = {\n",
-    "    \"train\": url + \"SQuAD_it-train.json.gz\",\n",
-    "    \"test\": url + \"SQuAD_it-test.json.gz\",\n",
-    "}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "E se il mio dataset non è sull'Hub?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter5/section3.ipynb b/course/it/chapter5/section3.ipynb
deleted file mode 100644
index fef66c12..00000000
--- a/course/it/chapter5/section3.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# È arrivato il momento di tagliuzzare"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
-    "!unzip drugsCom_raw.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
-    "# \\t rappresenta il tabulatore in Python\n",
-    "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Unnamed: 0': [87571, 178045, 80482],\n",
-       " 'drugName': ['Naproxen', 'Duloxetine', 'Mobic'],\n",
-       " 'condition': ['Gout, Acute', 'ibromyalgia', 'Inflammatory Conditions'],\n",
-       " 'review': ['\"like the previous person mention, I&#039;m a strong believer of aleve, it works faster for my gout than the prescription meds I take. No more going to the doctor for refills.....Aleve works!\"',\n",
-       "  '\"I have taken Cymbalta for about a year and a half for fibromyalgia pain. It is great\\r\\nas a pain reducer and an anti-depressant, however, the side effects outweighed \\r\\nany benefit I got from it. I had trouble with restlessness, being tired constantly,\\r\\ndizziness, dry mouth, numbness and tingling in my feet, and horrible sweating. I am\\r\\nbeing weaned off of it now. Went from 60 mg to 30mg and now to 15 mg. I will be\\r\\noff completely in about a week. The fibro pain is coming back, but I would rather deal with it than the side effects.\"',\n",
-       "  '\"I have been taking Mobic for over a year with no side effects other than an elevated blood pressure.  I had severe knee and ankle pain which completely went away after taking Mobic.  I attempted to stop the medication however pain returned after a few days.\"'],\n",
-       " 'rating': [9.0, 3.0, 10.0],\n",
-       " 'date': ['September 2, 2015', 'November 7, 2011', 'June 5, 2013'],\n",
-       " 'usefulCount': [36, 13, 128]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
-    "# Diamo un'occhiata ai primi esempi\n",
-    "drug_sample[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split in drug_dataset.keys():\n",
-    "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 161297\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 53766\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.rename_column(\n",
-    "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
-    ")\n",
-    "drug_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AttributeError: 'NoneType' object has no attribute 'lower'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def lowercase_condition(example):\n",
-    "    return {\"condition\": example[\"condition\"].lower()}\n",
-    "\n",
-    "\n",
-    "drug_dataset.map(lowercase_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_nones(x):\n",
-    "    return x[\"condition\"] is not None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x * x)(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda base, altezza: 0.5 * base * altezza)(4, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['left ventricular dysfunction', 'adhd', 'birth control']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(lowercase_condition)\n",
-    "# Check that lowercasing worked\n",
-    "drug_dataset[\"train\"][\"condition\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_review_length(example):\n",
-    "    return {\"review_length\": len(example[\"review\"].split())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': 206461,\n",
-       " 'drugName': 'Valsartan',\n",
-       " 'condition': 'left ventricular dysfunction',\n",
-       " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
-       " 'rating': 9.0,\n",
-       " 'date': 'May 20, 2012',\n",
-       " 'usefulCount': 27,\n",
-       " 'review_length': 17}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(compute_review_length)\n",
-    "# Inspect the first training example\n",
-    "drug_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': [103488, 23627, 20558],\n",
-       " 'drugName': ['Loestrin 21 1 / 20', 'Chlorzoxazone', 'Nucynta'],\n",
-       " 'condition': ['birth control', 'muscle spasm', 'pain'],\n",
-       " 'review': ['\"Excellent.\"', '\"useless\"', '\"ok\"'],\n",
-       " 'rating': [10.0, 1.0, 6.0],\n",
-       " 'date': ['November 4, 2008', 'March 24, 2017', 'August 20, 2016'],\n",
-       " 'usefulCount': [5, 2, 10],\n",
-       " 'review_length': [1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'train': 138514, 'test': 46108}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
-    "print(drug_dataset.num_rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm a transformer called BERT\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import html\n",
-    "\n",
-    "text = \"I&#039;m a transformer called BERT\"\n",
-    "html.unescape(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_drug_dataset = drug_dataset.map(\n",
-    "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "\n",
-    "def tokenize_function(examples):\n",
-    "    return tokenizer(examples[\"review\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%time tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
-    "\n",
-    "\n",
-    "def slow_tokenize_function(examples):\n",
-    "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    return tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[128, 49]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
-    "[len(inp) for inp in result[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ArrowInvalid: Column 1 named condition expected length 1463 but got length 1000"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(\n",
-    "    tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(206772, 138514)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    result = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    # Estraiamo la mappatura tra gli indici vecchi e quelli nuovi\n",
-    "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
-    "    for key, values in examples.items():\n",
-    "        result[key] = [values[i] for i in sample_map]\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 206772\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 68876\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.set_format(\"pandas\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df = drug_dataset[\"train\"][:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frequencies = (\n",
-    "    train_df[\"condition\"]\n",
-    "    .value_counts()\n",
-    "    .to_frame()\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
-    ")\n",
-    "frequencies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['condition', 'frequency'],\n",
-       "    num_rows: 819\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "freq_dataset = Dataset.from_pandas(frequencies)\n",
-    "freq_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
-    "# Rinominare la sezione di \"test\" in \"validazione\"\n",
-    "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
-    "# Aggiungere il set \"test\" al nostor `DatasetDict`\n",
-    "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
-    "drug_dataset_clean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_from_disk\n",
-    "\n",
-    "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
-    "drug_dataset_reloaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split, dataset in drug_dataset_clean.items():\n",
-    "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\"patient_id\":141780,\"drugName\":\"Escitalopram\",\"condition\":\"depression\",\"review\":\"\\\"I seemed to experience the regular side effects of LEXAPRO, insomnia, low sex drive, sleepiness during the day. I am taking it at night because my doctor said if it made me tired to take it at night. I assumed it would and started out taking it at night. Strange dreams, some pleasant. I was diagnosed with fibromyalgia. Seems to be helping with the pain. Have had anxiety and depression in my family, and have tried quite a few other medications that haven't worked. Only have been on it for two weeks but feel more positive in my mind, want to accomplish more in my life. Hopefully the side effects will dwindle away, worth it to stick with it from hearing others responses. Great medication.\\\"\",\"rating\":9.0,\"date\":\"May 29, 2011\",\"usefulCount\":10,\"review_length\":125}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "!head -n 1 drug-reviews-train.jsonl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\n",
-    "    \"train\": \"drug-reviews-train.jsonl\",\n",
-    "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
-    "    \"test\": \"drug-reviews-test.jsonl\",\n",
-    "}\n",
-    "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "È arrivato il momento di tagliuzzare",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter5/section4.ipynb b/course/it/chapter5/section4.ipynb
deleted file mode 100644
index 7006a6b5..00000000
--- a/course/it/chapter5/section4.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Big data? Ci pensa 🤗 Datasets!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install zstandard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['meta', 'text'],\n",
-       "    num_rows: 15518009\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Ci vuole qualche minuto per l'esecuzione, quindi preparati un tè o un caffè nell'attesa :)\n",
-    "data_files = \"https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\n",
-    "pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "pubmed_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pubmed_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install psutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RAM used: 5678.33 MB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# Process.memory_info mostra i dati in byte, quindi convertiamo in megabyte\n",
-    "print(f\"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of files in dataset : 20979437051\n",
-       "Dataset size (cache file) : 19.54 GB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(f\"Number of files in dataset : {pubmed_dataset.dataset_size}\")\n",
-    "size_gb = pubmed_dataset.dataset_size / (1024**3)\n",
-    "print(f\"Dataset size (cache file) : {size_gb:.2f} GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Iterated over 15518009 examples (about 19.5 GB) in 64.2s, i.e. 0.304 GB/s'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import timeit\n",
-    "\n",
-    "code_snippet = \"\"\"batch_size = 1000\n",
-    "\n",
-    "for idx in range(0, len(pubmed_dataset), batch_size):\n",
-    "    _ = pubmed_dataset[idx:idx + batch_size]\n",
-    "\"\"\"\n",
-    "\n",
-    "time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())\n",
-    "print(\n",
-    "    f\"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in \"\n",
-    "    f\"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset_streamed = load_dataset(\n",
-    "    \"json\", data_files=data_files, split=\"train\", streaming=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iter(pubmed_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 4958, 5178, 4328, 6779, ...], 'attention_mask': [1, 1, 1, 1, 1, ...]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
-    "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"text\"]))\n",
-    "next(iter(tokenized_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11410799, 'language': 'eng'},\n",
-       " 'text': 'Randomized study of dose or schedule modification of granulocyte colony-stimulating factor in platinum-based chemotherapy for elderly patients with lung cancer ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)\n",
-    "next(iter(shuffled_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'pmid': 11409575, 'language': 'eng'},\n",
-       "  'text': 'Clinical signs of hypoxaemia in children with acute lower respiratory infection: indicators of oxygen therapy ...'},\n",
-       " {'meta': {'pmid': 11409576, 'language': 'eng'},\n",
-       "  'text': \"Hypoxaemia in children with severe pneumonia in Papua New Guinea ...\"},\n",
-       " {'meta': {'pmid': 11409577, 'language': 'eng'},\n",
-       "  'text': 'Oxygen concentrators and cylinders ...'},\n",
-       " {'meta': {'pmid': 11409578, 'language': 'eng'},\n",
-       "  'text': 'Oxygen supply in rural africa: a personal experience ...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_head = pubmed_dataset_streamed.take(5)\n",
-    "list(dataset_head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Salta i primi 1.000 esempi, il resto viene incluso nell'insieme di addestramento\n",
-    "train_dataset = shuffled_dataset.skip(1000)\n",
-    "# Includi i primi 1.000 esempi nell'insieme di validazione\n",
-    "validation_dataset = shuffled_dataset.take(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'case_ID': '110921.json',\n",
-       "  'case_jurisdiction': 'scotus.tar.gz',\n",
-       "  'date_created': '2010-04-28T17:12:49Z'},\n",
-       " 'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "law_dataset_streamed = load_dataset(\n",
-    "    \"json\",\n",
-    "    data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\n",
-    "    split=\"train\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "next(iter(law_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'case_ID': '110921.json',\n",
-       "   'case_jurisdiction': 'scotus.tar.gz',\n",
-       "   'date_created': '2010-04-28T17:12:49Z'},\n",
-       "  'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from itertools import islice\n",
-    "from datasets import interleave_datasets\n",
-    "\n",
-    "combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])\n",
-    "list(islice(combined_dataset, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pile_set_name': 'Pile-CC'},\n",
-       " 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_url = \"https://the-eye.eu/public/AI/pile/\"\n",
-    "data_files = {\n",
-    "    \"train\": [base_url + \"train/\" + f\"{idx:02d}.jsonl.zst\" for idx in range(30)],\n",
-    "    \"validation\": base_url + \"val.jsonl.zst\",\n",
-    "    \"test\": base_url + \"test.jsonl.zst\",\n",
-    "}\n",
-    "pile_dataset = load_dataset(\"json\", data_files=data_files, streaming=True)\n",
-    "next(iter(pile_dataset[\"train\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Big data? Ci pensa 🤗 Datasets!",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter5/section5.ipynb b/course/it/chapter5/section5.ipynb
deleted file mode 100644
index 95be9569..00000000
--- a/course/it/chapter5/section5.ipynb
+++ /dev/null
@@ -1,524 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Creare il proprio dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install requests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n",
-    "response = requests.get(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.status_code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'repository_url': 'https://api.github.com/repos/huggingface/datasets',\n",
-       "  'labels_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/labels{/name}',\n",
-       "  'comments_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/comments',\n",
-       "  'events_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/events',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "  'id': 968650274,\n",
-       "  'node_id': 'MDExOlB1bGxSZXF1ZXN0NzEwNzUyMjc0',\n",
-       "  'number': 2792,\n",
-       "  'title': 'Update GooAQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'labels': [],\n",
-       "  'state': 'open',\n",
-       "  'locked': False,\n",
-       "  'assignee': None,\n",
-       "  'assignees': [],\n",
-       "  'milestone': None,\n",
-       "  'comments': 1,\n",
-       "  'created_at': '2021-08-12T11:40:18Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'closed_at': None,\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'active_lock_reason': None,\n",
-       "  'pull_request': {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/2792',\n",
-       "   'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "   'diff_url': 'https://github.com/huggingface/datasets/pull/2792.diff',\n",
-       "   'patch_url': 'https://github.com/huggingface/datasets/pull/2792.patch'},\n",
-       "  'body': '[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.',\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "GITHUB_TOKEN = xxx  # inserisci qui il tuo token GitHub\n",
-    "headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import math\n",
-    "from pathlib import Path\n",
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "\n",
-    "def fetch_issues(\n",
-    "    owner=\"huggingface\",\n",
-    "    repo=\"datasets\",\n",
-    "    num_issues=10_000,\n",
-    "    rate_limit=5_000,\n",
-    "    issues_path=Path(\".\"),\n",
-    "):\n",
-    "    if not issues_path.is_dir():\n",
-    "        issues_path.mkdir(exist_ok=True)\n",
-    "\n",
-    "    batch = []\n",
-    "    all_issues = []\n",
-    "    per_page = 100  # Numero di issue da restituire per pagina\n",
-    "    num_pages = math.ceil(num_issues / per_page)\n",
-    "    base_url = \"https://api.github.com/repos\"\n",
-    "\n",
-    "    for page in tqdm(range(num_pages)):\n",
-    "        # La query ha state=all per ottenere sia gli issue aperti che quelli chiusi\n",
-    "        query = f\"issues?page={page}&per_page={per_page}&state=all\"\n",
-    "        issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n",
-    "        batch.extend(issues.json())\n",
-    "\n",
-    "        if len(batch) > rate_limit and len(all_issues) < num_issues:\n",
-    "            all_issues.extend(batch)\n",
-    "            batch = []  # puliamo la batch per il termine successivo\n",
-    "            print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n",
-    "            time.sleep(60 * 60 + 1)\n",
-    "\n",
-    "    all_issues.extend(batch)\n",
-    "    df = pd.DataFrame.from_records(all_issues)\n",
-    "    df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n",
-    "    print(\n",
-    "        f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A seconda della tua connessione internet, ci potrebbe volere qualche secondo...\n",
-    "fetch_issues()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'timeline_url', 'performed_via_github_app'],\n",
-       "    num_rows: 3019\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = load_dataset(\"json\", data_files=\"datasets-issues.jsonl\", split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">> URL: https://github.com/huggingface/datasets/pull/850\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/850', 'html_url': 'https://github.com/huggingface/datasets/pull/850', 'diff_url': 'https://github.com/huggingface/datasets/pull/850.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/850.patch'}\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/issues/2773\n",
-       ">> Pull request: None\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/pull/783\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/783', 'html_url': 'https://github.com/huggingface/datasets/pull/783', 'diff_url': 'https://github.com/huggingface/datasets/pull/783.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/783.patch'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = issues_dataset.shuffle(seed=666).select(range(3))\n",
-    "\n",
-    "# Stampiamo le entrate `URL` e `pull_request`\n",
-    "for url, pr in zip(sample[\"html_url\"], sample[\"pull_request\"]):\n",
-    "    print(f\">> URL: {url}\")\n",
-    "    print(f\">> Pull request: {pr}\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"is_pull_request\": False if x[\"pull_request\"] is None else True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792#issuecomment-897594128',\n",
-       "  'issue_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'id': 897594128,\n",
-       "  'node_id': 'IC_kwDODunzps41gDMQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'created_at': '2021-08-12T12:21:52Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'body': \"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issue_number = 2792\n",
-    "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "response = requests.get(url, headers=headers)\n",
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\"]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def get_comments(issue_number):\n",
-    "    url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "    response = requests.get(url, headers=headers)\n",
-    "    return [r[\"body\"] for r in response.json()]\n",
-    "\n",
-    "\n",
-    "# Testiamo la nostra funzione\n",
-    "get_comments(2792)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A seconda della tua connessione, potrebbe volerci qualche secondo...\n",
-    "issues_with_comments_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"comments\": get_comments(x[\"number\"])}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_with_comments_dataset.to_json(\"issues-datasets-with-comments.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of datasets on Hub: 1487\n",
-       "Dataset Name: acronym_identification, Tags: ['annotations_creators:expert-generated', 'language_creators:found', 'languages:en', 'licenses:mit', 'multilinguality:monolingual', 'size_categories:10K<n<100K', 'source_datasets:original', 'task_categories:structure-prediction', 'task_ids:structure-prediction-other-acronym-identification']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_datasets\n",
-    "\n",
-    "all_datasets = list_datasets()\n",
-    "print(f\"Number of datasets on Hub: {len(all_datasets)}\")\n",
-    "print(all_datasets[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/datasets/lewtun/github-issues'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "repo_url = create_repo(name=\"github-issues\", repo_type=\"dataset\")\n",
-    "repo_url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)\n",
-    "!cp issues-datasets-with-comments.jsonl github-issues/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.lfs_track(\"*.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remote_dataset = load_dataset(\"lewtun/github-issues\", split=\"train\")\n",
-    "remote_dataset"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Creare il proprio dataset",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter5/section6_pt.ipynb b/course/it/chapter5/section6_pt.ipynb
deleted file mode 100644
index eedd0d0d..00000000
--- a/course/it/chapter5/section6_pt.ipynb
+++ /dev/null
@@ -1,518 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Ricerca semantica con FAISS (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = AutoModel.from_pretrained(model_ckpt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
-    "    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).detach().cpu().numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Ricerca semantica con FAISS (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter5/section6_tf.ipynb b/course/it/chapter5/section6_tf.ipynb
deleted file mode 100644
index ca7b4d73..00000000
--- a/course/it/chapter5/section6_tf.ipynb
+++ /dev/null
@@ -1,506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Ricerca semantica con FAISS (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
-    "    )\n",
-    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Ricerca semantica con FAISS (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter5/section8.ipynb b/course/it/chapter5/section8.ipynb
deleted file mode 100644
index 3402cd24..00000000
--- a/course/it/chapter5/section8.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz di fine capitolo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"allocine\", streaming=True, split=\"train\")\n",
-    "dataset[0]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz di fine capitolo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter8/section2.ipynb b/course/it/chapter8/section2.ipynb
deleted file mode 100644
index 3c120623..00000000
--- a/course/it/chapter8/section2.ipynb
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cosa fare quando si riceve un errore"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Clone the repo and extract the local path\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Create an empty repo on the Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Clone the empty repo\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Copy files\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Push to Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.gitattributes', 'README.md', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer_config.json', 'training_args.bin', 'vocab.txt']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.38669535517692566,\n",
-       " 'start': 34,\n",
-       " 'end': 95,\n",
-       " 'answer': 'the task of extracting an answer from a text given a question'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "---------------------------------------------------------------------------\n",
-       "AttributeError                            Traceback (most recent call last)\n",
-       "/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in <module>\n",
-       "      1 inputs = tokenizer(question, text, add_special_tokens=True)\n",
-       "      2 input_ids = inputs[\"input_ids\"]\n",
-       "----> 3 outputs = model(**inputs)\n",
-       "      4 answer_start_scores = outputs.start_logits\n",
-       "      5 answer_end_scores = outputs.end_logits\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\n",
-       "    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
-       "    724\n",
-       "--> 725         distilbert_output = self.distilbert(\n",
-       "    726             input_ids=input_ids,\n",
-       "    727             attention_mask=attention_mask,\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\n",
-       "    471             raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n",
-       "    472         elif input_ids is not None:\n",
-       "--> 473             input_shape = input_ids.size()\n",
-       "    474         elif inputs_embeds is not None:\n",
-       "    475             input_shape = inputs_embeds.size()[:-1]\n",
-       "\n",
-       "AttributeError: 'list' object has no attribute 'size'\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Get the most likely beginning of answer with the argmax of the score\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Get the most likely end of answer with the argmax of the score\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 2029, 7705, 2015, 2064]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Cosa fare quando si riceve un errore",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter8/section3.ipynb b/course/it/chapter8/section3.ipynb
deleted file mode 100644
index fd947fd5..00000000
--- a/course/it/chapter8/section3.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chiedere aiuto sui forum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModel.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"\"\"\n",
-    "Generation One is a retroactive term for the Transformers characters that\n",
-    "appeared between 1984 and 1993. The Transformers began with the 1980s Japanese\n",
-    "toy lines Micro Change and Diaclone. They presented robots able to transform\n",
-    "into everyday vehicles, electronic items or weapons. Hasbro bought the Micro\n",
-    "Change and Diaclone toys, and partnered with Takara. Marvel Comics was hired by\n",
-    "Hasbro to create the backstory; editor-in-chief Jim Shooter wrote an overall\n",
-    "story, and gave the task of creating the characthers to writer Dennis O'Neil.\n",
-    "Unhappy with O'Neil's work (although O'Neil created the name \"Optimus Prime\"),\n",
-    "Shooter chose Bob Budiansky to create the characters.\n",
-    "\n",
-    "The Transformers mecha were largely designed by Shōji Kawamori, the creator of\n",
-    "the Japanese mecha anime franchise Macross (which was adapted into the Robotech\n",
-    "franchise in North America). Kawamori came up with the idea of transforming\n",
-    "mechs while working on the Diaclone and Macross franchises in the early 1980s\n",
-    "(such as the VF-1 Valkyrie in Macross and Robotech), with his Diaclone mechs\n",
-    "later providing the basis for Transformers.\n",
-    "\n",
-    "The primary concept of Generation One is that the heroic Optimus Prime, the\n",
-    "villainous Megatron, and their finest soldiers crash land on pre-historic Earth\n",
-    "in the Ark and the Nemesis before awakening in 1985, Cybertron hurtling through\n",
-    "the Neutral zone as an effect of the war. The Marvel comic was originally part\n",
-    "of the main Marvel Universe, with appearances from Spider-Man and Nick Fury,\n",
-    "plus some cameos, as well as a visit to the Savage Land.\n",
-    "\n",
-    "The Transformers TV series began around the same time. Produced by Sunbow\n",
-    "Productions and Marvel Productions, later Hasbro Productions, from the start it\n",
-    "contradicted Budiansky's backstories. The TV series shows the Autobots looking\n",
-    "for new energy sources, and crash landing as the Decepticons attack. Marvel\n",
-    "interpreted the Autobots as destroying a rogue asteroid approaching Cybertron.\n",
-    "Shockwave is loyal to Megatron in the TV series, keeping Cybertron in a\n",
-    "stalemate during his absence, but in the comic book he attempts to take command\n",
-    "of the Decepticons. The TV series would also differ wildly from the origins\n",
-    "Budiansky had created for the Dinobots, the Decepticon turned Autobot Jetfire\n",
-    "(known as Skyfire on TV), the Constructicons (who combine to form\n",
-    "Devastator),[19][20] and Omega Supreme. The Marvel comic establishes early on\n",
-    "that Prime wields the Creation Matrix, which gives life to machines. In the\n",
-    "second season, the two-part episode The Key to Vector Sigma introduced the\n",
-    "ancient Vector Sigma computer, which served the same original purpose as the\n",
-    "Creation Matrix (giving life to Transformers), and its guardian Alpha Trion.\n",
-    "\"\"\"\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "logits = model(**inputs).logits"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Chiedere aiuto sui forum",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter8/section4.ipynb b/course/it/chapter8/section4.ipynb
deleted file mode 100644
index 9491cddd..00000000
--- a/course/it/chapter8/section4.ipynb
+++ /dev/null
@@ -1,865 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fare il debug della training pipeline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: You have to specify either input_ids or inputs_embeds'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=raw_datasets[\"train\"],\n",
-    "    eval_dataset=raw_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'hypothesis': 'Product and geography are what make cream skimming work. ',\n",
-       " 'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'premise': 'Conceptually cream skimming has two basic dimensions - product and geography.'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: expected sequence of length 43 at dim 1 (got 37)'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] conceptually cream skimming has two basic dimensions - product and geography. [SEP] product and geography are what make cream skimming work. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(trainer.train_dataset[0][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'hypothesis', 'idx', 'input_ids', 'label', 'premise'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0].keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "transformers.models.distilbert.modeling_distilbert.DistilBertForSequenceClassification"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(trainer.model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0][\"attention_mask\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(trainer.train_dataset[0][\"attention_mask\"]) == len(\n",
-    "    trainer.train_dataset[0][\"input_ids\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0][\"label\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['entailment', 'neutral', 'contradiction']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset.features[\"label\"].names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/git/transformers/src/transformers/data/data_collator.py in torch_default_data_collator(features)\n",
-       "    105                 batch[k] = torch.stack([f[k] for f in features])\n",
-       "    106             else:\n",
-       "--> 107                 batch[k] = torch.tensor([f[k] for f in features])\n",
-       "    108 \n",
-       "    109     return batch\n",
-       "\n",
-       "ValueError: expected sequence of length 45 at dim 1 (got 76)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function transformers.data.data_collator.default_data_collator(features: List[InputDataClass], return_tensors='pt') -> Dict[str, Any]>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "data_collator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "batch = data_collator([trainer.train_dataset[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "actual_train_set = trainer._remove_unused_columns(trainer.train_dataset)\n",
-    "batch = data_collator([actual_train_set[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)\n",
-       "   2386         )\n",
-       "   2387     if dim == 2:\n",
-       "-> 2388         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "   2389     elif dim == 4:\n",
-       "   2390         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "\n",
-       "IndexError: Target 2 is out of bounds."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "outputs = trainer.model.to(device)(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = outputs.loss\n",
-    "loss.backward()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.create_optimizer()\n",
-    "trainer.optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This will take a long time and error out, so you shouldn't run this cell\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_eval_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = outputs.logits.cpu().numpy()\n",
-    "labels = batch[\"labels\"].cpu().numpy()\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((8, 3), (8,))"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions.shape, labels.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.625}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "trainer.create_optimizer()\n",
-    "\n",
-    "for _ in range(20):\n",
-    "    outputs = trainer.model(**batch)\n",
-    "    loss = outputs.loss\n",
-    "    loss.backward()\n",
-    "    trainer.optimizer.step()\n",
-    "    trainer.optimizer.zero_grad()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 1.0}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)\n",
-    "preds = outputs.logits\n",
-    "labels = batch[\"labels\"]\n",
-    "\n",
-    "compute_metrics((preds.cpu().numpy(), labels.cpu().numpy()))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Fare il debug della training pipeline",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter8/section4_tf.ipynb b/course/it/chapter8/section4_tf.ipynb
deleted file mode 100644
index 1048454a..00000000
--- a/course/it/chapter8/section4_tf.ipynb
+++ /dev/null
@@ -1,442 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fare il debug della training pipeline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ValueError: No gradients provided for any variable: ['tf_distil_bert_for_sequence_classification/distilbert/embeddings/word_embeddings/weight:0', '...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    TFAutoModelForSequenceClassification,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "\n",
-    "train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "validation_dataset = tokenized_datasets[\"validation_matched\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
-    "\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        ...,\n",
-       "        [1, 1, 1, ..., 1, 1, 1],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0]])>,\n",
-       " 'label': <tf.Tensor: shape=(16,), dtype=int64, numpy=array([0, 2, 1, 2, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2, 1])>,\n",
-       " 'input_ids': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[ 101, 2174, 1010, ...,    0,    0,    0],\n",
-       "        [ 101, 3174, 2420, ...,    0,    0,    0],\n",
-       "        [ 101, 2044, 2048, ...,    0,    0,    0],\n",
-       "        ...,\n",
-       "        [ 101, 3398, 3398, ..., 2051, 2894,  102],\n",
-       "        [ 101, 1996, 4124, ...,    0,    0,    0],\n",
-       "        [ 101, 1999, 2070, ...,    0,    0,    0]])>}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  246/24543 [..............................] - ETA: 15:52 - loss: nan"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.compile(optimizer=\"adam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
-       "       nan, nan, nan], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([0.6844486 ,        nan,        nan, 0.67127866, 0.7068601 ,\n",
-       "              nan, 0.69309855,        nan, 0.65531296,        nan,\n",
-       "              nan,        nan, 0.675402  ,        nan,        nan,\n",
-       "       0.69831556], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[-0.04761693, -0.06509043],\n",
-       "       [-0.0481936 , -0.04556257],\n",
-       "       [-0.0040929 , -0.05848458],\n",
-       "       [-0.02417453, -0.0684005 ],\n",
-       "       [-0.02517801, -0.05241832],\n",
-       "       [-0.04514256, -0.0757378 ],\n",
-       "       [-0.02656011, -0.02646275],\n",
-       "       [ 0.00766164, -0.04350497],\n",
-       "       [ 0.02060014, -0.05655622],\n",
-       "       [-0.02615328, -0.0447021 ],\n",
-       "       [-0.05119278, -0.06928903],\n",
-       "       [-0.02859691, -0.04879177],\n",
-       "       [-0.02210129, -0.05791225],\n",
-       "       [-0.02363213, -0.05962167],\n",
-       "       [-0.05352269, -0.0481673 ],\n",
-       "       [-0.08141848, -0.07110836]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  2,  5,  7,  9, 10, 11, 13, 14])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "loss = model(batch).loss.numpy()\n",
-    "indices = np.flatnonzero(np.isnan(loss))\n",
-    "indices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  101,  2007,  2032,  2001,  1037, 16480,  3917,  2594,  4135,\n",
-       "        23212,  3070,  2214, 10170,  1010,  2012,  4356,  1997,  3183,\n",
-       "         6838, 12953,  2039,  2000,  1996,  6147,  1997,  2010,  2606,\n",
-       "         1012,   102,  6838,  2001,  3294,  6625,  3773,  1996,  2214,\n",
-       "         2158,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  6814,  2016,  2234,  2461,  2153,  1998, 13322,\n",
-       "         2009,  1012,   102,  2045,  1005,  1055,  2053,  3382,  2008,\n",
-       "         2016,  1005,  2222,  3046,  8103,  2075,  2009,  2153,  1012,\n",
-       "          102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  2007,  1996,  3712,  4634,  1010,  2057,  8108,\n",
-       "         2025,  3404,  2028,  1012,  1996,  2616, 18449,  2125,  1999,\n",
-       "         1037,  9666,  1997,  4100,  8663, 11020,  6313,  2791,  1998,\n",
-       "         2431,  1011,  4301,  1012,   102,  2028,  1005,  1055,  5177,\n",
-       "         2110,  1998,  3977,  2000,  2832,  2106,  2025,  2689,  2104,\n",
-       "         2122,  6214,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1045,  2001,  1999,  1037, 13090,  5948,  2007,  2048,\n",
-       "         2308,  2006,  2026,  5001,  2043,  2026,  2171,  2001,  2170,\n",
-       "         1012,   102,  1045,  2001,  3564,  1999,  2277,  1012,   102,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2195,  4279,  2191,  2039,  1996,  2181,  2124,  2004,\n",
-       "         1996,  2225,  7363,  1012,   102,  2045,  2003,  2069,  2028,\n",
-       "         2451,  1999,  1996,  2225,  7363,  1012,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2061,  2008,  1045,  2123,  1005,  1056,  2113,  2065,\n",
-       "         2009,  2428, 10654,  7347,  2030,  2009,  7126,  2256,  2495,\n",
-       "         2291,   102,  2009,  2003,  5094,  2256,  2495,  2291,  2035,\n",
-       "         2105,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2051,  1010,  2029,  3216,  2019,  2503,  3444,  1010,\n",
-       "         6732,  1996,  2265,  2038, 19840,  2098,  2125,  9906,  1998,\n",
-       "         2003,  2770,  2041,  1997,  4784,  1012,   102,  2051,  6732,\n",
-       "         1996,  2265,  2003,  9525,  1998,  4569,  1012,   102,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1996, 10556,  2140, 11515,  2058,  1010,  2010,  2162,\n",
-       "         2252,  5689,  2013,  2010,  7223,  1012,   102,  2043,  1996,\n",
-       "        10556,  2140, 11515,  2058,  1010,  2010,  2252,  3062,  2000,\n",
-       "         1996,  2598,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101, 13543,  1999,  2049,  6143,  2933,  2443,   102,  2025,\n",
-       "        13543,  1999,  6143,  2933,  2003,  2443,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "input_ids[indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model.compile(optimizer=Adam(5e-5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "319/24543 [..............................] - ETA: 16:07 - loss: 0.9718"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "tokenizer.decode(input_ids[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = batch[\"labels\"].numpy()\n",
-    "label = labels[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break\n",
-    "\n",
-    "# Make sure you have run model.compile() and set your optimizer,\n",
-    "# and your loss/metrics if you're using them\n",
-    "\n",
-    "model.fit(batch, epochs=20)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Fare il debug della training pipeline",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter8/section5.ipynb b/course/it/chapter8/section5.ipynb
deleted file mode 100644
index dbaccbbe..00000000
--- a/course/it/chapter8/section5.ipynb
+++ /dev/null
@@ -1,42 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Come scrivere un issue correttamente"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Come scrivere un issue correttamente",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter8/section7.ipynb b/course/it/chapter8/section7.ipynb
deleted file mode 100644
index 93f2e01c..00000000
--- a/course/it/chapter8/section7.ipynb
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quiz di fine capitolo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)\n",
-    "# ---------------------------------------------------------------------------\n",
-    "# ImportError                               Traceback (most recent call last)\n",
-    "# /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_30848/333858878.py in <module>\n",
-    "# ----> 1 from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Quiz di fine capitolo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter9/section2.ipynb b/course/it/chapter9/section2.ipynb
deleted file mode 100644
index 2e6a1b38..00000000
--- a/course/it/chapter9/section2.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Creare la tua prima demo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def greet(name):\n",
-    "    return \"Hello \" + name\n",
-    "\n",
-    "\n",
-    "demo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\")\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def greet(name):\n",
-    "    return \"Hello \" + name\n",
-    "\n",
-    "\n",
-    "# We instantiate the Textbox class\n",
-    "textbox = gr.Textbox(label=\"Type your name here:\", placeholder=\"John Doe\", lines=2)\n",
-    "\n",
-    "gr.Interface(fn=greet, inputs=textbox, outputs=\"text\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model = pipeline(\"text-generation\")\n",
-    "\n",
-    "\n",
-    "def predict(prompt):\n",
-    "    completion = model(prompt)[0][\"generated_text\"]\n",
-    "    return completion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "gr.Interface(fn=predict, inputs=\"text\", outputs=\"text\").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Creare la tua prima demo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/it/chapter9/section3.ipynb b/course/it/chapter9/section3.ipynb
deleted file mode 100644
index 1e1d4dc6..00000000
--- a/course/it/chapter9/section3.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Capire la classe Interface"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def reverse_audio(audio):\n",
-    "    sr, data = audio\n",
-    "    reversed_audio = (sr, np.flipud(data))\n",
-    "    return reversed_audio\n",
-    "\n",
-    "\n",
-    "mic = gr.Audio(source=\"microphone\", type=\"numpy\", label=\"Speak here...\")\n",
-    "gr.Interface(reverse_audio, mic, \"audio\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "notes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n",
-    "\n",
-    "\n",
-    "def generate_tone(note, octave, duration):\n",
-    "    sr = 48000\n",
-    "    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n",
-    "    frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n",
-    "    duration = int(duration)\n",
-    "    audio = np.linspace(0, duration, duration * sr)\n",
-    "    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n",
-    "    return (sr, audio)\n",
-    "\n",
-    "\n",
-    "gr.Interface(\n",
-    "    generate_tone,\n",
-    "    [\n",
-    "        gr.Dropdown(notes, type=\"index\"),\n",
-    "        gr.Slider(minimum=4, maximum=6, step=1),\n",
-    "        gr.Textbox(type=\"number\", value=1, label=\"Duration in seconds\"),\n",
-    "    ],\n",
-    "    \"audio\",\n",
-    ").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "import gradio as gr\n",
-    "\n",
-    "model = pipeline(\"automatic-speech-recognition\")\n",
-    "\n",
-    "\n",
-    "def transcribe_audio(mic=None, file=None):\n",
-    "    if mic is not None:\n",
-    "        audio = mic\n",
-    "    elif file is not None:\n",
-    "        audio = file\n",
-    "    else:\n",
-    "        return \"You must either provide a mic recording or a file\"\n",
-    "    transcription = model(audio)[\"text\"]\n",
-    "    return transcription\n",
-    "\n",
-    "\n",
-    "gr.Interface(\n",
-    "    fn=transcribe_audio,\n",
-    "    inputs=[\n",
-    "        gr.Audio(source=\"microphone\", type=\"filepath\", optional=True),\n",
-    "        gr.Audio(source=\"upload\", type=\"filepath\", optional=True),\n",
-    "    ],\n",
-    "    outputs=\"text\",\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Capire la classe Interface",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter1/section10.ipynb b/course/ja/chapter1/section10.ipynb
deleted file mode 100644
index 99fb8073..00000000
--- a/course/ja/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 章末クイズ"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "章末クイズ",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter1/section3.ipynb b/course/ja/chapter1/section3.ipynb
deleted file mode 100644
index d741d589..00000000
--- a/course/ja/chapter1/section3.ipynb
+++ /dev/null
@@ -1,321 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformersで何ができる?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformersで何ができる?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter1/section8.ipynb b/course/ja/chapter1/section8.ipynb
deleted file mode 100644
index 94a07d7c..00000000
--- a/course/ja/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# バイアスと限界"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "バイアスと限界",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section2_pt.ipynb b/course/ja/chapter2/section2_pt.ipynb
deleted file mode 100644
index ad4552a0..00000000
--- a/course/ja/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# pipelineの裏側 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "pipelineの裏側 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section2_tf.ipynb b/course/ja/chapter2/section2_tf.ipynb
deleted file mode 100644
index 0774f04c..00000000
--- a/course/ja/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# pipelineの裏側 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "pipelineの裏側 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section3_pt.ipynb b/course/ja/chapter2/section3_pt.ipynb
deleted file mode 100644
index 2dc7398e..00000000
--- a/course/ja/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# モデル (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "モデル (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section3_tf.ipynb b/course/ja/chapter2/section3_tf.ipynb
deleted file mode 100644
index f065062a..00000000
--- a/course/ja/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# モデル (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "モデル (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section4_pt.ipynb b/course/ja/chapter2/section4_pt.ipynb
deleted file mode 100644
index 112009f1..00000000
--- a/course/ja/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# トークナイザ (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "トークナイザ (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section4_tf.ipynb b/course/ja/chapter2/section4_tf.ipynb
deleted file mode 100644
index 0d095bf1..00000000
--- a/course/ja/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# トークナイザ (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "トークナイザ (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section5_pt.ipynb b/course/ja/chapter2/section5_pt.ipynb
deleted file mode 100644
index e9e1f6de..00000000
--- a/course/ja/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 複数系列の処理 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "複数系列の処理 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter2/section5_tf.ipynb b/course/ja/chapter2/section5_tf.ipynb
deleted file mode 100644
index 848777f8..00000000
--- a/course/ja/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 複数系列の処理 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "複数系列の処理 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter4/section2_pt.ipynb b/course/ja/chapter4/section2_pt.ipynb
deleted file mode 100644
index 1f85095e..00000000
--- a/course/ja/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 学習済みモデルを使う (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "学習済みモデルを使う (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter4/section2_tf.ipynb b/course/ja/chapter4/section2_tf.ipynb
deleted file mode 100644
index 631c916f..00000000
--- a/course/ja/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 学習済みモデルを使う (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "学習済みモデルを使う (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter4/section3_pt.ipynb b/course/ja/chapter4/section3_pt.ipynb
deleted file mode 100644
index 280294c4..00000000
--- a/course/ja/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 学習済みモデルを共有する (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # ユーザー管理\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # レポジトリの作成と管理\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # そして、コンテンツに関する情報を取得／変更するためのいくつかのメソッド\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# モデルを使って、トレーニングしたり、微調整したり...。\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "学習済みモデルを共有する (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter4/section3_tf.ipynb b/course/ja/chapter4/section3_tf.ipynb
deleted file mode 100644
index 7e4ad001..00000000
--- a/course/ja/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 学習済みモデルを共有する (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # ユーザー管理\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # レポジトリの作成と管理\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # そして、コンテンツに関する情報を取得／変更するためのいくつかのメソッド\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# モデルを使って、トレーニングしたり、微調整したり...。\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "学習済みモデルを共有する (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section2_pt.ipynb b/course/ja/chapter7/section2_pt.ipynb
deleted file mode 100644
index dc313015..00000000
--- a/course/ja/chapter7/section2_pt.ipynb
+++ /dev/null
@@ -1,891 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# トークン分類 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"conll2003\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 14041\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3250\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3453\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"tokens\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"ner_tags\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], names_file=None, id=None), length=-1, id=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n",
-    "ner_feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label_names = ner_feature.feature.names\n",
-    "label_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'EU    rejects German call to boycott British lamb .'\n",
-       "'B-ORG O       B-MISC O    O  O       B-MISC  O    O'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "words = raw_datasets[\"train\"][0][\"tokens\"]\n",
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "line1 = \"\"\n",
-    "line2 = \"\"\n",
-    "for word, label in zip(words, labels):\n",
-    "    full_label = label_names[label]\n",
-    "    max_length = max(len(word), len(full_label))\n",
-    "    line1 += word + \" \" * (max_length - len(word) + 1)\n",
-    "    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
-    "\n",
-    "print(line1)\n",
-    "print(line2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'la', '##mb', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
-    "inputs.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def align_labels_with_tokens(labels, word_ids):\n",
-    "    new_labels = []\n",
-    "    current_word = None\n",
-    "    for word_id in word_ids:\n",
-    "        if word_id != current_word:\n",
-    "            # Start of a new word!\n",
-    "            current_word = word_id\n",
-    "            label = -100 if word_id is None else labels[word_id]\n",
-    "            new_labels.append(label)\n",
-    "        elif word_id is None:\n",
-    "            # Special token\n",
-    "            new_labels.append(-100)\n",
-    "        else:\n",
-    "            # Same word as previous token\n",
-    "            label = labels[word_id]\n",
-    "            # If the label is B-XXX we change it to I-XXX\n",
-    "            if label % 2 == 1:\n",
-    "                label += 1\n",
-    "            new_labels.append(label)\n",
-    "\n",
-    "    return new_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n",
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "word_ids = inputs.word_ids()\n",
-    "print(labels)\n",
-    "print(align_labels_with_tokens(labels, word_ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_align_labels(examples):\n",
-    "    tokenized_inputs = tokenizer(\n",
-    "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
-    "    )\n",
-    "    all_labels = examples[\"ner_tags\"]\n",
-    "    new_labels = []\n",
-    "    for i, labels in enumerate(all_labels):\n",
-    "        word_ids = tokenized_inputs.word_ids(i)\n",
-    "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
-    "\n",
-    "    tokenized_inputs[\"labels\"] = new_labels\n",
-    "    return tokenized_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize_and_align_labels,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForTokenClassification\n",
-    "\n",
-    "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
-       "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
-       "[-100, 1, 2, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(2):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install seqeval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"seqeval\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "labels = [label_names[i] for i in labels]\n",
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MISC': {'precision': 1.0, 'recall': 0.5, 'f1': 0.67, 'number': 2},\n",
-       " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
-       " 'overall_precision': 1.0,\n",
-       " 'overall_recall': 0.67,\n",
-       " 'overall_f1': 0.8,\n",
-       " 'overall_accuracy': 0.89}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = labels.copy()\n",
-    "predictions[2] = \"O\"\n",
-    "metric.compute(predictions=[predictions], references=[labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_preds):\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "\n",
-    "    # Remove ignored index (special tokens) and convert to labels\n",
-    "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
-    "    true_predictions = [\n",
-    "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
-    "    return {\n",
-    "        \"precision\": all_metrics[\"overall_precision\"],\n",
-    "        \"recall\": all_metrics[\"overall_recall\"],\n",
-    "        \"f1\": all_metrics[\"overall_f1\"],\n",
-    "        \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id2label = {i: label for i, label in enumerate(label_names)}\n",
-    "label2id = {v: k for k, v in id2label.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForTokenClassification\n",
-    "\n",
-    "model = AutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    \"bert-finetuned-ner\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/bert-finetuned-ner/commit/26ab21e5b1568f9afeccdaed2d8715f571d786ed'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/bert-finetuned-ner-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"bert-finetuned-ner-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"bert-finetuned-ner-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess(predictions, labels):\n",
-    "    predictions = predictions.detach().cpu().clone().numpy()\n",
-    "    labels = labels.detach().cpu().clone().numpy()\n",
-    "\n",
-    "    # Remove ignored index (special tokens) and convert to labels\n",
-    "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
-    "    true_predictions = [\n",
-    "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "    return true_labels, true_predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    for batch in eval_dataloader:\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        predictions = outputs.logits.argmax(dim=-1)\n",
-    "        labels = batch[\"labels\"]\n",
-    "\n",
-    "        # Necessary to pad predictions and labels for being gathered\n",
-    "        predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)\n",
-    "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
-    "\n",
-    "        predictions_gathered = accelerator.gather(predictions)\n",
-    "        labels_gathered = accelerator.gather(labels)\n",
-    "\n",
-    "        true_predictions, true_labels = postprocess(predictions_gathered, labels_gathered)\n",
-    "        metric.add_batch(predictions=true_predictions, references=true_labels)\n",
-    "\n",
-    "    results = metric.compute()\n",
-    "    print(\n",
-    "        f\"epoch {epoch}:\",\n",
-    "        {\n",
-    "            key: results[f\"overall_{key}\"]\n",
-    "            for key in [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n",
-    "        },\n",
-    "    )\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accelerator.wait_for_everyone()\n",
-    "unwrapped_model = accelerator.unwrap_model(model)\n",
-    "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9988506, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.9647625, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.9986118, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-ner\"\n",
-    "token_classifier = pipeline(\n",
-    "    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",
-    ")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "トークン分類 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section2_tf.ipynb b/course/ja/chapter7/section2_tf.ipynb
deleted file mode 100644
index 363134f1..00000000
--- a/course/ja/chapter7/section2_tf.ipynb
+++ /dev/null
@@ -1,707 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# トークン分類 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"conll2003\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 14041\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3250\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3453\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"tokens\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"ner_tags\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], names_file=None, id=None), length=-1, id=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n",
-    "ner_feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label_names = ner_feature.feature.names\n",
-    "label_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'EU    rejects German call to boycott British lamb .'\n",
-       "'B-ORG O       B-MISC O    O  O       B-MISC  O    O'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "words = raw_datasets[\"train\"][0][\"tokens\"]\n",
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "line1 = \"\"\n",
-    "line2 = \"\"\n",
-    "for word, label in zip(words, labels):\n",
-    "    full_label = label_names[label]\n",
-    "    max_length = max(len(word), len(full_label))\n",
-    "    line1 += word + \" \" * (max_length - len(word) + 1)\n",
-    "    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
-    "\n",
-    "print(line1)\n",
-    "print(line2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'la', '##mb', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
-    "inputs.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def align_labels_with_tokens(labels, word_ids):\n",
-    "    new_labels = []\n",
-    "    current_word = None\n",
-    "    for word_id in word_ids:\n",
-    "        if word_id != current_word:\n",
-    "            # Start of a new word!\n",
-    "            current_word = word_id\n",
-    "            label = -100 if word_id is None else labels[word_id]\n",
-    "            new_labels.append(label)\n",
-    "        elif word_id is None:\n",
-    "            # Special token\n",
-    "            new_labels.append(-100)\n",
-    "        else:\n",
-    "            # Same word as previous token\n",
-    "            label = labels[word_id]\n",
-    "            # If the label is B-XXX we change it to I-XXX\n",
-    "            if label % 2 == 1:\n",
-    "                label += 1\n",
-    "            new_labels.append(label)\n",
-    "\n",
-    "    return new_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n",
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "word_ids = inputs.word_ids()\n",
-    "print(labels)\n",
-    "print(align_labels_with_tokens(labels, word_ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_align_labels(examples):\n",
-    "    tokenized_inputs = tokenizer(\n",
-    "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
-    "    )\n",
-    "    all_labels = examples[\"ner_tags\"]\n",
-    "    new_labels = []\n",
-    "    for i, labels in enumerate(all_labels):\n",
-    "        word_ids = tokenized_inputs.word_ids(i)\n",
-    "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
-    "\n",
-    "    tokenized_inputs[\"labels\"] = new_labels\n",
-    "    return tokenized_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize_and_align_labels,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForTokenClassification\n",
-    "\n",
-    "data_collator = DataCollatorForTokenClassification(\n",
-    "    tokenizer=tokenizer, return_tensors=\"tf\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
-       "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
-       "[-100, 1, 2, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(2):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=16,\n",
-    ")\n",
-    "\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id2label = {i: label for i, label in enumerate(label_names)}\n",
-    "label2id = {v: k for k, v in id2label.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForTokenClassification\n",
-    "\n",
-    "model = TFAutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "# Comment this line out if you're using a GPU that will not benefit from this\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"bert-finetuned-ner\", tokenizer=tokenizer)\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_eval_dataset,\n",
-    "    callbacks=[callback],\n",
-    "    epochs=num_epochs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install seqeval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"seqeval\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "labels = [label_names[i] for i in labels]\n",
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MISC': {'precision': 1.0, 'recall': 0.5, 'f1': 0.67, 'number': 2},\n",
-       " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
-       " 'overall_precision': 1.0,\n",
-       " 'overall_recall': 0.67,\n",
-       " 'overall_f1': 0.8,\n",
-       " 'overall_accuracy': 0.89}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = labels.copy()\n",
-    "predictions[2] = \"O\"\n",
-    "metric.compute(predictions=[predictions], references=[labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'LOC': {'precision': 0.91, 'recall': 0.92, 'f1': 0.91, 'number': 1668},\n",
-       " 'MISC': {'precision': 0.70, 'recall': 0.79, 'f1': 0.74, 'number': 702},\n",
-       " 'ORG': {'precision': 0.85, 'recall': 0.90, 'f1': 0.88, 'number': 1661},\n",
-       " 'PER': {'precision': 0.95, 'recall': 0.95, 'f1': 0.95, 'number': 1617},\n",
-       " 'overall_precision': 0.87,\n",
-       " 'overall_recall': 0.91,\n",
-       " 'overall_f1': 0.89,\n",
-       " 'overall_accuracy': 0.97}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "all_predictions = []\n",
-    "all_labels = []\n",
-    "for batch in tf_eval_dataset:\n",
-    "    logits = model.predict(batch)[\"logits\"]\n",
-    "    labels = batch[\"labels\"]\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    for prediction, label in zip(predictions, labels):\n",
-    "        for predicted_idx, label_idx in zip(prediction, label):\n",
-    "            if label_idx == -100:\n",
-    "                continue\n",
-    "            all_predictions.append(label_names[predicted_idx])\n",
-    "            all_labels.append(label_names[label_idx])\n",
-    "metric.compute(predictions=[all_predictions], references=[all_labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9988506, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.9647625, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.9986118, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-ner\"\n",
-    "token_classifier = pipeline(\n",
-    "    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",
-    ")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "トークン分類 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section3_pt.ipynb b/course/ja/chapter7/section3_pt.ipynb
deleted file mode 100644
index 8ea1d647..00000000
--- a/course/ja/chapter7/section3_pt.ipynb
+++ /dev/null
@@ -1,957 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# マスク言語モデルの微調整 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> DistilBERT number of parameters: 67M'\n",
-       "'>>> BERT number of parameters: 110M'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "distilbert_num_parameters = model.num_parameters() / 1_000_000\n",
-    "print(f\"'>>> DistilBERT number of parameters: {round(distilbert_num_parameters)}M'\")\n",
-    "print(f\"'>>> BERT number of parameters: 110M'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a great [MASK].\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> This is a great deal.'\n",
-       "'>>> This is a great success.'\n",
-       "'>>> This is a great adventure.'\n",
-       "'>>> This is a great idea.'\n",
-       "'>>> This is a great feat.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "token_logits = model(**inputs).logits\n",
-    "# Find the location of [MASK] and extract its logits\n",
-    "mask_token_index = torch.where(inputs[\"input_ids\"] == tokenizer.mask_token_id)[1]\n",
-    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
-    "# Pick the [MASK] candidates with the highest logits\n",
-    "top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()\n",
-    "\n",
-    "for token in top_5_tokens:\n",
-    "    print(f\"'>>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "imdb_dataset = load_dataset(\"imdb\")\n",
-    "imdb_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "'>>> Review: This is your typical Priyadarshan movie--a bunch of loony characters out on some silly mission. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, \"kodokoo\" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. Don\\'t get me wrong; as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Now, the hilarity of his films is fading as he is using the same formula over and over again.<br /><br />Songs are good. Tanushree Datta looks awesome. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Kunal Khemu is OK, and Sharman Joshi is the best.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam\" Peckinpah directed the film. Somehow, his direction is not enough. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.<br /><br />The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. Watch the great Peckinpah films. Skip this mess.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: I saw this movie at the theaters when I was about 6 or 7 years old. I loved it then, and have recently come to own a VHS version. <br /><br />My 4 and 6 year old children love this movie and have been asking again and again to watch it. <br /><br />I have enjoyed watching it again too. Though I have to admit it is not as good on a little TV.<br /><br />I do not have older children so I do not know what they would think of it. <br /><br />The songs are very cute. My daughter keeps singing them over and over.<br /><br />Hope this helps.'\n",
-       "'>>> Label: 1'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
-    "\n",
-    "for row in sample:\n",
-    "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
-    "    print(f\"'>>> Label: {row['label']}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize_function(examples):\n",
-    "    result = tokenizer(examples[\"text\"])\n",
-    "    if tokenizer.is_fast:\n",
-    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Use batched=True to activate fast multithreading!\n",
-    "tokenized_datasets = imdb_dataset.map(\n",
-    "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "512"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.model_max_length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 128"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review 0 length: 200'\n",
-       "'>>> Review 1 length: 559'\n",
-       "'>>> Review 2 length: 192'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Slicing produces a list of lists for each feature\n",
-    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
-    "\n",
-    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
-    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Concatenated reviews length: 951'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatenated_examples = {\n",
-    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
-    "}\n",
-    "total_length = len(concatenated_examples[\"input_ids\"])\n",
-    "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 55'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chunks = {\n",
-    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "    for k, t in concatenated_examples.items()\n",
-    "}\n",
-    "\n",
-    "for chunk in chunks[\"input_ids\"]:\n",
-    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def group_texts(examples):\n",
-    "    # Concatenate all texts\n",
-    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
-    "    # Compute length of concatenated texts\n",
-    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
-    "    # We drop the last chunk if it's smaller than chunk_size\n",
-    "    total_length = (total_length // chunk_size) * chunk_size\n",
-    "    # Split by chunks of max_len\n",
-    "    result = {\n",
-    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "        for k, t in concatenated_examples.items()\n",
-    "    }\n",
-    "    # Create a new labels column\n",
-    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 61289\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 59905\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 122963\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
-    "lm_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\".... at.......... high. a classic line : inspector : i'm here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn't! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "for sample in samples:\n",
-    "    _ = sample.pop(\"word_ids\")\n",
-    "\n",
-    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "import numpy as np\n",
-    "\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "wwm_probability = 0.2\n",
-    "\n",
-    "\n",
-    "def whole_word_masking_data_collator(features):\n",
-    "    for feature in features:\n",
-    "        word_ids = feature.pop(\"word_ids\")\n",
-    "\n",
-    "        # Create a map between words and corresponding token indices\n",
-    "        mapping = collections.defaultdict(list)\n",
-    "        current_word_index = -1\n",
-    "        current_word = None\n",
-    "        for idx, word_id in enumerate(word_ids):\n",
-    "            if word_id is not None:\n",
-    "                if word_id != current_word:\n",
-    "                    current_word = word_id\n",
-    "                    current_word_index += 1\n",
-    "                mapping[current_word_index].append(idx)\n",
-    "\n",
-    "        # Randomly mask words\n",
-    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
-    "        input_ids = feature[\"input_ids\"]\n",
-    "        labels = feature[\"labels\"]\n",
-    "        new_labels = [-100] * len(labels)\n",
-    "        for word_id in np.where(mask)[0]:\n",
-    "            word_id = word_id.item()\n",
-    "            for idx in mapping[word_id]:\n",
-    "                new_labels[idx] = labels[idx]\n",
-    "                input_ids[idx] = tokenizer.mask_token_id\n",
-    "        feature[\"labels\"] = new_labels\n",
-    "\n",
-    "    return default_data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> [CLS] bromwell high is a cartoon comedy [MASK] it ran at the same time as some other programs about school life, such as \" teachers \". my 35 years in the teaching profession lead me to believe that bromwell high\\'s satire is much closer to reality than is \" teachers \". the scramble to survive financially, the insightful students who can see right through their pathetic teachers\\'pomp, the pettiness of the whole situation, all remind me of the schools i knew and their students. when i saw the episode in which a student repeatedly tried to burn down the school, i immediately recalled.....'\n",
-       "\n",
-       "'>>> .... [MASK] [MASK] [MASK] [MASK]....... high. a classic line : inspector : i\\'m here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn\\'t! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "batch = whole_word_masking_data_collator(samples)\n",
-    "\n",
-    "for chunk in batch[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 10000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 1000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_size = 10_000\n",
-    "test_size = int(0.1 * train_size)\n",
-    "\n",
-    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
-    "    train_size=train_size, test_size=test_size, seed=42\n",
-    ")\n",
-    "downsampled_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "batch_size = 64\n",
-    "# Show the training loss with every epoch\n",
-    "logging_steps = len(downsampled_dataset[\"train\"]) // batch_size\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=f\"{model_name}-finetuned-imdb\",\n",
-    "    overwrite_output_dir=True,\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    weight_decay=0.01,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    push_to_hub=True,\n",
-    "    fp16=True,\n",
-    "    logging_steps=logging_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=training_args,\n",
-    "    train_dataset=downsampled_dataset[\"train\"],\n",
-    "    eval_dataset=downsampled_dataset[\"test\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 21.75"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 11.32"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def insert_random_mask(batch):\n",
-    "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
-    "    masked_inputs = data_collator(features)\n",
-    "    # Create a new \"masked\" column for each column in the dataset\n",
-    "    return {\"masked_\" + k: v.numpy() for k, v in masked_inputs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downsampled_dataset = downsampled_dataset.remove_columns([\"word_ids\"])\n",
-    "eval_dataset = downsampled_dataset[\"test\"].map(\n",
-    "    insert_random_mask,\n",
-    "    batched=True,\n",
-    "    remove_columns=downsampled_dataset[\"test\"].column_names,\n",
-    ")\n",
-    "eval_dataset = eval_dataset.rename_columns(\n",
-    "    {\n",
-    "        \"masked_input_ids\": \"input_ids\",\n",
-    "        \"masked_attention_mask\": \"attention_mask\",\n",
-    "        \"masked_labels\": \"labels\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "batch_size = 64\n",
-    "train_dataloader = DataLoader(\n",
-    "    downsampled_dataset[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    batch_size=batch_size,\n",
-    "    collate_fn=data_collator,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    eval_dataset, batch_size=batch_size, collate_fn=default_data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'lewtun/distilbert-base-uncased-finetuned-imdb-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import get_full_repo_name\n",
-    "\n",
-    "model_name = \"distilbert-base-uncased-finetuned-imdb-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "output_dir = model_name\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Epoch 0: Perplexity: 11.397545307900472\n",
-       ">>> Epoch 1: Perplexity: 10.904909330983092\n",
-       ">>> Epoch 2: Perplexity: 10.729503505340409"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "import math\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        loss = outputs.loss\n",
-    "        losses.append(accelerator.gather(loss.repeat(batch_size)))\n",
-    "\n",
-    "    losses = torch.cat(losses)\n",
-    "    losses = losses[: len(eval_dataset)]\n",
-    "    try:\n",
-    "        perplexity = math.exp(torch.mean(losses))\n",
-    "    except OverflowError:\n",
-    "        perplexity = float(\"inf\")\n",
-    "\n",
-    "    print(f\">>> Epoch {epoch}: Perplexity: {perplexity}\")\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "mask_filler = pipeline(\n",
-    "    \"fill-mask\", model=\"huggingface-course/distilbert-base-uncased-finetuned-imdb\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> this is a great movie.'\n",
-       "'>>> this is a great film.'\n",
-       "'>>> this is a great story.'\n",
-       "'>>> this is a great movies.'\n",
-       "'>>> this is a great character.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds = mask_filler(text)\n",
-    "\n",
-    "for pred in preds:\n",
-    "    print(f\">>> {pred['sequence']}\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "マスク言語モデルの微調整 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section3_tf.ipynb b/course/ja/chapter7/section3_tf.ipynb
deleted file mode 100644
index f8c837ae..00000000
--- a/course/ja/chapter7/section3_tf.ipynb
+++ /dev/null
@@ -1,759 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# マスク言語モデルの微調整 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Model: \"tf_distil_bert_for_masked_lm\"\n",
-       "_________________________________________________________________\n",
-       "Layer (type)                 Output Shape              Param #   \n",
-       "=================================================================\n",
-       "distilbert (TFDistilBertMain multiple                  66362880  \n",
-       "_________________________________________________________________\n",
-       "vocab_transform (Dense)      multiple                  590592    \n",
-       "_________________________________________________________________\n",
-       "vocab_layer_norm (LayerNorma multiple                  1536      \n",
-       "_________________________________________________________________\n",
-       "vocab_projector (TFDistilBer multiple                  23866170  \n",
-       "=================================================================\n",
-       "Total params: 66,985,530\n",
-       "Trainable params: 66,985,530\n",
-       "Non-trainable params: 0\n",
-       "_________________________________________________________________"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model(model.dummy_inputs)  # Build the model\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a great [MASK].\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> This is a great deal.'\n",
-       "'>>> This is a great success.'\n",
-       "'>>> This is a great adventure.'\n",
-       "'>>> This is a great idea.'\n",
-       "'>>> This is a great feat.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"np\")\n",
-    "token_logits = model(**inputs).logits\n",
-    "# Find the location of [MASK] and extract its logits\n",
-    "mask_token_index = np.argwhere(inputs[\"input_ids\"] == tokenizer.mask_token_id)[0, 1]\n",
-    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
-    "# Pick the [MASK] candidates with the highest logits\n",
-    "# We negate the array before argsort to get the largest, not the smallest, logits\n",
-    "top_5_tokens = np.argsort(-mask_token_logits)[:5].tolist()\n",
-    "\n",
-    "for token in top_5_tokens:\n",
-    "    print(f\">>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "imdb_dataset = load_dataset(\"imdb\")\n",
-    "imdb_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "'>>> Review: This is your typical Priyadarshan movie--a bunch of loony characters out on some silly mission. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, \"kodokoo\" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. Don\\'t get me wrong; as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Now, the hilarity of his films is fading as he is using the same formula over and over again.<br /><br />Songs are good. Tanushree Datta looks awesome. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Kunal Khemu is OK, and Sharman Joshi is the best.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam\" Peckinpah directed the film. Somehow, his direction is not enough. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.<br /><br />The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. Watch the great Peckinpah films. Skip this mess.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: I saw this movie at the theaters when I was about 6 or 7 years old. I loved it then, and have recently come to own a VHS version. <br /><br />My 4 and 6 year old children love this movie and have been asking again and again to watch it. <br /><br />I have enjoyed watching it again too. Though I have to admit it is not as good on a little TV.<br /><br />I do not have older children so I do not know what they would think of it. <br /><br />The songs are very cute. My daughter keeps singing them over and over.<br /><br />Hope this helps.'\n",
-       "'>>> Label: 1'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
-    "\n",
-    "for row in sample:\n",
-    "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
-    "    print(f\"'>>> Label: {row['label']}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize_function(examples):\n",
-    "    result = tokenizer(examples[\"text\"])\n",
-    "    if tokenizer.is_fast:\n",
-    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Use batched=True to activate fast multithreading!\n",
-    "tokenized_datasets = imdb_dataset.map(\n",
-    "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "512"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.model_max_length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 128"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review 0 length: 200'\n",
-       "'>>> Review 1 length: 559'\n",
-       "'>>> Review 2 length: 192'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Slicing produces a list of lists for each feature\n",
-    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
-    "\n",
-    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
-    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Concatenated reviews length: 951'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatenated_examples = {\n",
-    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
-    "}\n",
-    "total_length = len(concatenated_examples[\"input_ids\"])\n",
-    "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 55'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chunks = {\n",
-    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "    for k, t in concatenated_examples.items()\n",
-    "}\n",
-    "\n",
-    "for chunk in chunks[\"input_ids\"]:\n",
-    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def group_texts(examples):\n",
-    "    # Concatenate all texts\n",
-    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
-    "    # Compute length of concatenated texts\n",
-    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
-    "    # We drop the last chunk if it's smaller than chunk_size\n",
-    "    total_length = (total_length // chunk_size) * chunk_size\n",
-    "    # Split by chunks of max_len\n",
-    "    result = {\n",
-    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "        for k, t in concatenated_examples.items()\n",
-    "    }\n",
-    "    # Create a new labels column\n",
-    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 61289\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 59905\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 122963\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
-    "lm_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\".... at.......... high. a classic line : inspector : i'm here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn't! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "for sample in samples:\n",
-    "    _ = sample.pop(\"word_ids\")\n",
-    "\n",
-    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "import numpy as np\n",
-    "\n",
-    "from transformers.data.data_collator import tf_default_data_collator\n",
-    "\n",
-    "wwm_probability = 0.2\n",
-    "\n",
-    "\n",
-    "def whole_word_masking_data_collator(features):\n",
-    "    for feature in features:\n",
-    "        word_ids = feature.pop(\"word_ids\")\n",
-    "\n",
-    "        # Create a map between words and corresponding token indices\n",
-    "        mapping = collections.defaultdict(list)\n",
-    "        current_word_index = -1\n",
-    "        current_word = None\n",
-    "        for idx, word_id in enumerate(word_ids):\n",
-    "            if word_id is not None:\n",
-    "                if word_id != current_word:\n",
-    "                    current_word = word_id\n",
-    "                    current_word_index += 1\n",
-    "                mapping[current_word_index].append(idx)\n",
-    "\n",
-    "        # Randomly mask words\n",
-    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
-    "        input_ids = feature[\"input_ids\"]\n",
-    "        labels = feature[\"labels\"]\n",
-    "        new_labels = [-100] * len(labels)\n",
-    "        for word_id in np.where(mask)[0]:\n",
-    "            word_id = word_id.item()\n",
-    "            for idx in mapping[word_id]:\n",
-    "                new_labels[idx] = labels[idx]\n",
-    "                input_ids[idx] = tokenizer.mask_token_id\n",
-    "        feature[\"labels\"] = new_labels\n",
-    "\n",
-    "    return tf_default_data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> [CLS] bromwell high is a cartoon comedy [MASK] it ran at the same time as some other programs about school life, such as \" teachers \". my 35 years in the teaching profession lead me to believe that bromwell high\\'s satire is much closer to reality than is \" teachers \". the scramble to survive financially, the insightful students who can see right through their pathetic teachers\\'pomp, the pettiness of the whole situation, all remind me of the schools i knew and their students. when i saw the episode in which a student repeatedly tried to burn down the school, i immediately recalled.....'\n",
-       "\n",
-       "'>>> .... [MASK] [MASK] [MASK] [MASK]....... high. a classic line : inspector : i\\'m here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn\\'t! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "batch = whole_word_masking_data_collator(samples)\n",
-    "\n",
-    "for chunk in batch[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 10000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 1000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_size = 10_000\n",
-    "test_size = int(0.1 * train_size)\n",
-    "\n",
-    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
-    "    train_size=train_size, test_size=test_size, seed=42\n",
-    ")\n",
-    "downsampled_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = downsampled_dataset[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "\n",
-    "tf_eval_dataset = downsampled_dataset[\"test\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=32,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "num_train_steps = len(tf_train_dataset)\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=f\"{model_name}-finetuned-imdb\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 21.75"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "eval_loss = model.evaluate(tf_eval_dataset)\n",
-    "print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 11.32"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eval_loss = model.evaluate(tf_eval_dataset)\n",
-    "print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "mask_filler = pipeline(\n",
-    "    \"fill-mask\", model=\"huggingface-course/distilbert-base-uncased-finetuned-imdb\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> this is a great movie.'\n",
-       "'>>> this is a great film.'\n",
-       "'>>> this is a great story.'\n",
-       "'>>> this is a great movies.'\n",
-       "'>>> this is a great character.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds = mask_filler(text)\n",
-    "\n",
-    "for pred in preds:\n",
-    "    print(f\">>> {pred['sequence']}\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "マスク言語モデルの微調整 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section4_pt.ipynb b/course/ja/chapter7/section4_pt.ipynb
deleted file mode 100644
index 98fab28b..00000000
--- a/course/ja/chapter7/section4_pt.ipynb
+++ /dev/null
@@ -1,963 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 翻訳 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 210173\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 189155\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 21018\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
-    "split_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Default to expanded threads',\n",
-       " 'fr': 'Par défaut, développer les fils de discussion'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][1][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut pour les threads élargis'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Unable to import %1 using the OFX importer plugin. This file is not the correct format.',\n",
-       " 'fr': \"Impossible d'importer %1 en utilisant le module d'extension d'importation OFX. Ce fichier n'a pas un format correct.\"}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][172][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le plugin d'importateur OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
-    "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
-    "\n",
-    "inputs = tokenizer(en_sentence)\n",
-    "with tokenizer.as_target_tokenizer():\n",
-    "    targets = tokenizer(fr_sentence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Par', '▁dé', 'f', 'aut', ',', '▁dé', 've', 'lop', 'per', '▁les', '▁fil', 's', '▁de', '▁discussion', '</s>']\n",
-       "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong_targets = tokenizer(fr_sentence)\n",
-    "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
-    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 128\n",
-    "max_target_length = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
-    "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
-    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
-    "\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = split_datasets.map(\n",
-    "    preprocess_function,\n",
-    "    batched=True,\n",
-    "    remove_columns=split_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'input_ids', 'labels', 'decoder_input_ids'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
-    "batch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,  -100,\n",
-       "          -100,  -100,  -100,  -100,  -100,  -100],\n",
-       "        [ 1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,   817,\n",
-       "           550,  7032,  5821,  7907, 12649,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[59513,   577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,\n",
-       "         59513, 59513, 59513, 59513, 59513, 59513],\n",
-       "        [59513,  1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,\n",
-       "           817,   550,  7032,  5821,  7907, 12649]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"decoder_input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[577, 5891, 2, 3184, 16, 2542, 5, 1710, 0]\n",
-       "[1211, 3, 49, 9409, 1211, 3, 29140, 817, 3124, 817, 550, 7032, 5821, 7907, 12649, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(1, 3):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install sacrebleu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"sacrebleu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 46.750469682990165,\n",
-       " 'counts': [11, 6, 4, 3],\n",
-       " 'totals': [12, 11, 10, 9],\n",
-       " 'precisions': [91.67, 54.54, 40.0, 33.33],\n",
-       " 'bp': 0.9200444146293233,\n",
-       " 'sys_len': 12,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\n",
-    "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
-    "]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1.683602693167689,\n",
-       " 'counts': [1, 0, 0, 0],\n",
-       " 'totals': [4, 3, 2, 1],\n",
-       " 'precisions': [25.0, 16.67, 12.5, 12.5],\n",
-       " 'bp': 0.10539922456186433,\n",
-       " 'sys_len': 4,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This This This This\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0,\n",
-       " 'counts': [2, 1, 0, 0],\n",
-       " 'totals': [2, 1, 0, 0],\n",
-       " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
-       " 'bp': 0.004086771438464067,\n",
-       " 'sys_len': 2,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This plugin\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_preds):\n",
-    "    preds, labels = eval_preds\n",
-    "    # In case the model returns more than the prediction logits\n",
-    "    if isinstance(preds, tuple):\n",
-    "        preds = preds[0]\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
-    "\n",
-    "    # Replace -100s in the labels as we can't decode them\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Some simple post-processing\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "\n",
-    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
-    "    return {\"bleu\": result[\"score\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainingArguments\n",
-    "\n",
-    "args = Seq2SeqTrainingArguments(\n",
-    "    f\"marian-finetuned-kde4-en-to-fr\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    per_device_train_batch_size=32,\n",
-    "    per_device_eval_batch_size=64,\n",
-    "    weight_decay=0.01,\n",
-    "    save_total_limit=3,\n",
-    "    num_train_epochs=3,\n",
-    "    predict_with_generate=True,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainer\n",
-    "\n",
-    "trainer = Seq2SeqTrainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 1.6964408159255981,\n",
-       " 'eval_bleu': 39.26865061007616,\n",
-       " 'eval_runtime': 965.8884,\n",
-       " 'eval_samples_per_second': 21.76,\n",
-       " 'eval_steps_per_second': 0.341}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 0.8558505773544312,\n",
-       " 'eval_bleu': 52.94161337775576,\n",
-       " 'eval_runtime': 714.2576,\n",
-       " 'eval_samples_per_second': 29.426,\n",
-       " 'eval_steps_per_second': 0.461,\n",
-       " 'epoch': 3.0}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/marian-finetuned-kde4-en-to-fr/commit/3601d621e3baae2bc63d3311452535f8f58f6ef3'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(tags=\"translation\", commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/marian-finetuned-kde4-en-to-fr-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess(predictions, labels):\n",
-    "    predictions = predictions.cpu().numpy()\n",
-    "    labels = labels.cpu().numpy()\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "\n",
-    "    # Replace -100 in the labels as we can't decode them.\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Some simple post-processing\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "    return decoded_preds, decoded_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "epoch 0, BLEU score: 53.47\n",
-       "epoch 1, BLEU score: 54.24\n",
-       "epoch 2, BLEU score: 54.44"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            generated_tokens = accelerator.unwrap_model(model).generate(\n",
-    "                batch[\"input_ids\"],\n",
-    "                attention_mask=batch[\"attention_mask\"],\n",
-    "                max_length=128,\n",
-    "            )\n",
-    "        labels = batch[\"labels\"]\n",
-    "\n",
-    "        # Necessary to pad predictions and labels for being gathered\n",
-    "        generated_tokens = accelerator.pad_across_processes(\n",
-    "            generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n",
-    "        )\n",
-    "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
-    "\n",
-    "        predictions_gathered = accelerator.gather(generated_tokens)\n",
-    "        labels_gathered = accelerator.gather(labels)\n",
-    "\n",
-    "        decoded_preds, decoded_labels = postprocess(predictions_gathered, labels_gathered)\n",
-    "        metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
-    "\n",
-    "    results = metric.compute()\n",
-    "    print(f\"epoch {epoch}, BLEU score: {results['score']:.2f}\")\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut, développer les fils de discussion'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le module externe d'importation OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "翻訳 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section4_tf.ipynb b/course/ja/chapter7/section4_tf.ipynb
deleted file mode 100644
index dc7c9f38..00000000
--- a/course/ja/chapter7/section4_tf.ipynb
+++ /dev/null
@@ -1,728 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 翻訳 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 210173\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 189155\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 21018\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
-    "split_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Default to expanded threads',\n",
-       " 'fr': 'Par défaut, développer les fils de discussion'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][1][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut pour les threads élargis'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Unable to import %1 using the OFX importer plugin. This file is not the correct format.',\n",
-       " 'fr': \"Impossible d'importer %1 en utilisant le module d'extension d'importation OFX. Ce fichier n'a pas un format correct.\"}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][172][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le plugin d'importateur OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
-    "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
-    "\n",
-    "inputs = tokenizer(en_sentence)\n",
-    "with tokenizer.as_target_tokenizer():\n",
-    "    targets = tokenizer(fr_sentence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Par', '▁dé', 'f', 'aut', ',', '▁dé', 've', 'lop', 'per', '▁les', '▁fil', 's', '▁de', '▁discussion', '</s>']\n",
-       "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong_targets = tokenizer(fr_sentence)\n",
-    "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
-    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 128\n",
-    "max_target_length = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
-    "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
-    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
-    "\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = split_datasets.map(\n",
-    "    preprocess_function,\n",
-    "    batched=True,\n",
-    "    remove_columns=split_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'input_ids', 'labels', 'decoder_input_ids'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
-    "batch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,  -100,\n",
-       "          -100,  -100,  -100,  -100,  -100,  -100],\n",
-       "        [ 1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,   817,\n",
-       "           550,  7032,  5821,  7907, 12649,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[59513,   577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,\n",
-       "         59513, 59513, 59513, 59513, 59513, 59513],\n",
-       "        [59513,  1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,\n",
-       "           817,   550,  7032,  5821,  7907, 12649]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"decoder_input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[577, 5891, 2, 3184, 16, 2542, 5, 1710, 0]\n",
-       "[1211, 3, 49, 9409, 1211, 3, 29140, 817, 3124, 817, 550, 7032, 5821, 7907, 12649, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(1, 3):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install sacrebleu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"sacrebleu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 46.750469682990165,\n",
-       " 'counts': [11, 6, 4, 3],\n",
-       " 'totals': [12, 11, 10, 9],\n",
-       " 'precisions': [91.67, 54.54, 40.0, 33.33],\n",
-       " 'bp': 0.9200444146293233,\n",
-       " 'sys_len': 12,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\n",
-    "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
-    "]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1.683602693167689,\n",
-       " 'counts': [1, 0, 0, 0],\n",
-       " 'totals': [4, 3, 2, 1],\n",
-       " 'precisions': [25.0, 16.67, 12.5, 12.5],\n",
-       " 'bp': 0.10539922456186433,\n",
-       " 'sys_len': 4,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This This This This\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0,\n",
-       " 'counts': [2, 1, 0, 0],\n",
-       " 'totals': [2, 1, 0, 0],\n",
-       " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
-       " 'bp': 0.004086771438464067,\n",
-       " 'sys_len': 2,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This plugin\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics():\n",
-    "    all_preds = []\n",
-    "    all_labels = []\n",
-    "    sampled_dataset = tokenized_datasets[\"validation\"].shuffle().select(range(200))\n",
-    "    tf_generate_dataset = sampled_dataset.to_tf_dataset(\n",
-    "        columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "        collate_fn=data_collator,\n",
-    "        shuffle=False,\n",
-    "        batch_size=4,\n",
-    "    )\n",
-    "    for batch in tf_generate_dataset:\n",
-    "        predictions = model.generate(\n",
-    "            input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
-    "        )\n",
-    "        decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "        labels = batch[\"labels\"].numpy()\n",
-    "        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "        decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "        decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "        all_preds.extend(decoded_preds)\n",
-    "        all_labels.extend(decoded_labels)\n",
-    "\n",
-    "    result = metric.compute(predictions=all_preds, references=all_labels)\n",
-    "    return {\"bleu\": result[\"score\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(compute_metrics())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=\"marian-finetuned-kde4-en-to-fr\", tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_eval_dataset,\n",
-    "    callbacks=[callback],\n",
-    "    epochs=num_epochs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(compute_metrics())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut, développer les fils de discussion'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le module externe d'importation OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "翻訳 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section5_pt.ipynb b/course/ja/chapter7/section5_pt.ipynb
deleted file mode 100644
index 36bcfabb..00000000
--- a/course/ja/chapter7/section5_pt.ipynb
+++ /dev/null
@@ -1,1035 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 要約 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 200000\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "spanish_dataset = load_dataset(\"amazon_reviews_multi\", \"es\")\n",
-    "english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n",
-    "english_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Worked in front position, not rear'\n",
-       "'>> Review: 3 stars because these are not rear brakes as stated in the item description. At least the mount adapter only worked on the front fork of the bike that I got it for.'\n",
-       "\n",
-       "'>> Title: meh'\n",
-       "'>> Review: Does it’s job and it’s gorgeous but mine is falling apart, I had to basically put it together again with hot glue'\n",
-       "\n",
-       "'>> Title: Can\\'t beat these for the money'\n",
-       "'>> Review: Bought this for handling miscellaneous aircraft parts and hanger \"stuff\" that I needed to organize; it really fit the bill. The unit arrived quickly, was well packaged and arrived intact (always a good sign). There are five wall mounts-- three on the top and two on the bottom. I wanted to mount it on the wall, so all I had to do was to remove the top two layers of plastic drawers, as well as the bottom corner drawers, place it when I wanted and mark it; I then used some of the new plastic screw in wall anchors (the 50 pound variety) and it easily mounted to the wall. Some have remarked that they wanted dividers for the drawers, and that they made those. Good idea. My application was that I needed something that I can see the contents at about eye level, so I wanted the fuller-sized drawers. I also like that these are the new plastic that doesn\\'t get brittle and split like my older plastic drawers did. I like the all-plastic construction. It\\'s heavy duty enough to hold metal parts, but being made of plastic it\\'s not as heavy as a metal frame, so you can easily mount it to the wall and still load it up with heavy stuff, or light stuff. No problem there. For the money, you can\\'t beat it. Best one of these I\\'ve bought to date-- and I\\'ve been using some version of these for over forty years.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def show_samples(dataset, num_samples=3, seed=42):\n",
-    "    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n",
-    "    for example in sample:\n",
-    "        print(f\"\\n'>> Title: {example['review_title']}'\")\n",
-    "        print(f\"'>> Review: {example['review_body']}'\")\n",
-    "\n",
-    "\n",
-    "show_samples(english_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "home                      17679\n",
-       "apparel                   15951\n",
-       "wireless                  15717\n",
-       "other                     13418\n",
-       "beauty                    12091\n",
-       "drugstore                 11730\n",
-       "kitchen                   10382\n",
-       "toy                        8745\n",
-       "sports                     8277\n",
-       "automotive                 7506\n",
-       "lawn_and_garden            7327\n",
-       "home_improvement           7136\n",
-       "pet_products               7082\n",
-       "digital_ebook_purchase     6749\n",
-       "pc                         6401\n",
-       "electronics                6186\n",
-       "office_product             5521\n",
-       "shoes                      5197\n",
-       "grocery                    4730\n",
-       "book                       3756\n",
-       "Name: product_category, dtype: int64"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "english_dataset.set_format(\"pandas\")\n",
-    "english_df = english_dataset[\"train\"][:]\n",
-    "# Show counts for top 20 products\n",
-    "english_df[\"product_category\"].value_counts()[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_books(example):\n",
-    "    return (\n",
-    "        example[\"product_category\"] == \"book\"\n",
-    "        or example[\"product_category\"] == \"digital_ebook_purchase\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "english_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: I\\'m dissapointed.'\n",
-       "'>> Review: I guess I had higher expectations for this book from the reviews. I really thought I\\'d at least like it. The plot idea was great. I loved Ash but, it just didnt go anywhere. Most of the book was about their radio show and talking to callers. I wanted the author to dig deeper so we could really get to know the characters. All we know about Grace is that she is attractive looking, Latino and is kind of a brat. I\\'m dissapointed.'\n",
-       "\n",
-       "'>> Title: Good art, good price, poor design'\n",
-       "'>> Review: I had gotten the DC Vintage calendar the past two years, but it was on backorder forever this year and I saw they had shrunk the dimensions for no good reason. This one has good art choices but the design has the fold going through the picture, so it\\'s less aesthetically pleasing, especially if you want to keep a picture to hang. For the price, a good calendar'\n",
-       "\n",
-       "'>> Title: Helpful'\n",
-       "'>> Review: Nearly all the tips useful and. I consider myself an intermediate to advanced user of OneNote. I would highly recommend.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spanish_books = spanish_dataset.filter(filter_books)\n",
-    "english_books = english_dataset.filter(filter_books)\n",
-    "show_samples(english_books)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Easy to follow!!!!'\n",
-       "'>> Review: I loved The dash diet weight loss Solution. Never hungry. I would recommend this diet. Also the menus are well rounded. Try it. Has lots of the information need thanks.'\n",
-       "\n",
-       "'>> Title: PARCIALMENTE DAÑADO'\n",
-       "'>> Review: Me llegó el día que tocaba, junto a otros libros que pedí, pero la caja llegó en mal estado lo cual dañó las esquinas de los libros porque venían sin protección (forro).'\n",
-       "\n",
-       "'>> Title: no lo he podido descargar'\n",
-       "'>> Review: igual que el anterior'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import concatenate_datasets, DatasetDict\n",
-    "\n",
-    "books_dataset = DatasetDict()\n",
-    "\n",
-    "for split in english_books.keys():\n",
-    "    books_dataset[split] = concatenate_datasets(\n",
-    "        [english_books[split], spanish_books[split]]\n",
-    "    )\n",
-    "    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n",
-    "\n",
-    "# Peek at a few examples\n",
-    "show_samples(books_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"google/mt5-small\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [336, 259, 28387, 11807, 287, 62893, 295, 12507, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"I loved reading the Hunger Games!\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁I', '▁', 'loved', '▁reading', '▁the', '▁Hung', 'er', '▁Games', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs.input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 512\n",
-    "max_target_length = 30\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    model_inputs = tokenizer(\n",
-    "        examples[\"review_body\"], max_length=max_input_length, truncation=True\n",
-    "    )\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(\n",
-    "            examples[\"review_title\"], max_length=max_target_length, truncation=True\n",
-    "        )\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generated_summary = \"I absolutely loved reading the Hunger Games\"\n",
-    "reference_summary = \"I loved reading the Hunger Games\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install rouge_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "rouge_score = evaluate.load(\"rouge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rouge2': AggregateScore(low=Score(precision=0.67, recall=0.8, fmeasure=0.73), mid=Score(precision=0.67, recall=0.8, fmeasure=0.73), high=Score(precision=0.67, recall=0.8, fmeasure=0.73)),\n",
-       " 'rougeL': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rougeLsum': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92))}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = rouge_score.compute(\n",
-    "    predictions=[generated_summary], references=[reference_summary]\n",
-    ")\n",
-    "scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Score(precision=0.86, recall=1.0, fmeasure=0.92)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores[\"rouge1\"].mid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install nltk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk\n",
-    "\n",
-    "nltk.download(\"punkt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'I grew up reading Koontz, and years ago, I stopped,convinced i had \"outgrown\" him.'\n",
-       "'Still,when a friend was looking for something suspenseful too read, I suggested Koontz.'\n",
-       "'She found Strangers.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "\n",
-    "\n",
-    "def three_sentence_summary(text):\n",
-    "    return \"\\n\".join(sent_tokenize(text)[:3])\n",
-    "\n",
-    "\n",
-    "print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_baseline(dataset, metric):\n",
-    "    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n",
-    "    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': 16.74, 'rouge2': 8.83, 'rougeL': 15.6, 'rougeLsum': 15.96}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n",
-    "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
-    "rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n",
-    "rouge_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainingArguments\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_train_epochs = 8\n",
-    "# Show the training loss with every epoch\n",
-    "logging_steps = len(tokenized_datasets[\"train\"]) // batch_size\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "args = Seq2SeqTrainingArguments(\n",
-    "    output_dir=f\"{model_name}-finetuned-amazon-en-es\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    learning_rate=5.6e-5,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    weight_decay=0.01,\n",
-    "    save_total_limit=3,\n",
-    "    num_train_epochs=num_train_epochs,\n",
-    "    predict_with_generate=True,\n",
-    "    logging_steps=logging_steps,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    # Decode generated summaries into text\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "    # Replace -100 in the labels as we can't decode them\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    # Decode reference summaries into text\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "    # ROUGE expects a newline after each sentence\n",
-    "    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
-    "    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n",
-    "    # Compute ROUGE scores\n",
-    "    result = rouge_score.compute(\n",
-    "        predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n",
-    "    )\n",
-    "    # Extract the median scores\n",
-    "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "    return {k: round(v, 4) for k, v in result.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
-    "    books_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[  1494,    259,   8622,    390,    259,    262,   2316,   3435,    955,\n",
-       "            772,    281,    772,   1617,    263,    305,  14701,    260,   1385,\n",
-       "           3031,    259,  24146,    332,   1037,    259,  43906,    305,    336,\n",
-       "            260,      1,      0,      0,      0,      0,      0,      0],\n",
-       "        [   259,  27531,  13483,    259,   7505,    260, 112240,  15192,    305,\n",
-       "          53198,    276,    259,  74060,    263,    260,    459,  25640,    776,\n",
-       "           2119,    336,    259,   2220,    259,  18896,    288,   4906,    288,\n",
-       "           1037,   3931,    260,   7083, 101476,   1143,    260,      1]]), 'labels': tensor([[ 7483,   259,  2364, 15695,     1,  -100],\n",
-       "        [  259, 27531, 13483,   259,  7505,     1]]), 'decoder_input_ids': tensor([[    0,  7483,   259,  2364, 15695,     1],\n",
-       "        [    0,   259, 27531, 13483,   259,  7505]])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n",
-    "data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainer\n",
-    "\n",
-    "trainer = Seq2SeqTrainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 3.028524398803711,\n",
-       " 'eval_rouge1': 16.9728,\n",
-       " 'eval_rouge2': 8.2969,\n",
-       " 'eval_rougeL': 16.8366,\n",
-       " 'eval_rougeLsum': 16.851,\n",
-       " 'eval_gen_len': 10.1597,\n",
-       " 'eval_runtime': 6.1054,\n",
-       " 'eval_samples_per_second': 38.982,\n",
-       " 'eval_steps_per_second': 4.914}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets.set_format(\"torch\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "batch_size = 8\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=batch_size,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=batch_size\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 10\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess_text(preds, labels):\n",
-    "    preds = [pred.strip() for pred in preds]\n",
-    "    labels = [label.strip() for label in labels]\n",
-    "\n",
-    "    # ROUGE expects a newline after each sentence\n",
-    "    preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n",
-    "    labels = [\"\\n\".join(nltk.sent_tokenize(label)) for label in labels]\n",
-    "\n",
-    "    return preds, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'lewtun/mt5-finetuned-amazon-en-es-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import get_full_repo_name\n",
-    "\n",
-    "model_name = \"test-bert-finetuned-squad-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "output_dir = \"results-mt5-finetuned-squad-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Epoch 0: {'rouge1': 5.6351, 'rouge2': 1.1625, 'rougeL': 5.4866, 'rougeLsum': 5.5005}\n",
-       "Epoch 1: {'rouge1': 9.8646, 'rouge2': 3.4106, 'rougeL': 9.9439, 'rougeLsum': 9.9306}\n",
-       "Epoch 2: {'rouge1': 11.0872, 'rouge2': 3.3273, 'rougeL': 11.0508, 'rougeLsum': 10.9468}\n",
-       "Epoch 3: {'rouge1': 11.8587, 'rouge2': 4.8167, 'rougeL': 11.7986, 'rougeLsum': 11.7518}\n",
-       "Epoch 4: {'rouge1': 12.9842, 'rouge2': 5.5887, 'rougeL': 12.7546, 'rougeLsum': 12.7029}\n",
-       "Epoch 5: {'rouge1': 13.4628, 'rouge2': 6.4598, 'rougeL': 13.312, 'rougeLsum': 13.2913}\n",
-       "Epoch 6: {'rouge1': 12.9131, 'rouge2': 5.8914, 'rougeL': 12.6896, 'rougeLsum': 12.5701}\n",
-       "Epoch 7: {'rouge1': 13.3079, 'rouge2': 6.2994, 'rougeL': 13.1536, 'rougeLsum': 13.1194}\n",
-       "Epoch 8: {'rouge1': 13.96, 'rouge2': 6.5998, 'rougeL': 13.9123, 'rougeLsum': 13.7744}\n",
-       "Epoch 9: {'rouge1': 14.1192, 'rouge2': 7.0059, 'rougeL': 14.1172, 'rougeLsum': 13.9509}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for step, batch in enumerate(train_dataloader):\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            generated_tokens = accelerator.unwrap_model(model).generate(\n",
-    "                batch[\"input_ids\"],\n",
-    "                attention_mask=batch[\"attention_mask\"],\n",
-    "            )\n",
-    "\n",
-    "            generated_tokens = accelerator.pad_across_processes(\n",
-    "                generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n",
-    "            )\n",
-    "            labels = batch[\"labels\"]\n",
-    "\n",
-    "            # If we did not pad to max length, we need to pad the labels too\n",
-    "            labels = accelerator.pad_across_processes(\n",
-    "                batch[\"labels\"], dim=1, pad_index=tokenizer.pad_token_id\n",
-    "            )\n",
-    "\n",
-    "            generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()\n",
-    "            labels = accelerator.gather(labels).cpu().numpy()\n",
-    "\n",
-    "            # Replace -100 in the labels as we can't decode them\n",
-    "            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "            if isinstance(generated_tokens, tuple):\n",
-    "                generated_tokens = generated_tokens[0]\n",
-    "            decoded_preds = tokenizer.batch_decode(\n",
-    "                generated_tokens, skip_special_tokens=True\n",
-    "            )\n",
-    "            decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "            decoded_preds, decoded_labels = postprocess_text(\n",
-    "                decoded_preds, decoded_labels\n",
-    "            )\n",
-    "\n",
-    "            rouge_score.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
-    "\n",
-    "    # Compute metrics\n",
-    "    result = rouge_score.compute()\n",
-    "    # Extract the median ROUGE scores\n",
-    "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "    result = {k: round(v, 4) for k, v in result.items()}\n",
-    "    print(f\"Epoch {epoch}:\", result)\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-es\"\n",
-    "summarizer = pipeline(\"summarization\", model=hub_model_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_summary(idx):\n",
-    "    review = books_dataset[\"test\"][idx][\"review_body\"]\n",
-    "    title = books_dataset[\"test\"][idx][\"review_title\"]\n",
-    "    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n",
-    "    print(f\"'>>> Review: {review}'\")\n",
-    "    print(f\"\\n'>>> Title: {title}'\")\n",
-    "    print(f\"\\n'>>> Summary: {summary}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Nothing special at all about this product... the book is too small and stiff and hard to write in. The huge sticker on the back doesn’t come off and looks super tacky. I would not purchase this again. I could have just bought a journal from the dollar store and it would be basically the same thing. It’s also really expensive for what it is.'\n",
-       "\n",
-       "'>>> Title: Not impressed at all... buy something else'\n",
-       "\n",
-       "'>>> Summary: Nothing special at all about this product'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Es una trilogia que se hace muy facil de leer. Me ha gustado, no me esperaba el final para nada'\n",
-       "\n",
-       "'>>> Title: Buena literatura para adolescentes'\n",
-       "\n",
-       "'>>> Summary: Muy facil de leer'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(0)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "要約 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section5_tf.ipynb b/course/ja/chapter7/section5_tf.ipynb
deleted file mode 100644
index 4c207c81..00000000
--- a/course/ja/chapter7/section5_tf.ipynb
+++ /dev/null
@@ -1,784 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 要約 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 200000\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "spanish_dataset = load_dataset(\"amazon_reviews_multi\", \"es\")\n",
-    "english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n",
-    "english_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Worked in front position, not rear'\n",
-       "'>> Review: 3 stars because these are not rear brakes as stated in the item description. At least the mount adapter only worked on the front fork of the bike that I got it for.'\n",
-       "\n",
-       "'>> Title: meh'\n",
-       "'>> Review: Does it’s job and it’s gorgeous but mine is falling apart, I had to basically put it together again with hot glue'\n",
-       "\n",
-       "'>> Title: Can\\'t beat these for the money'\n",
-       "'>> Review: Bought this for handling miscellaneous aircraft parts and hanger \"stuff\" that I needed to organize; it really fit the bill. The unit arrived quickly, was well packaged and arrived intact (always a good sign). There are five wall mounts-- three on the top and two on the bottom. I wanted to mount it on the wall, so all I had to do was to remove the top two layers of plastic drawers, as well as the bottom corner drawers, place it when I wanted and mark it; I then used some of the new plastic screw in wall anchors (the 50 pound variety) and it easily mounted to the wall. Some have remarked that they wanted dividers for the drawers, and that they made those. Good idea. My application was that I needed something that I can see the contents at about eye level, so I wanted the fuller-sized drawers. I also like that these are the new plastic that doesn\\'t get brittle and split like my older plastic drawers did. I like the all-plastic construction. It\\'s heavy duty enough to hold metal parts, but being made of plastic it\\'s not as heavy as a metal frame, so you can easily mount it to the wall and still load it up with heavy stuff, or light stuff. No problem there. For the money, you can\\'t beat it. Best one of these I\\'ve bought to date-- and I\\'ve been using some version of these for over forty years.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def show_samples(dataset, num_samples=3, seed=42):\n",
-    "    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n",
-    "    for example in sample:\n",
-    "        print(f\"\\n'>> Title: {example['review_title']}'\")\n",
-    "        print(f\"'>> Review: {example['review_body']}'\")\n",
-    "\n",
-    "\n",
-    "show_samples(english_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "home                      17679\n",
-       "apparel                   15951\n",
-       "wireless                  15717\n",
-       "other                     13418\n",
-       "beauty                    12091\n",
-       "drugstore                 11730\n",
-       "kitchen                   10382\n",
-       "toy                        8745\n",
-       "sports                     8277\n",
-       "automotive                 7506\n",
-       "lawn_and_garden            7327\n",
-       "home_improvement           7136\n",
-       "pet_products               7082\n",
-       "digital_ebook_purchase     6749\n",
-       "pc                         6401\n",
-       "electronics                6186\n",
-       "office_product             5521\n",
-       "shoes                      5197\n",
-       "grocery                    4730\n",
-       "book                       3756\n",
-       "Name: product_category, dtype: int64"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "english_dataset.set_format(\"pandas\")\n",
-    "english_df = english_dataset[\"train\"][:]\n",
-    "# Show counts for top 20 products\n",
-    "english_df[\"product_category\"].value_counts()[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_books(example):\n",
-    "    return (\n",
-    "        example[\"product_category\"] == \"book\"\n",
-    "        or example[\"product_category\"] == \"digital_ebook_purchase\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "english_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: I\\'m dissapointed.'\n",
-       "'>> Review: I guess I had higher expectations for this book from the reviews. I really thought I\\'d at least like it. The plot idea was great. I loved Ash but, it just didnt go anywhere. Most of the book was about their radio show and talking to callers. I wanted the author to dig deeper so we could really get to know the characters. All we know about Grace is that she is attractive looking, Latino and is kind of a brat. I\\'m dissapointed.'\n",
-       "\n",
-       "'>> Title: Good art, good price, poor design'\n",
-       "'>> Review: I had gotten the DC Vintage calendar the past two years, but it was on backorder forever this year and I saw they had shrunk the dimensions for no good reason. This one has good art choices but the design has the fold going through the picture, so it\\'s less aesthetically pleasing, especially if you want to keep a picture to hang. For the price, a good calendar'\n",
-       "\n",
-       "'>> Title: Helpful'\n",
-       "'>> Review: Nearly all the tips useful and. I consider myself an intermediate to advanced user of OneNote. I would highly recommend.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spanish_books = spanish_dataset.filter(filter_books)\n",
-    "english_books = english_dataset.filter(filter_books)\n",
-    "show_samples(english_books)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Easy to follow!!!!'\n",
-       "'>> Review: I loved The dash diet weight loss Solution. Never hungry. I would recommend this diet. Also the menus are well rounded. Try it. Has lots of the information need thanks.'\n",
-       "\n",
-       "'>> Title: PARCIALMENTE DAÑADO'\n",
-       "'>> Review: Me llegó el día que tocaba, junto a otros libros que pedí, pero la caja llegó en mal estado lo cual dañó las esquinas de los libros porque venían sin protección (forro).'\n",
-       "\n",
-       "'>> Title: no lo he podido descargar'\n",
-       "'>> Review: igual que el anterior'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import concatenate_datasets, DatasetDict\n",
-    "\n",
-    "books_dataset = DatasetDict()\n",
-    "\n",
-    "for split in english_books.keys():\n",
-    "    books_dataset[split] = concatenate_datasets(\n",
-    "        [english_books[split], spanish_books[split]]\n",
-    "    )\n",
-    "    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n",
-    "\n",
-    "# Peek at a few examples\n",
-    "show_samples(books_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"google/mt5-small\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [336, 259, 28387, 11807, 287, 62893, 295, 12507, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"I loved reading the Hunger Games!\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁I', '▁', 'loved', '▁reading', '▁the', '▁Hung', 'er', '▁Games', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs.input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 512\n",
-    "max_target_length = 30\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    model_inputs = tokenizer(\n",
-    "        examples[\"review_body\"], max_length=max_input_length, truncation=True\n",
-    "    )\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(\n",
-    "            examples[\"review_title\"], max_length=max_target_length, truncation=True\n",
-    "        )\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generated_summary = \"I absolutely loved reading the Hunger Games\"\n",
-    "reference_summary = \"I loved reading the Hunger Games\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install rouge_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "rouge_score = evaluate.load(\"rouge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rouge2': AggregateScore(low=Score(precision=0.67, recall=0.8, fmeasure=0.73), mid=Score(precision=0.67, recall=0.8, fmeasure=0.73), high=Score(precision=0.67, recall=0.8, fmeasure=0.73)),\n",
-       " 'rougeL': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rougeLsum': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92))}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = rouge_score.compute(\n",
-    "    predictions=[generated_summary], references=[reference_summary]\n",
-    ")\n",
-    "scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Score(precision=0.86, recall=1.0, fmeasure=0.92)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores[\"rouge1\"].mid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install nltk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk\n",
-    "\n",
-    "nltk.download(\"punkt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'I grew up reading Koontz, and years ago, I stopped,convinced i had \"outgrown\" him.'\n",
-       "'Still,when a friend was looking for something suspenseful too read, I suggested Koontz.'\n",
-       "'She found Strangers.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "\n",
-    "\n",
-    "def three_sentence_summary(text):\n",
-    "    return \"\\n\".join(sent_tokenize(text)[:3])\n",
-    "\n",
-    "\n",
-    "print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_baseline(dataset, metric):\n",
-    "    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n",
-    "    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': 16.74, 'rouge2': 8.83, 'rougeL': 15.6, 'rougeLsum': 15.96}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n",
-    "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
-    "rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n",
-    "rouge_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
-    "    books_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[  1494,    259,   8622,    390,    259,    262,   2316,   3435,    955,\n",
-       "            772,    281,    772,   1617,    263,    305,  14701,    260,   1385,\n",
-       "           3031,    259,  24146,    332,   1037,    259,  43906,    305,    336,\n",
-       "            260,      1,      0,      0,      0,      0,      0,      0],\n",
-       "        [   259,  27531,  13483,    259,   7505,    260, 112240,  15192,    305,\n",
-       "          53198,    276,    259,  74060,    263,    260,    459,  25640,    776,\n",
-       "           2119,    336,    259,   2220,    259,  18896,    288,   4906,    288,\n",
-       "           1037,   3931,    260,   7083, 101476,   1143,    260,      1]]), 'labels': tensor([[ 7483,   259,  2364, 15695,     1,  -100],\n",
-       "        [  259, 27531, 13483,   259,  7505,     1]]), 'decoder_input_ids': tensor([[    0,  7483,   259,  2364, 15695,     1],\n",
-       "        [    0,   259, 27531, 13483,   259,  7505]])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n",
-    "data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_epochs = 8\n",
-    "num_train_steps = len(tf_train_dataset) * num_train_epochs\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5.6e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=f\"{model_name}-finetuned-amazon-en-es\", tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback], epochs=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm\n",
-    "import numpy as np\n",
-    "\n",
-    "all_preds = []\n",
-    "all_labels = []\n",
-    "for batch in tqdm(tf_eval_dataset):\n",
-    "    predictions = model.generate(**batch)\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "    labels = batch[\"labels\"].numpy()\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
-    "    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n",
-    "    all_preds.extend(decoded_preds)\n",
-    "    all_labels.extend(decoded_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = rouge_score.compute(\n",
-    "    predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n",
-    ")\n",
-    "result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "{k: round(v, 4) for k, v in result.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-es\"\n",
-    "summarizer = pipeline(\"summarization\", model=hub_model_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_summary(idx):\n",
-    "    review = books_dataset[\"test\"][idx][\"review_body\"]\n",
-    "    title = books_dataset[\"test\"][idx][\"review_title\"]\n",
-    "    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n",
-    "    print(f\"'>>> Review: {review}'\")\n",
-    "    print(f\"\\n'>>> Title: {title}'\")\n",
-    "    print(f\"\\n'>>> Summary: {summary}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Nothing special at all about this product... the book is too small and stiff and hard to write in. The huge sticker on the back doesn’t come off and looks super tacky. I would not purchase this again. I could have just bought a journal from the dollar store and it would be basically the same thing. It’s also really expensive for what it is.'\n",
-       "\n",
-       "'>>> Title: Not impressed at all... buy something else'\n",
-       "\n",
-       "'>>> Summary: Nothing special at all about this product'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Es una trilogia que se hace muy facil de leer. Me ha gustado, no me esperaba el final para nada'\n",
-       "\n",
-       "'>>> Title: Buena literatura para adolescentes'\n",
-       "\n",
-       "'>>> Summary: Muy facil de leer'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(0)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "要約 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section6_pt.ipynb b/course/ja/chapter7/section6_pt.ipynb
deleted file mode 100644
index 3dd14fa6..00000000
--- a/course/ja/chapter7/section6_pt.ipynb
+++ /dev/null
@@ -1,895 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 因果言語モデルを一から学習 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def any_keyword_in_string(string, keywords):\n",
-    "    for keyword in keywords:\n",
-    "        if keyword in string:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "example_1 = \"import numpy as np\"\n",
-    "example_2 = \"import pandas as pd\"\n",
-    "\n",
-    "print(\n",
-    "    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm import tqdm\n",
-    "from datasets import Dataset\n",
-    "\n",
-    "\n",
-    "def filter_streaming_dataset(dataset, filters):\n",
-    "    filtered_dict = defaultdict(list)\n",
-    "    total = 0\n",
-    "    for sample in tqdm(iter(dataset)):\n",
-    "        total += 1\n",
-    "        if any_keyword_in_string(sample[\"content\"], filters):\n",
-    "            for k, v in sample.items():\n",
-    "                filtered_dict[k].append(v)\n",
-    "    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n",
-    "    return Dataset.from_dict(filtered_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.26% of data after filtering."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This cell will take a very long time to execute, so you should skip it and go to\n",
-    "# the next one!\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "split = \"train\"  # \"valid\"\n",
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "\n",
-    "data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n",
-    "filtered_data = filter_streaming_dataset(data, filters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 606720\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 3322\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, DatasetDict\n",
-    "\n",
-    "ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n",
-    "ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"validation\")\n",
-    "\n",
-    "raw_datasets = DatasetDict(\n",
-    "    {\n",
-    "        \"train\": ds_train,  # .shuffle().select(range(50000)),\n",
-    "        \"valid\": ds_valid,  # .shuffle().select(range(500))\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'REPO_NAME: kmike/scikit-learn'\n",
-       "'PATH: sklearn/utils/__init__.py'\n",
-       "'COPIES: 3'\n",
-       "'SIZE: 10094'\n",
-       "'''CONTENT: \"\"\"\n",
-       "The :mod:`sklearn.utils` module includes various utilites.\n",
-       "\"\"\"\n",
-       "\n",
-       "from collections import Sequence\n",
-       "\n",
-       "import numpy as np\n",
-       "from scipy.sparse import issparse\n",
-       "import warnings\n",
-       "\n",
-       "from .murmurhash import murm\n",
-       "LICENSE: bsd-3-clause'''"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for key in raw_datasets[\"train\"][0]:\n",
-    "    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs length: 34\n",
-       "Input chunk lengths: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 117, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 41]\n",
-       "Chunk mapping: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "context_length = 128\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n",
-    "\n",
-    "outputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][:2][\"content\"],\n",
-    "    truncation=True,\n",
-    "    max_length=context_length,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_length=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n",
-    "print(f\"Input chunk lengths: {(outputs['length'])}\")\n",
-    "print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 16702061\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 93164\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(element):\n",
-    "    outputs = tokenizer(\n",
-    "        element[\"content\"],\n",
-    "        truncation=True,\n",
-    "        max_length=context_length,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_length=True,\n",
-    "    )\n",
-    "    input_batch = []\n",
-    "    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
-    "        if length == context_length:\n",
-    "            input_batch.append(input_ids)\n",
-    "    return {\"input_ids\": input_batch}\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\n",
-    "    \"gpt2\",\n",
-    "    vocab_size=len(tokenizer),\n",
-    "    n_ctx=context_length,\n",
-    "    bos_token_id=tokenizer.bos_token_id,\n",
-    "    eos_token_id=tokenizer.eos_token_id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "GPT-2 size: 124.2M parameters"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = GPT2LMHeadModel(config)\n",
-    "model_size = sum(t.numel() for t in model.parameters())\n",
-    "print(f\"GPT-2 size: {model_size/1000**2:.1f}M parameters\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "input_ids shape: torch.Size([5, 128])\n",
-       "attention_mask shape: torch.Size([5, 128])\n",
-       "labels shape: torch.Size([5, 128])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out = data_collator([tokenized_datasets[\"train\"][i] for i in range(5)])\n",
-    "for key in out:\n",
-    "    print(f\"{key} shape: {out[key].shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer, TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    output_dir=\"codeparrot-ds\",\n",
-    "    per_device_train_batch_size=32,\n",
-    "    per_device_eval_batch_size=32,\n",
-    "    evaluation_strategy=\"steps\",\n",
-    "    eval_steps=5_000,\n",
-    "    logging_steps=5_000,\n",
-    "    gradient_accumulation_steps=8,\n",
-    "    num_train_epochs=1,\n",
-    "    weight_decay=0.1,\n",
-    "    warmup_steps=1_000,\n",
-    "    lr_scheduler_type=\"cosine\",\n",
-    "    learning_rate=5e-4,\n",
-    "    save_steps=5_000,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    tokenizer=tokenizer,\n",
-    "    args=args,\n",
-    "    data_collator=data_collator,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"valid\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import pipeline\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=\"huggingface-course/codeparrot-ds\", device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create scatter plot with x, y\n",
-       "plt.scatter(x, y)\n",
-       "\n",
-       "# create scatter"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create scatter plot with x, y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create dataframe from x and y\n",
-       "df = pd.DataFrame({'x': x, 'y': y})\n",
-       "df.insert(0,'x', x)\n",
-       "for"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create dataframe from x and y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# dataframe with profession, income and name\n",
-       "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-       "\n",
-       "# calculate the mean income per profession\n",
-       "profession = df.groupby(['profession']).mean()\n",
-       "\n",
-       "# compute the"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# dataframe with profession, income and name\n",
-    "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-    "\n",
-    "# calculate the mean income per profession\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# import random forest regressor from scikit-learn\n",
-       "from sklearn.ensemble import RandomForestRegressor\n",
-       "\n",
-       "# fit random forest model with 300 estimators on X, y:\n",
-       "rf = RandomForestRegressor(n_estimators=300, random_state=random_state, max_depth=3)\n",
-       "rf.fit(X, y)\n",
-       "rf"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\n",
-    "# import random forest regressor from scikit-learn\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "# fit random forest model with 300 estimators on X, y:\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Keyword has not single token: testtest'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "keytoken_ids = []\n",
-    "for keyword in [\n",
-    "    \"plt\",\n",
-    "    \"pd\",\n",
-    "    \"sk\",\n",
-    "    \"fit\",\n",
-    "    \"predict\",\n",
-    "    \" plt\",\n",
-    "    \" pd\",\n",
-    "    \" sk\",\n",
-    "    \" fit\",\n",
-    "    \" predict\",\n",
-    "    \"testtest\",\n",
-    "]:\n",
-    "    ids = tokenizer([keyword]).input_ids[0]\n",
-    "    if len(ids) == 1:\n",
-    "        keytoken_ids.append(ids[0])\n",
-    "    else:\n",
-    "        print(f\"Keyword has not single token: {keyword}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.nn import CrossEntropyLoss\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "def keytoken_weighted_loss(inputs, logits, keytoken_ids, alpha=1.0):\n",
-    "    # Shift so that tokens < n predict n\n",
-    "    shift_labels = inputs[..., 1:].contiguous()\n",
-    "    shift_logits = logits[..., :-1, :].contiguous()\n",
-    "    # Calculate per-token loss\n",
-    "    loss_fct = CrossEntropyLoss(reduce=False)\n",
-    "    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n",
-    "    # Resize and average loss per sample\n",
-    "    loss_per_sample = loss.view(shift_logits.size(0), shift_logits.size(1)).mean(axis=1)\n",
-    "    # Calculate and scale weighting\n",
-    "    weights = torch.stack([(inputs == kt).float() for kt in keytoken_ids]).sum(\n",
-    "        axis=[0, 2]\n",
-    "    )\n",
-    "    weights = alpha * (1.0 + weights)\n",
-    "    # Calculate weighted average\n",
-    "    weighted_loss = (loss_per_sample * weights).mean()\n",
-    "    return weighted_loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data.dataloader import DataLoader\n",
-    "\n",
-    "tokenized_dataset.set_format(\"torch\")\n",
-    "train_dataloader = DataLoader(tokenized_dataset[\"train\"], batch_size=32, shuffle=True)\n",
-    "eval_dataloader = DataLoader(tokenized_dataset[\"valid\"], batch_size=32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weight_decay = 0.1\n",
-    "\n",
-    "\n",
-    "def get_grouped_params(model, no_decay=[\"bias\", \"LayerNorm.weight\"]):\n",
-    "    params_with_wd, params_without_wd = [], []\n",
-    "    for n, p in model.named_parameters():\n",
-    "        if any(nd in n for nd in no_decay):\n",
-    "            params_without_wd.append(p)\n",
-    "        else:\n",
-    "            params_with_wd.append(p)\n",
-    "    return [\n",
-    "        {\"params\": params_with_wd, \"weight_decay\": weight_decay},\n",
-    "        {\"params\": params_without_wd, \"weight_decay\": 0.0},\n",
-    "    ]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate():\n",
-    "    model.eval()\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(batch[\"input_ids\"], labels=batch[\"input_ids\"])\n",
-    "\n",
-    "        losses.append(accelerator.gather(outputs.loss))\n",
-    "    loss = torch.mean(torch.cat(losses))\n",
-    "    try:\n",
-    "        perplexity = torch.exp(loss)\n",
-    "    except OverflowError:\n",
-    "        perplexity = float(\"inf\")\n",
-    "    return loss.item(), perplexity.item()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = GPT2LMHeadModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(get_grouped_params(model), lr=5e-4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator(fp16=True)\n",
-    "\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 1\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    name=\"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/codeparrot-ds-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"codeparrot-ds-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"codeparrot-ds-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.934126853942871, 56057.14453125)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "gradient_accumulation_steps = 8\n",
-    "eval_steps = 5_000\n",
-    "\n",
-    "model.train()\n",
-    "completed_steps = 0\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    for step, batch in tqdm(\n",
-    "        enumerate(train_dataloader, start=1), total=num_training_steps\n",
-    "    ):\n",
-    "        logits = model(batch[\"input_ids\"]).logits\n",
-    "        loss = keytoken_weighted_loss(batch[\"input_ids\"], logits, keytoken_ids)\n",
-    "        if step % 100 == 0:\n",
-    "            accelerator.print(\n",
-    "                {\n",
-    "                    \"lr\": get_lr(),\n",
-    "                    \"samples\": step * samples_per_step,\n",
-    "                    \"steps\": completed_steps,\n",
-    "                    \"loss/train\": loss.item() * gradient_accumulation_steps,\n",
-    "                }\n",
-    "            )\n",
-    "        loss = loss / gradient_accumulation_steps\n",
-    "        accelerator.backward(loss)\n",
-    "        if step % gradient_accumulation_steps == 0:\n",
-    "            accelerator.clip_grad_norm_(model.parameters(), 1.0)\n",
-    "            optimizer.step()\n",
-    "            lr_scheduler.step()\n",
-    "            optimizer.zero_grad()\n",
-    "            completed_steps += 1\n",
-    "        if (step % (eval_steps * gradient_accumulation_steps)) == 0:\n",
-    "            eval_loss, perplexity = evaluate()\n",
-    "            accelerator.print({\"loss/eval\": eval_loss, \"perplexity\": perplexity})\n",
-    "            model.train()\n",
-    "            accelerator.wait_for_everyone()\n",
-    "            unwrapped_model = accelerator.unwrap_model(model)\n",
-    "            unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "            if accelerator.is_main_process:\n",
-    "                tokenizer.save_pretrained(output_dir)\n",
-    "                repo.push_to_hub(\n",
-    "                    commit_message=f\"Training in progress step {step}\", blocking=False\n",
-    "                )"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "因果言語モデルを一から学習 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section6_tf.ipynb b/course/ja/chapter7/section6_tf.ipynb
deleted file mode 100644
index d202546b..00000000
--- a/course/ja/chapter7/section6_tf.ipynb
+++ /dev/null
@@ -1,618 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 因果言語モデルを一から学習 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def any_keyword_in_string(string, keywords):\n",
-    "    for keyword in keywords:\n",
-    "        if keyword in string:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "example_1 = \"import numpy as np\"\n",
-    "example_2 = \"import pandas as pd\"\n",
-    "\n",
-    "print(\n",
-    "    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm import tqdm\n",
-    "from datasets import Dataset\n",
-    "\n",
-    "\n",
-    "def filter_streaming_dataset(dataset, filters):\n",
-    "    filtered_dict = defaultdict(list)\n",
-    "    total = 0\n",
-    "    for sample in tqdm(iter(dataset)):\n",
-    "        total += 1\n",
-    "        if any_keyword_in_string(sample[\"content\"], filters):\n",
-    "            for k, v in sample.items():\n",
-    "                filtered_dict[k].append(v)\n",
-    "    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n",
-    "    return Dataset.from_dict(filtered_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.26% of data after filtering."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This cell will take a very long time to execute, so you should skip it and go to\n",
-    "# the next one!\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "split = \"train\"  # \"valid\"\n",
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "\n",
-    "data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n",
-    "filtered_data = filter_streaming_dataset(data, filters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 606720\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 3322\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, DatasetDict\n",
-    "\n",
-    "ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n",
-    "ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"validation\")\n",
-    "\n",
-    "raw_datasets = DatasetDict(\n",
-    "    {\n",
-    "        \"train\": ds_train,  # .shuffle().select(range(50000)),\n",
-    "        \"valid\": ds_valid,  # .shuffle().select(range(500))\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'REPO_NAME: kmike/scikit-learn'\n",
-       "'PATH: sklearn/utils/__init__.py'\n",
-       "'COPIES: 3'\n",
-       "'SIZE: 10094'\n",
-       "'''CONTENT: \"\"\"\n",
-       "The :mod:`sklearn.utils` module includes various utilites.\n",
-       "\"\"\"\n",
-       "\n",
-       "from collections import Sequence\n",
-       "\n",
-       "import numpy as np\n",
-       "from scipy.sparse import issparse\n",
-       "import warnings\n",
-       "\n",
-       "from .murmurhash import murm\n",
-       "LICENSE: bsd-3-clause'''"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for key in raw_datasets[\"train\"][0]:\n",
-    "    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs length: 34\n",
-       "Input chunk lengths: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 117, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 41]\n",
-       "Chunk mapping: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "context_length = 128\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n",
-    "\n",
-    "outputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][:2][\"content\"],\n",
-    "    truncation=True,\n",
-    "    max_length=context_length,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_length=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n",
-    "print(f\"Input chunk lengths: {(outputs['length'])}\")\n",
-    "print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 16702061\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 93164\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(element):\n",
-    "    outputs = tokenizer(\n",
-    "        element[\"content\"],\n",
-    "        truncation=True,\n",
-    "        max_length=context_length,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_length=True,\n",
-    "    )\n",
-    "    input_batch = []\n",
-    "    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
-    "        if length == context_length:\n",
-    "            input_batch.append(input_ids)\n",
-    "    return {\"input_ids\": input_batch}\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFGPT2LMHeadModel, AutoConfig\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\n",
-    "    \"gpt2\",\n",
-    "    vocab_size=len(tokenizer),\n",
-    "    n_ctx=context_length,\n",
-    "    bos_token_id=tokenizer.bos_token_id,\n",
-    "    eos_token_id=tokenizer.eos_token_id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "_________________________________________________________________\n",
-       "Layer (type)                 Output Shape              Param #   \n",
-       "=================================================================\n",
-       "transformer (TFGPT2MainLayer multiple                  124242432 \n",
-       "=================================================================\n",
-       "Total params: 124,242,432\n",
-       "Trainable params: 124,242,432\n",
-       "Non-trainable params: 0\n",
-       "_________________________________________________________________"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFGPT2LMHeadModel(config)\n",
-    "model(model.dummy_inputs)  # Builds the model\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "input_ids shape: (5, 128)\n",
-       "attention_mask shape: (5, 128)\n",
-       "labels shape: (5, 128)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out = data_collator([tokenized_datasets[\"train\"][i] for i in range(5)])\n",
-    "for key in out:\n",
-    "    print(f\"{key} shape: {out[key].shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_dataset[\"valid\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=32,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "num_train_steps = len(tf_train_dataset)\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5e-5,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"codeparrot-ds\", tokenizer=tokenizer)\n",
-    "\n",
-    "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "course_model = TFGPT2LMHeadModel.from_pretrained(\"huggingface-course/codeparrot-ds\")\n",
-    "course_tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/codeparrot-ds\")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=course_model, tokenizer=course_tokenizer, device=0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create scatter plot with x, y\n",
-       "plt.scatter(x, y)\n",
-       "\n",
-       "# create scatter"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create scatter plot with x, y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create dataframe from x and y\n",
-       "df = pd.DataFrame({'x': x, 'y': y})\n",
-       "df.insert(0,'x', x)\n",
-       "for"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create dataframe from x and y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# dataframe with profession, income and name\n",
-       "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-       "\n",
-       "# calculate the mean income per profession\n",
-       "profession = df.groupby(['profession']).mean()\n",
-       "\n",
-       "# compute the"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# dataframe with profession, income and name\n",
-    "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-    "\n",
-    "# calculate the mean income per profession\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# import random forest regressor from scikit-learn\n",
-       "from sklearn.ensemble import RandomForestRegressor\n",
-       "\n",
-       "# fit random forest model with 300 estimators on X, y:\n",
-       "rf = RandomForestRegressor(n_estimators=300, random_state=random_state, max_depth=3)\n",
-       "rf.fit(X, y)\n",
-       "rf"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\n",
-    "# import random forest regressor from scikit-learn\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "# fit random forest model with 300 estimators on X, y:\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "因果言語モデルを一から学習 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section7_pt.ipynb b/course/ja/chapter7/section7_pt.ipynb
deleted file mode 100644
index 74279149..00000000
--- a/course/ja/chapter7/section7_pt.ipynb
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 質問応答 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 87599\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 10570\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Context: 'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'\n",
-       "Question: 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'\n",
-       "Answer: {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n",
-    "print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n",
-    "print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "    num_rows: 0\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}\n",
-       "{'text': ['Santa Clara, California', \"Levi's Stadium\", \"Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.\"], 'answer_start': [403, 355, 355]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][0][\"answers\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \"Super Bowl L\"), so that the logo could prominently feature the Arabic numerals 50.'\n",
-       "'Where did Super Bowl 50 take place?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][2][\"context\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, '\n",
-       "'the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin '\n",
-       "'Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms '\n",
-       "'upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred '\n",
-       "'Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a '\n",
-       "'replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette '\n",
-       "'Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues '\n",
-       "'and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context = raw_datasets[\"train\"][0][\"context\"]\n",
-    "question = raw_datasets[\"train\"][0][\"question\"]\n",
-    "\n",
-    "inputs = tokenizer(question, context)\n",
-    "tokenizer.decode(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basi [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP]. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "inputs.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"overflow_to_sample_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The 4 examples gave 19 features.'\n",
-       "'Here is where each comes from: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3].'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][2:6][\"question\"],\n",
-    "    raw_datasets[\"train\"][2:6][\"context\"],\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n",
-    "print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([83, 51, 19, 0, 0, 64, 27, 0, 34, 0, 0, 0, 67, 34, 0, 0, 0, 0, 0],\n",
-       " [85, 53, 21, 0, 0, 70, 33, 0, 40, 0, 0, 0, 68, 35, 0, 0, 0, 0, 0])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "answers = raw_datasets[\"train\"][2:6][\"answers\"]\n",
-    "start_positions = []\n",
-    "end_positions = []\n",
-    "\n",
-    "for i, offset in enumerate(inputs[\"offset_mapping\"]):\n",
-    "    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n",
-    "    answer = answers[sample_idx]\n",
-    "    start_char = answer[\"answer_start\"][0]\n",
-    "    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "    sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "    # Find the start and end of the context\n",
-    "    idx = 0\n",
-    "    while sequence_ids[idx] != 1:\n",
-    "        idx += 1\n",
-    "    context_start = idx\n",
-    "    while sequence_ids[idx] == 1:\n",
-    "        idx += 1\n",
-    "    context_end = idx - 1\n",
-    "\n",
-    "    # If the answer is not fully inside the context, label is (0, 0)\n",
-    "    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "        start_positions.append(0)\n",
-    "        end_positions.append(0)\n",
-    "    else:\n",
-    "        # Otherwise it's the start and end token positions\n",
-    "        idx = context_start\n",
-    "        while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "            idx += 1\n",
-    "        start_positions.append(idx - 1)\n",
-    "\n",
-    "        idx = context_end\n",
-    "        while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "            idx -= 1\n",
-    "        end_positions.append(idx + 1)\n",
-    "\n",
-    "start_positions, end_positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: the Main Building, labels give: the Main Building'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "start = start_positions[idx]\n",
-    "end = end_positions[idx]\n",
-    "labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n",
-    "\n",
-    "print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: a Marian place of prayer and reflection, decoded example: [CLS] What is the Grotto at Notre Dame? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grot [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 4\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n",
-    "print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_length = 384\n",
-    "stride = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_training_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    answers = examples[\"answers\"]\n",
-    "    start_positions = []\n",
-    "    end_positions = []\n",
-    "\n",
-    "    for i, offset in enumerate(offset_mapping):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        answer = answers[sample_idx]\n",
-    "        start_char = answer[\"answer_start\"][0]\n",
-    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "        # Find the start and end of the context\n",
-    "        idx = 0\n",
-    "        while sequence_ids[idx] != 1:\n",
-    "            idx += 1\n",
-    "        context_start = idx\n",
-    "        while sequence_ids[idx] == 1:\n",
-    "            idx += 1\n",
-    "        context_end = idx - 1\n",
-    "\n",
-    "        # If the answer is not fully inside the context, label is (0, 0)\n",
-    "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "            start_positions.append(0)\n",
-    "            end_positions.append(0)\n",
-    "        else:\n",
-    "            # Otherwise it's the start and end token positions\n",
-    "            idx = context_start\n",
-    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "                idx += 1\n",
-    "            start_positions.append(idx - 1)\n",
-    "\n",
-    "            idx = context_end\n",
-    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "                idx -= 1\n",
-    "            end_positions.append(idx + 1)\n",
-    "\n",
-    "    inputs[\"start_positions\"] = start_positions\n",
-    "    inputs[\"end_positions\"] = end_positions\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(87599, 88729)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_dataset = raw_datasets[\"train\"].map(\n",
-    "    preprocess_training_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"train\"]), len(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess_validation_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    example_ids = []\n",
-    "\n",
-    "    for i in range(len(inputs[\"input_ids\"])):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        example_ids.append(examples[\"id\"][sample_idx])\n",
-    "\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "        offset = inputs[\"offset_mapping\"][i]\n",
-    "        inputs[\"offset_mapping\"][i] = [\n",
-    "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
-    "        ]\n",
-    "\n",
-    "    inputs[\"example_id\"] = example_ids\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10570, 10822)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation_dataset = raw_datasets[\"validation\"].map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"validation\"]), len(validation_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "small_eval_set = raw_datasets[\"validation\"].select(range(100))\n",
-    "trained_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n",
-    "eval_set = small_eval_set.map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoModelForQuestionAnswering\n",
-    "\n",
-    "eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "eval_set_for_model.set_format(\"torch\")\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: eval_set_for_model[k].to(device) for k in eval_set_for_model.column_names}\n",
-    "trained_model = AutoModelForQuestionAnswering.from_pretrained(trained_checkpoint).to(\n",
-    "    device\n",
-    ")\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trained_model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_logits = outputs.start_logits.cpu().numpy()\n",
-    "end_logits = outputs.end_logits.cpu().numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "\n",
-    "example_to_features = collections.defaultdict(list)\n",
-    "for idx, feature in enumerate(eval_set):\n",
-    "    example_to_features[feature[\"example_id\"]].append(idx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "n_best = 20\n",
-    "max_answer_length = 30\n",
-    "predicted_answers = []\n",
-    "\n",
-    "for example in small_eval_set:\n",
-    "    example_id = example[\"id\"]\n",
-    "    context = example[\"context\"]\n",
-    "    answers = []\n",
-    "\n",
-    "    for feature_index in example_to_features[example_id]:\n",
-    "        start_logit = start_logits[feature_index]\n",
-    "        end_logit = end_logits[feature_index]\n",
-    "        offsets = eval_set[\"offset_mapping\"][feature_index]\n",
-    "\n",
-    "        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        for start_index in start_indexes:\n",
-    "            for end_index in end_indexes:\n",
-    "                # Skip answers that are not fully in the context\n",
-    "                if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                    continue\n",
-    "                # Skip answers with a length that is either < 0 or > max_answer_length.\n",
-    "                if (\n",
-    "                    end_index < start_index\n",
-    "                    or end_index - start_index + 1 > max_answer_length\n",
-    "                ):\n",
-    "                    continue\n",
-    "\n",
-    "                answers.append(\n",
-    "                    {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                )\n",
-    "\n",
-    "    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theoretical_answers = [\n",
-    "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\n",
-       "{'id': '56be4db0acb8001400a502ec', 'answers': {'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(predicted_answers[0])\n",
-    "print(theoretical_answers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "\n",
-    "def compute_metrics(start_logits, end_logits, features, examples):\n",
-    "    example_to_features = collections.defaultdict(list)\n",
-    "    for idx, feature in enumerate(features):\n",
-    "        example_to_features[feature[\"example_id\"]].append(idx)\n",
-    "\n",
-    "    predicted_answers = []\n",
-    "    for example in tqdm(examples):\n",
-    "        example_id = example[\"id\"]\n",
-    "        context = example[\"context\"]\n",
-    "        answers = []\n",
-    "\n",
-    "        # Loop through all features associated with that example\n",
-    "        for feature_index in example_to_features[example_id]:\n",
-    "            start_logit = start_logits[feature_index]\n",
-    "            end_logit = end_logits[feature_index]\n",
-    "            offsets = features[feature_index][\"offset_mapping\"]\n",
-    "\n",
-    "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            for start_index in start_indexes:\n",
-    "                for end_index in end_indexes:\n",
-    "                    # Skip answers that are not fully in the context\n",
-    "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                        continue\n",
-    "                    # Skip answers with a length that is either < 0 or > max_answer_length\n",
-    "                    if (\n",
-    "                        end_index < start_index\n",
-    "                        or end_index - start_index + 1 > max_answer_length\n",
-    "                    ):\n",
-    "                        continue\n",
-    "\n",
-    "                    answer = {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                    answers.append(answer)\n",
-    "\n",
-    "        # Select the answer with the best score\n",
-    "        if len(answers) > 0:\n",
-    "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "            predicted_answers.append(\n",
-    "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
-    "            )\n",
-    "        else:\n",
-    "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
-    "\n",
-    "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
-    "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    \"bert-finetuned-squad\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=train_dataset,\n",
-    "    eval_dataset=validation_dataset,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 81.18259224219489, 'f1': 88.67381321905516}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions, _, _ = trainer.predict(validation_dataset)\n",
-    "start_logits, end_logits = predictions\n",
-    "compute_metrics(start_logits, end_logits, validation_dataset, raw_datasets[\"validation\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/bert-finetuned-squad/commit/9dcee1fbc25946a6ed4bb32efb1bd71d5fa90b68'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "train_dataset.set_format(\"torch\")\n",
-    "validation_set = validation_dataset.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "validation_set.set_format(\"torch\")\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    train_dataset,\n",
-    "    shuffle=True,\n",
-    "    collate_fn=default_data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    validation_set, collate_fn=default_data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator(fp16=True)\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/bert-finetuned-squad-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"bert-finetuned-squad-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"bert-finetuned-squad-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for step, batch in enumerate(train_dataloader):\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    start_logits = []\n",
-    "    end_logits = []\n",
-    "    accelerator.print(\"Evaluation!\")\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        start_logits.append(accelerator.gather(outputs.start_logits).cpu().numpy())\n",
-    "        end_logits.append(accelerator.gather(outputs.end_logits).cpu().numpy())\n",
-    "\n",
-    "    start_logits = np.concatenate(start_logits)\n",
-    "    end_logits = np.concatenate(end_logits)\n",
-    "    start_logits = start_logits[: len(validation_dataset)]\n",
-    "    end_logits = end_logits[: len(validation_dataset)]\n",
-    "\n",
-    "    metrics = compute_metrics(\n",
-    "        start_logits, end_logits, validation_dataset, raw_datasets[\"validation\"]\n",
-    "    )\n",
-    "    print(f\"epoch {epoch}:\", metrics)\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accelerator.wait_for_everyone()\n",
-    "unwrapped_model = accelerator.unwrap_model(model)\n",
-    "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.9979003071784973,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-squad\"\n",
-    "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
-    "\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "質問応答 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter7/section7_tf.ipynb b/course/ja/chapter7/section7_tf.ipynb
deleted file mode 100644
index a6051a93..00000000
--- a/course/ja/chapter7/section7_tf.ipynb
+++ /dev/null
@@ -1,1056 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 質問応答 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 87599\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 10570\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Context: 'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'\n",
-       "Question: 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'\n",
-       "Answer: {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n",
-    "print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n",
-    "print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "    num_rows: 0\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}\n",
-       "{'text': ['Santa Clara, California', \"Levi's Stadium\", \"Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.\"], 'answer_start': [403, 355, 355]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][0][\"answers\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \"Super Bowl L\"), so that the logo could prominently feature the Arabic numerals 50.'\n",
-       "'Where did Super Bowl 50 take place?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][2][\"context\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, '\n",
-       "'the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin '\n",
-       "'Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms '\n",
-       "'upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred '\n",
-       "'Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a '\n",
-       "'replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette '\n",
-       "'Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues '\n",
-       "'and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context = raw_datasets[\"train\"][0][\"context\"]\n",
-    "question = raw_datasets[\"train\"][0][\"question\"]\n",
-    "\n",
-    "inputs = tokenizer(question, context)\n",
-    "tokenizer.decode(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basi [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP]. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "inputs.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"overflow_to_sample_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The 4 examples gave 19 features.'\n",
-       "'Here is where each comes from: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3].'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][2:6][\"question\"],\n",
-    "    raw_datasets[\"train\"][2:6][\"context\"],\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n",
-    "print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([83, 51, 19, 0, 0, 64, 27, 0, 34, 0, 0, 0, 67, 34, 0, 0, 0, 0, 0],\n",
-       " [85, 53, 21, 0, 0, 70, 33, 0, 40, 0, 0, 0, 68, 35, 0, 0, 0, 0, 0])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "answers = raw_datasets[\"train\"][2:6][\"answers\"]\n",
-    "start_positions = []\n",
-    "end_positions = []\n",
-    "\n",
-    "for i, offset in enumerate(inputs[\"offset_mapping\"]):\n",
-    "    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n",
-    "    answer = answers[sample_idx]\n",
-    "    start_char = answer[\"answer_start\"][0]\n",
-    "    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "    sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "    # Find the start and end of the context\n",
-    "    idx = 0\n",
-    "    while sequence_ids[idx] != 1:\n",
-    "        idx += 1\n",
-    "    context_start = idx\n",
-    "    while sequence_ids[idx] == 1:\n",
-    "        idx += 1\n",
-    "    context_end = idx - 1\n",
-    "\n",
-    "    # If the answer is not fully inside the context, label is (0, 0)\n",
-    "    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "        start_positions.append(0)\n",
-    "        end_positions.append(0)\n",
-    "    else:\n",
-    "        # Otherwise it's the start and end token positions\n",
-    "        idx = context_start\n",
-    "        while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "            idx += 1\n",
-    "        start_positions.append(idx - 1)\n",
-    "\n",
-    "        idx = context_end\n",
-    "        while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "            idx -= 1\n",
-    "        end_positions.append(idx + 1)\n",
-    "\n",
-    "start_positions, end_positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: the Main Building, labels give: the Main Building'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "start = start_positions[idx]\n",
-    "end = end_positions[idx]\n",
-    "labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n",
-    "\n",
-    "print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: a Marian place of prayer and reflection, decoded example: [CLS] What is the Grotto at Notre Dame? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grot [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 4\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n",
-    "print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_length = 384\n",
-    "stride = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_training_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    answers = examples[\"answers\"]\n",
-    "    start_positions = []\n",
-    "    end_positions = []\n",
-    "\n",
-    "    for i, offset in enumerate(offset_mapping):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        answer = answers[sample_idx]\n",
-    "        start_char = answer[\"answer_start\"][0]\n",
-    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "        # Find the start and end of the context\n",
-    "        idx = 0\n",
-    "        while sequence_ids[idx] != 1:\n",
-    "            idx += 1\n",
-    "        context_start = idx\n",
-    "        while sequence_ids[idx] == 1:\n",
-    "            idx += 1\n",
-    "        context_end = idx - 1\n",
-    "\n",
-    "        # If the answer is not fully inside the context, label is (0, 0)\n",
-    "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "            start_positions.append(0)\n",
-    "            end_positions.append(0)\n",
-    "        else:\n",
-    "            # Otherwise it's the start and end token positions\n",
-    "            idx = context_start\n",
-    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "                idx += 1\n",
-    "            start_positions.append(idx - 1)\n",
-    "\n",
-    "            idx = context_end\n",
-    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "                idx -= 1\n",
-    "            end_positions.append(idx + 1)\n",
-    "\n",
-    "    inputs[\"start_positions\"] = start_positions\n",
-    "    inputs[\"end_positions\"] = end_positions\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(87599, 88729)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_dataset = raw_datasets[\"train\"].map(\n",
-    "    preprocess_training_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"train\"]), len(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess_validation_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    example_ids = []\n",
-    "\n",
-    "    for i in range(len(inputs[\"input_ids\"])):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        example_ids.append(examples[\"id\"][sample_idx])\n",
-    "\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "        offset = inputs[\"offset_mapping\"][i]\n",
-    "        inputs[\"offset_mapping\"][i] = [\n",
-    "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
-    "        ]\n",
-    "\n",
-    "    inputs[\"example_id\"] = example_ids\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10570, 10822)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation_dataset = raw_datasets[\"validation\"].map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"validation\"]), len(validation_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "small_eval_set = raw_datasets[\"validation\"].select(range(100))\n",
-    "trained_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n",
-    "eval_set = small_eval_set.map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import TFAutoModelForQuestionAnswering\n",
-    "\n",
-    "eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "eval_set_for_model.set_format(\"numpy\")\n",
-    "\n",
-    "batch = {k: eval_set_for_model[k] for k in eval_set_for_model.column_names}\n",
-    "trained_model = TFAutoModelForQuestionAnswering.from_pretrained(trained_checkpoint)\n",
-    "\n",
-    "outputs = trained_model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_logits = outputs.start_logits.numpy()\n",
-    "end_logits = outputs.end_logits.numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "\n",
-    "example_to_features = collections.defaultdict(list)\n",
-    "for idx, feature in enumerate(eval_set):\n",
-    "    example_to_features[feature[\"example_id\"]].append(idx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "n_best = 20\n",
-    "max_answer_length = 30\n",
-    "predicted_answers = []\n",
-    "\n",
-    "for example in small_eval_set:\n",
-    "    example_id = example[\"id\"]\n",
-    "    context = example[\"context\"]\n",
-    "    answers = []\n",
-    "\n",
-    "    for feature_index in example_to_features[example_id]:\n",
-    "        start_logit = start_logits[feature_index]\n",
-    "        end_logit = end_logits[feature_index]\n",
-    "        offsets = eval_set[\"offset_mapping\"][feature_index]\n",
-    "\n",
-    "        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        for start_index in start_indexes:\n",
-    "            for end_index in end_indexes:\n",
-    "                # Skip answers that are not fully in the context\n",
-    "                if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                    continue\n",
-    "                # Skip answers with a length that is either < 0 or > max_answer_length.\n",
-    "                if (\n",
-    "                    end_index < start_index\n",
-    "                    or end_index - start_index + 1 > max_answer_length\n",
-    "                ):\n",
-    "                    continue\n",
-    "\n",
-    "                answers.append(\n",
-    "                    {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                )\n",
-    "\n",
-    "    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theoretical_answers = [\n",
-    "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\n",
-       "{'id': '56be4db0acb8001400a502ec', 'answers': {'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(predicted_answers[0])\n",
-    "print(theoretical_answers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "\n",
-    "def compute_metrics(start_logits, end_logits, features, examples):\n",
-    "    example_to_features = collections.defaultdict(list)\n",
-    "    for idx, feature in enumerate(features):\n",
-    "        example_to_features[feature[\"example_id\"]].append(idx)\n",
-    "\n",
-    "    predicted_answers = []\n",
-    "    for example in tqdm(examples):\n",
-    "        example_id = example[\"id\"]\n",
-    "        context = example[\"context\"]\n",
-    "        answers = []\n",
-    "\n",
-    "        # Loop through all features associated with that example\n",
-    "        for feature_index in example_to_features[example_id]:\n",
-    "            start_logit = start_logits[feature_index]\n",
-    "            end_logit = end_logits[feature_index]\n",
-    "            offsets = features[feature_index][\"offset_mapping\"]\n",
-    "\n",
-    "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            for start_index in start_indexes:\n",
-    "                for end_index in end_indexes:\n",
-    "                    # Skip answers that are not fully in the context\n",
-    "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                        continue\n",
-    "                    # Skip answers with a length that is either < 0 or > max_answer_length\n",
-    "                    if (\n",
-    "                        end_index < start_index\n",
-    "                        or end_index - start_index + 1 > max_answer_length\n",
-    "                    ):\n",
-    "                        continue\n",
-    "\n",
-    "                    answer = {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                    answers.append(answer)\n",
-    "\n",
-    "        # Select the answer with the best score\n",
-    "        if len(answers) > 0:\n",
-    "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "            predicted_answers.append(\n",
-    "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
-    "            )\n",
-    "        else:\n",
-    "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
-    "\n",
-    "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
-    "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DefaultDataCollator\n",
-    "\n",
-    "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = train_dataset.to_tf_dataset(\n",
-    "    columns=[\n",
-    "        \"input_ids\",\n",
-    "        \"start_positions\",\n",
-    "        \"end_positions\",\n",
-    "        \"attention_mask\",\n",
-    "        \"token_type_ids\",\n",
-    "    ],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=16,\n",
-    ")\n",
-    "tf_eval_dataset = validation_dataset.to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_train_epochs\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"bert-finetuned-squad\", tokenizer=tokenizer)\n",
-    "\n",
-    "# We're going to do validation afterwards, so no validation mid-training\n",
-    "model.fit(tf_train_dataset, callbacks=[callback], epochs=num_train_epochs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 81.18259224219489, 'f1': 88.67381321905516}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = model.predict(tf_eval_dataset)\n",
-    "compute_metrics(\n",
-    "    predictions[\"start_logits\"],\n",
-    "    predictions[\"end_logits\"],\n",
-    "    validation_dataset,\n",
-    "    raw_datasets[\"validation\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.9979003071784973,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-squad\"\n",
-    "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
-    "\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "質問応答 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ja/chapter8/section2.ipynb b/course/ja/chapter8/section2.ipynb
deleted file mode 100644
index 69b335f0..00000000
--- a/course/ja/chapter8/section2.ipynb
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# エラーを見つけた時に最初にすること"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Clone the repo and extract the local path\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Create an empty repo on the Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Clone the empty repo\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Copy files\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Push to Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.gitattributes', 'README.md', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer_config.json', 'training_args.bin', 'vocab.txt']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.38669535517692566,\n",
-       " 'start': 34,\n",
-       " 'end': 95,\n",
-       " 'answer': 'the task of extracting an answer from a text given a question'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "---------------------------------------------------------------------------\n",
-       "AttributeError                            Traceback (most recent call last)\n",
-       "/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in <module>\n",
-       "      1 inputs = tokenizer(question, text, add_special_tokens=True)\n",
-       "      2 input_ids = inputs[\"input_ids\"]\n",
-       "----> 3 outputs = model(**inputs)\n",
-       "      4 answer_start_scores = outputs.start_logits\n",
-       "      5 answer_end_scores = outputs.end_logits\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\n",
-       "    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
-       "    724\n",
-       "--> 725         distilbert_output = self.distilbert(\n",
-       "    726             input_ids=input_ids,\n",
-       "    727             attention_mask=attention_mask,\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\n",
-       "    471             raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n",
-       "    472         elif input_ids is not None:\n",
-       "--> 473             input_shape = input_ids.size()\n",
-       "    474         elif inputs_embeds is not None:\n",
-       "    475             input_shape = inputs_embeds.size()[:-1]\n",
-       "\n",
-       "AttributeError: 'list' object has no attribute 'size'\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Get the most likely beginning of answer with the argmax of the score\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Get the most likely end of answer with the argmax of the score\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 2029, 7705, 2015, 2064]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "エラーを見つけた時に最初にすること",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter1/section10.ipynb b/course/ko/chapter1/section10.ipynb
deleted file mode 100644
index 4a47e72c..00000000
--- a/course/ko/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 단원 마무리 퀴즈"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "단원 마무리 퀴즈",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter1/section3.ipynb b/course/ko/chapter1/section3.ipynb
deleted file mode 100644
index 9161c707..00000000
--- a/course/ko/chapter1/section3.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 트랜스포머로 무엇을 할 수 있나요?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of\n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil,\n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of\n",
-    "    the premier American universities engineering curricula now concentrate on\n",
-    "    and encourage largely the study of engineering science. As a result, there\n",
-    "    are declining offerings in engineering subjects dealing with infrastructure,\n",
-    "    the environment, and related issues, and greater concentration on high\n",
-    "    technology subjects, largely supporting increasingly complex scientific\n",
-    "    developments. While the latter is important, it should not be at the expense\n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other\n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance\n",
-    "    the teaching of engineering. Both China and India, respectively, graduate\n",
-    "    six and eight times as many traditional engineers as does the United States.\n",
-    "    Other industrial countries at minimum maintain their output, while America\n",
-    "    suffers an increasingly serious decline in the number of engineering graduates\n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "트랜스포머로 무엇을 할 수 있나요?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter1/section8.ipynb b/course/ko/chapter1/section8.ipynb
deleted file mode 100644
index f83bac4e..00000000
--- a/course/ko/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 편향과 한계"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "편향과 한계",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter2/section2_pt.ipynb b/course/ko/chapter2/section2_pt.ipynb
deleted file mode 100644
index 04b0443a..00000000
--- a/course/ko/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 파이프라인 내부 동작 과정 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "파이프라인 내부 동작 과정 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter2/section2_tf.ipynb b/course/ko/chapter2/section2_tf.ipynb
deleted file mode 100644
index 2ec20b97..00000000
--- a/course/ko/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 파이프라인 내부 동작 과정 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "파이프라인 내부 동작 과정 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter8/section2.ipynb b/course/ko/chapter8/section2.ipynb
deleted file mode 100644
index f4156629..00000000
--- a/course/ko/chapter8/section2.ipynb
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 에러가 발생했을 때 대응 방법"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Clone the repo and extract the local path\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Create an empty repo on the Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Clone the empty repo\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Copy files\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Push to Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'. make sure that:\n",
-       "\n",
-       "- 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'이라는 모델명이 'https://huggingface.co/models'에 존재하는지 확인하거나\n",
-       "\n",
-       "'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'이라는 경로 또는 폴더가 config.json 파일 포함하고 있는지 확인하세요.\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.gitattributes', 'README.md', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer_config.json', 'training_args.bin', 'vocab.txt']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.38669535517692566,\n",
-       " 'start': 34,\n",
-       " 'end': 95,\n",
-       " 'answer': 'the task of extracting an answer from a text given a question'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "---------------------------------------------------------------------------\n",
-       "AttributeError                            Traceback (most recent call last)\n",
-       "/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in <module>\n",
-       "      1 inputs = tokenizer(question, text, add_special_tokens=True)\n",
-       "      2 input_ids = inputs[\"input_ids\"]\n",
-       "----> 3 outputs = model(**inputs)\n",
-       "      4 answer_start_scores = outputs.start_logits\n",
-       "      5 answer_end_scores = outputs.end_logits\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\n",
-       "    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
-       "    724\n",
-       "--> 725         distilbert_output = self.distilbert(\n",
-       "    726             input_ids=input_ids,\n",
-       "    727             attention_mask=attention_mask,\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\n",
-       "    471             raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n",
-       "    472         elif input_ids is not None:\n",
-       "--> 473             input_shape = input_ids.size()\n",
-       "    474         elif inputs_embeds is not None:\n",
-       "    475             input_shape = inputs_embeds.size()[:-1]\n",
-       "\n",
-       "AttributeError: 'list' object has no attribute 'size'\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Get the most likely beginning of answer with the argmax of the score\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Get the most likely end of answer with the argmax of the score\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 2029, 7705, 2015, 2064]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "에러가 발생했을 때 대응 방법",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter8/section3.ipynb b/course/ko/chapter8/section3.ipynb
deleted file mode 100644
index 761d5b1f..00000000
--- a/course/ko/chapter8/section3.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 포럼에서 도움 요청하기"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModel.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"\"\"\n",
-    "Generation One is a retroactive term for the Transformers characters that\n",
-    "appeared between 1984 and 1993. The Transformers began with the 1980s Japanese\n",
-    "toy lines Micro Change and Diaclone. They presented robots able to transform\n",
-    "into everyday vehicles, electronic items or weapons. Hasbro bought the Micro\n",
-    "Change and Diaclone toys, and partnered with Takara. Marvel Comics was hired by\n",
-    "Hasbro to create the backstory; editor-in-chief Jim Shooter wrote an overall\n",
-    "story, and gave the task of creating the characthers to writer Dennis O'Neil.\n",
-    "Unhappy with O'Neil's work (although O'Neil created the name \"Optimus Prime\"),\n",
-    "Shooter chose Bob Budiansky to create the characters.\n",
-    "\n",
-    "The Transformers mecha were largely designed by Shōji Kawamori, the creator of\n",
-    "the Japanese mecha anime franchise Macross (which was adapted into the Robotech\n",
-    "franchise in North America). Kawamori came up with the idea of transforming\n",
-    "mechs while working on the Diaclone and Macross franchises in the early 1980s\n",
-    "(such as the VF-1 Valkyrie in Macross and Robotech), with his Diaclone mechs\n",
-    "later providing the basis for Transformers.\n",
-    "\n",
-    "The primary concept of Generation One is that the heroic Optimus Prime, the\n",
-    "villainous Megatron, and their finest soldiers crash land on pre-historic Earth\n",
-    "in the Ark and the Nemesis before awakening in 1985, Cybertron hurtling through\n",
-    "the Neutral zone as an effect of the war. The Marvel comic was originally part\n",
-    "of the main Marvel Universe, with appearances from Spider-Man and Nick Fury,\n",
-    "plus some cameos, as well as a visit to the Savage Land.\n",
-    "\n",
-    "The Transformers TV series began around the same time. Produced by Sunbow\n",
-    "Productions and Marvel Productions, later Hasbro Productions, from the start it\n",
-    "contradicted Budiansky's backstories. The TV series shows the Autobots looking\n",
-    "for new energy sources, and crash landing as the Decepticons attack. Marvel\n",
-    "interpreted the Autobots as destroying a rogue asteroid approaching Cybertron.\n",
-    "Shockwave is loyal to Megatron in the TV series, keeping Cybertron in a\n",
-    "stalemate during his absence, but in the comic book he attempts to take command\n",
-    "of the Decepticons. The TV series would also differ wildly from the origins\n",
-    "Budiansky had created for the Dinobots, the Decepticon turned Autobot Jetfire\n",
-    "(known as Skyfire on TV), the Constructicons (who combine to form\n",
-    "Devastator),[19][20] and Omega Supreme. The Marvel comic establishes early on\n",
-    "that Prime wields the Creation Matrix, which gives life to machines. In the\n",
-    "second season, the two-part episode The Key to Vector Sigma introduced the\n",
-    "ancient Vector Sigma computer, which served the same original purpose as the\n",
-    "Creation Matrix (giving life to Transformers), and its guardian Alpha Trion.\n",
-    "\"\"\"\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "logits = model(**inputs).logits"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "포럼에서 도움 요청하기",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter8/section4.ipynb b/course/ko/chapter8/section4.ipynb
deleted file mode 100644
index 249cadd7..00000000
--- a/course/ko/chapter8/section4.ipynb
+++ /dev/null
@@ -1,870 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 학습 파이프라인 디버깅(파이토치)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: You have to specify either input_ids or inputs_embeds'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=raw_datasets[\"train\"],\n",
-    "    eval_dataset=raw_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'hypothesis': 'Product and geography are what make cream skimming work. ',\n",
-       " 'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'premise': 'Conceptually cream skimming has two basic dimensions - product and geography.'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: expected sequence of length 43 at dim 1 (got 37)'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] conceptually cream skimming has two basic dimensions - product and geography. [SEP] product and geography are what make cream skimming work. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(trainer.train_dataset[0][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'hypothesis', 'idx', 'input_ids', 'label', 'premise'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0].keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "transformers.models.distilbert.modeling_distilbert.DistilBertForSequenceClassification"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(trainer.model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0][\"attention_mask\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(trainer.train_dataset[0][\"attention_mask\"]) == len(\n",
-    "    trainer.train_dataset[0][\"input_ids\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0][\"label\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['entailment', 'neutral', 'contradiction']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset.features[\"label\"].names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/git/transformers/src/transformers/data/data_collator.py in torch_default_data_collator(features)\n",
-       "    105                 batch[k] = torch.stack([f[k] for f in features])\n",
-       "    106             else:\n",
-       "--> 107                 batch[k] = torch.tensor([f[k] for f in features])\n",
-       "    108 \n",
-       "    109     return batch\n",
-       "\n",
-       "ValueError: expected sequence of length 45 at dim 1 (got 76)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function transformers.data.data_collator.default_data_collator(features: List[InputDataClass], return_tensors='pt') -> Dict[str, Any]>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "data_collator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "batch = data_collator([trainer.train_dataset[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "actual_train_set = trainer._remove_unused_columns(trainer.train_dataset)\n",
-    "batch = data_collator([actual_train_set[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)\n",
-       "   2386         )\n",
-       "   2387     if dim == 2:\n",
-       "-> 2388         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "   2389     elif dim == 4:\n",
-       "   2390         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "\n",
-       "IndexError: Target 2 is out of bounds."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "outputs = trainer.model.to(device)(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = outputs.loss\n",
-    "loss.backward()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.create_optimizer()\n",
-    "trainer.optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This will take a long time and error out, so you shouldn't run this cell\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_eval_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = outputs.logits.cpu().numpy()\n",
-    "labels = batch[\"labels\"].cpu().numpy()\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((8, 3), (8,))"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions.shape, labels.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.625}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "trainer.create_optimizer()\n",
-    "\n",
-    "for _ in range(20):\n",
-    "    outputs = trainer.model(**batch)\n",
-    "    loss = outputs.loss\n",
-    "    loss.backward()\n",
-    "    trainer.optimizer.step()\n",
-    "    trainer.optimizer.zero_grad()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 1.0}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)\n",
-    "preds = outputs.logits\n",
-    "labels = batch[\"labels\"]\n",
-    "\n",
-    "compute_metrics((preds.cpu().numpy(), labels.cpu().numpy()))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "학습 파이프라인 디버깅(파이토치)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter8/section4_tf.ipynb b/course/ko/chapter8/section4_tf.ipynb
deleted file mode 100644
index fc010e0c..00000000
--- a/course/ko/chapter8/section4_tf.ipynb
+++ /dev/null
@@ -1,443 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 학습 파이프라인 디버깅(텐서플로우)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ValueError: No gradients provided for any variable: ['tf_distil_bert_for_sequence_classification/distilbert/embeddings/word_embeddings/weight:0', '...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    TFAutoModelForSequenceClassification,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "\n",
-    "train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "validation_dataset = tokenized_datasets[\"validation_matched\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
-    "\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        ...,\n",
-       "        [1, 1, 1, ..., 1, 1, 1],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0]])>,\n",
-       " 'label': <tf.Tensor: shape=(16,), dtype=int64, numpy=array([0, 2, 1, 2, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2, 1])>,\n",
-       " 'input_ids': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[ 101, 2174, 1010, ...,    0,    0,    0],\n",
-       "        [ 101, 3174, 2420, ...,    0,    0,    0],\n",
-       "        [ 101, 2044, 2048, ...,    0,    0,    0],\n",
-       "        ...,\n",
-       "        [ 101, 3398, 3398, ..., 2051, 2894,  102],\n",
-       "        [ 101, 1996, 4124, ...,    0,    0,    0],\n",
-       "        [ 101, 1999, 2070, ...,    0,    0,    0]])>}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  246/24543 [..............................] - ETA: 15:52 - loss: nan"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.compile(optimizer=\"adam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
-       "       nan, nan, nan], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([0.6844486 ,        nan,        nan, 0.67127866, 0.7068601 ,\n",
-       "              nan, 0.69309855,        nan, 0.65531296,        nan,\n",
-       "              nan,        nan, 0.675402  ,        nan,        nan,\n",
-       "       0.69831556], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[-0.04761693, -0.06509043],\n",
-       "       [-0.0481936 , -0.04556257],\n",
-       "       [-0.0040929 , -0.05848458],\n",
-       "       [-0.02417453, -0.0684005 ],\n",
-       "       [-0.02517801, -0.05241832],\n",
-       "       [-0.04514256, -0.0757378 ],\n",
-       "       [-0.02656011, -0.02646275],\n",
-       "       [ 0.00766164, -0.04350497],\n",
-       "       [ 0.02060014, -0.05655622],\n",
-       "       [-0.02615328, -0.0447021 ],\n",
-       "       [-0.05119278, -0.06928903],\n",
-       "       [-0.02859691, -0.04879177],\n",
-       "       [-0.02210129, -0.05791225],\n",
-       "       [-0.02363213, -0.05962167],\n",
-       "       [-0.05352269, -0.0481673 ],\n",
-       "       [-0.08141848, -0.07110836]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  2,  5,  7,  9, 10, 11, 13, 14])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "loss = model(batch).loss.numpy()\n",
-    "indices = np.flatnonzero(np.isnan(loss))\n",
-    "indices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  101,  2007,  2032,  2001,  1037, 16480,  3917,  2594,  4135,\n",
-       "        23212,  3070,  2214, 10170,  1010,  2012,  4356,  1997,  3183,\n",
-       "         6838, 12953,  2039,  2000,  1996,  6147,  1997,  2010,  2606,\n",
-       "         1012,   102,  6838,  2001,  3294,  6625,  3773,  1996,  2214,\n",
-       "         2158,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  6814,  2016,  2234,  2461,  2153,  1998, 13322,\n",
-       "         2009,  1012,   102,  2045,  1005,  1055,  2053,  3382,  2008,\n",
-       "         2016,  1005,  2222,  3046,  8103,  2075,  2009,  2153,  1012,\n",
-       "          102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  2007,  1996,  3712,  4634,  1010,  2057,  8108,\n",
-       "         2025,  3404,  2028,  1012,  1996,  2616, 18449,  2125,  1999,\n",
-       "         1037,  9666,  1997,  4100,  8663, 11020,  6313,  2791,  1998,\n",
-       "         2431,  1011,  4301,  1012,   102,  2028,  1005,  1055,  5177,\n",
-       "         2110,  1998,  3977,  2000,  2832,  2106,  2025,  2689,  2104,\n",
-       "         2122,  6214,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1045,  2001,  1999,  1037, 13090,  5948,  2007,  2048,\n",
-       "         2308,  2006,  2026,  5001,  2043,  2026,  2171,  2001,  2170,\n",
-       "         1012,   102,  1045,  2001,  3564,  1999,  2277,  1012,   102,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2195,  4279,  2191,  2039,  1996,  2181,  2124,  2004,\n",
-       "         1996,  2225,  7363,  1012,   102,  2045,  2003,  2069,  2028,\n",
-       "         2451,  1999,  1996,  2225,  7363,  1012,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2061,  2008,  1045,  2123,  1005,  1056,  2113,  2065,\n",
-       "         2009,  2428, 10654,  7347,  2030,  2009,  7126,  2256,  2495,\n",
-       "         2291,   102,  2009,  2003,  5094,  2256,  2495,  2291,  2035,\n",
-       "         2105,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2051,  1010,  2029,  3216,  2019,  2503,  3444,  1010,\n",
-       "         6732,  1996,  2265,  2038, 19840,  2098,  2125,  9906,  1998,\n",
-       "         2003,  2770,  2041,  1997,  4784,  1012,   102,  2051,  6732,\n",
-       "         1996,  2265,  2003,  9525,  1998,  4569,  1012,   102,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1996, 10556,  2140, 11515,  2058,  1010,  2010,  2162,\n",
-       "         2252,  5689,  2013,  2010,  7223,  1012,   102,  2043,  1996,\n",
-       "        10556,  2140, 11515,  2058,  1010,  2010,  2252,  3062,  2000,\n",
-       "         1996,  2598,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101, 13543,  1999,  2049,  6143,  2933,  2443,   102,  2025,\n",
-       "        13543,  1999,  6143,  2933,  2003,  2443,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "input_ids[indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model.compile(optimizer=Adam(5e-5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "319/24543 [..............................] - ETA: 16:07 - loss: 0.9718"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "tokenizer.decode(input_ids[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = batch[\"labels\"].numpy()\n",
-    "label = labels[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break\n",
-    "\n",
-    "# Make sure you have run model.compile() and set your optimizer,\n",
-    "# and your loss/metrics if you're using them\n",
-    "\n",
-    "model.fit(batch, epochs=20)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "학습 파이프라인 디버깅(텐서플로우)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter8/section5.ipynb b/course/ko/chapter8/section5.ipynb
deleted file mode 100644
index 1a3ee9b9..00000000
--- a/course/ko/chapter8/section5.ipynb
+++ /dev/null
@@ -1,42 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 이슈 리포트 작성하는 법"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "이슈 리포트 작성하는 법",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ko/chapter8/section7.ipynb b/course/ko/chapter8/section7.ipynb
deleted file mode 100644
index 8547835b..00000000
--- a/course/ko/chapter8/section7.ipynb
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 단원 마무리 퀴즈"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)\n",
-    "# ---------------------------------------------------------------------------\n",
-    "# ImportError                               Traceback (most recent call last)\n",
-    "# /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_30848/333858878.py in <module>\n",
-    "# ----> 1 from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "단원 마무리 퀴즈",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter1/section10.ipynb b/course/pt/chapter1/section10.ipynb
deleted file mode 100644
index 6444f160..00000000
--- a/course/pt/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questionário de fim de capítulo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Questionário de fim de capítulo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter1/section3.ipynb b/course/pt/chapter1/section3.ipynb
deleted file mode 100644
index fe683db1..00000000
--- a/course/pt/chapter1/section3.ipynb
+++ /dev/null
@@ -1,324 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformers, o que eles podem fazer?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}] # nesse curso, nós te mostraremos como você pode entender e usar o fluxo de dados e a troca de dados quando for lidar com dados do usuário. Nós estaremos trabalhando com um ou um dos mais comuns fluxos de dados utilizados - fluxo de dados de vários tipos, como visto pelo 'HTTP'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\"\n",
-    ")  # nesse curso, nós te mostraremos como você"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformers, o que eles podem fazer?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter1/section8.ipynb b/course/pt/chapter1/section8.ipynb
deleted file mode 100644
index 3bfa506b..00000000
--- a/course/pt/chapter1/section8.ipynb
+++ /dev/null
@@ -1,54 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Vieses e limitações"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "[\"lawyer\", \"carpenter\", \"doctor\", \"waiter\", \"mechanic\"]\n",
-    "[\"nurse\", \"waitress\", \"teacher\", \"maid\", \"prostitute\"]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Vieses e limitações",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section2_pt.ipynb b/course/pt/chapter2/section2_pt.ipynb
deleted file mode 100644
index cbc83ed8..00000000
--- a/course/pt/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Por dentro da função pipeline (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Por dentro da função pipeline (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section2_tf.ipynb b/course/pt/chapter2/section2_tf.ipynb
deleted file mode 100644
index 663181d2..00000000
--- a/course/pt/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Por dentro da função pipeline (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Por dentro da função pipeline (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section3_pt.ipynb b/course/pt/chapter2/section3_pt.ipynb
deleted file mode 100644
index c29d60a1..00000000
--- a/course/pt/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modelos (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Construindo a configuração\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Construindo o modelo a partir da configuração\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# O modelo é inicializado aleatoriamente!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"path_no_seu_computador\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Modelos (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section3_tf.ipynb b/course/pt/chapter2/section3_tf.ipynb
deleted file mode 100644
index 2fc71c7f..00000000
--- a/course/pt/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modelos (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Construindo a configuração\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Construindo o modelo a partir da configuração\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# O modelo é inicializado aleatoriamente!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"path_no_seu_computador\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Modelos (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section4_pt.ipynb b/course/pt/chapter2/section4_pt.ipynb
deleted file mode 100644
index fadf2b34..00000000
--- a/course/pt/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section4_tf.ipynb b/course/pt/chapter2/section4_tf.ipynb
deleted file mode 100644
index 15343d71..00000000
--- a/course/pt/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section5_pt.ipynb b/course/pt/chapter2/section5_pt.ipynb
deleted file mode 100644
index 21a5869a..00000000
--- a/course/pt/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tratando sequências múltiplas (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tratando sequências múltiplas (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section5_tf.ipynb b/course/pt/chapter2/section5_tf.ipynb
deleted file mode 100644
index 90d69319..00000000
--- a/course/pt/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tratando sequências múltiplas (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tratando sequências múltiplas (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section6_pt.ipynb b/course/pt/chapter2/section6_pt.ipynb
deleted file mode 100644
index 7586acb8..00000000
--- a/course/pt/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Colocando tudo junto (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Irá preencher as sequências até o comprimento máximo da sequência\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Irá preencher as sequências até o comprimento máximo do modelo\n",
-    "# (512 para o modelo BERT ou DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Irá preencher as sequências até o comprimento máximo especificado\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Irá preencher as sequências até o comprimento máximo do modelo\n",
-    "# (512 para o modelo BERT ou DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Truncará as sequências que são mais longas do que o comprimento máximo especificado\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Retorna tensores PyTorch\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Retorna tensores TensorFlow\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Retorna NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Colocando tudo junto (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section6_tf.ipynb b/course/pt/chapter2/section6_tf.ipynb
deleted file mode 100644
index 8de6a5df..00000000
--- a/course/pt/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Colocando tudo junto (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Irá preencher as sequências até o comprimento máximo da sequência\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Irá preencher as sequências até o comprimento máximo do modelo\n",
-    "# (512 para o modelo BERT ou DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Irá preencher as sequências até o comprimento máximo especificado\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Irá preencher as sequências até o comprimento máximo do modelo\n",
-    "# (512 para o modelo BERT ou DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Truncará as sequências que são mais longas do que o comprimento máximo especificado\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Retorna tensores PyTorch\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Retorna tensores TensorFlow\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Retorna NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Colocando tudo junto (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section8_pt.ipynb b/course/pt/chapter2/section8_pt.ipynb
deleted file mode 100644
index 9f34ecb7..00000000
--- a/course/pt/chapter2/section8_pt.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questionário de fim de capítulo (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = AutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Questionário de fim de capítulo (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter2/section8_tf.ipynb b/course/pt/chapter2/section8_tf.ipynb
deleted file mode 100644
index 09ddb5ab..00000000
--- a/course/pt/chapter2/section8_tf.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questionário de fim de capítulo (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = TFAutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Questionário de fim de capítulo (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter4/section2_pt.ipynb b/course/pt/chapter4/section2_pt.ipynb
deleted file mode 100644
index 3132f62b..00000000
--- a/course/pt/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Usando modelos pré-treinados (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Usando modelos pré-treinados (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter4/section2_tf.ipynb b/course/pt/chapter4/section2_tf.ipynb
deleted file mode 100644
index 76bbfa13..00000000
--- a/course/pt/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Usando modelos pré-treinados (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Usando modelos pré-treinados (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter4/section3_pt.ipynb b/course/pt/chapter4/section3_pt.ipynb
deleted file mode 100644
index e36efc5f..00000000
--- a/course/pt/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Compartilhando modelos pré-treinados (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Gestão de usuários\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Criação e gestão de repositório\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    #E alguns métodos para recuperar/trocar informações sobre o conteúdo\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Compartilhando modelos pré-treinados (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter4/section3_tf.ipynb b/course/pt/chapter4/section3_tf.ipynb
deleted file mode 100644
index 1ac1c85f..00000000
--- a/course/pt/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Compartilhando modelos pré-treinados (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Gestão de usuários\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Criação e gestão de repositório\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    #E alguns métodos para recuperar/trocar informações sobre o conteúdo\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Compartilhando modelos pré-treinados (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter5/section2.ipynb b/course/pt/chapter5/section2.ipynb
deleted file mode 100644
index 9f56b206..00000000
--- a/course/pt/chapter5/section2.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# E se o meu dataset não estiver no Hub?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz\n",
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gzip -dkv SQuAD_it-*.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=\"SQuAD_it-train.json\", field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    \"title\": \"Terremoto del Sichuan del 2008\",\n",
-       "    \"paragraphs\": [\n",
-       "        {\n",
-       "            \"context\": \"Il terremoto del Sichuan del 2008 o il terremoto...\",\n",
-       "            \"qas\": [\n",
-       "                {\n",
-       "                    \"answers\": [{\"answer_start\": 29, \"text\": \"2008\"}],\n",
-       "                    \"id\": \"56cdca7862d2951400fa6826\",\n",
-       "                    \"question\": \"In quale anno si è verificato il terremoto nel Sichuan?\",\n",
-       "                },\n",
-       "                ...\n",
-       "            ],\n",
-       "        },\n",
-       "        ...\n",
-       "    ],\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 48\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json\", \"test\": \"SQuAD_it-test.json\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")\n",
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json.gz\", \"test\": \"SQuAD_it-test.json.gz\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/crux82/squad-it/raw/master/\"\n",
-    "data_files = {\n",
-    "    \"train\": url + \"SQuAD_it-train.json.gz\",\n",
-    "    \"test\": url + \"SQuAD_it-test.json.gz\",\n",
-    "}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "E se o meu dataset não estiver no Hub?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter5/section3.ipynb b/course/pt/chapter5/section3.ipynb
deleted file mode 100644
index c9ba403d..00000000
--- a/course/pt/chapter5/section3.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hora de fatiar e dividir os dados"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
-    "!unzip drugsCom_raw.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
-    "# \\t is the tab character in Python\n",
-    "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Unnamed: 0': [87571, 178045, 80482],\n",
-       " 'drugName': ['Naproxen', 'Duloxetine', 'Mobic'],\n",
-       " 'condition': ['Gout, Acute', 'ibromyalgia', 'Inflammatory Conditions'],\n",
-       " 'review': ['\"like the previous person mention, I&#039;m a strong believer of aleve, it works faster for my gout than the prescription meds I take. No more going to the doctor for refills.....Aleve works!\"',\n",
-       "  '\"I have taken Cymbalta for about a year and a half for fibromyalgia pain. It is great\\r\\nas a pain reducer and an anti-depressant, however, the side effects outweighed \\r\\nany benefit I got from it. I had trouble with restlessness, being tired constantly,\\r\\ndizziness, dry mouth, numbness and tingling in my feet, and horrible sweating. I am\\r\\nbeing weaned off of it now. Went from 60 mg to 30mg and now to 15 mg. I will be\\r\\noff completely in about a week. The fibro pain is coming back, but I would rather deal with it than the side effects.\"',\n",
-       "  '\"I have been taking Mobic for over a year with no side effects other than an elevated blood pressure.  I had severe knee and ankle pain which completely went away after taking Mobic.  I attempted to stop the medication however pain returned after a few days.\"'],\n",
-       " 'rating': [9.0, 3.0, 10.0],\n",
-       " 'date': ['September 2, 2015', 'November 7, 2011', 'June 5, 2013'],\n",
-       " 'usefulCount': [36, 13, 128]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
-    "# Peek at the first few examples\n",
-    "drug_sample[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split in drug_dataset.keys():\n",
-    "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 161297\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 53766\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.rename_column(\n",
-    "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
-    ")\n",
-    "drug_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AttributeError: 'NoneType' object has no attribute 'lower'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def lowercase_condition(example):\n",
-    "    return {\"condition\": example[\"condition\"].lower()}\n",
-    "\n",
-    "\n",
-    "drug_dataset.map(lowercase_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_nones(x):\n",
-    "    return x[\"condition\"] is not None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x * x)(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda base, height: 0.5 * base * height)(4, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['left ventricular dysfunction', 'adhd', 'birth control']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(lowercase_condition)\n",
-    "# Check that lowercasing worked\n",
-    "drug_dataset[\"train\"][\"condition\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_review_length(example):\n",
-    "    return {\"review_length\": len(example[\"review\"].split())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': 206461,\n",
-       " 'drugName': 'Valsartan',\n",
-       " 'condition': 'left ventricular dysfunction',\n",
-       " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
-       " 'rating': 9.0,\n",
-       " 'date': 'May 20, 2012',\n",
-       " 'usefulCount': 27,\n",
-       " 'review_length': 17}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(compute_review_length)\n",
-    "# Inspect the first training example\n",
-    "drug_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': [103488, 23627, 20558],\n",
-       " 'drugName': ['Loestrin 21 1 / 20', 'Chlorzoxazone', 'Nucynta'],\n",
-       " 'condition': ['birth control', 'muscle spasm', 'pain'],\n",
-       " 'review': ['\"Excellent.\"', '\"useless\"', '\"ok\"'],\n",
-       " 'rating': [10.0, 1.0, 6.0],\n",
-       " 'date': ['November 4, 2008', 'March 24, 2017', 'August 20, 2016'],\n",
-       " 'usefulCount': [5, 2, 10],\n",
-       " 'review_length': [1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'train': 138514, 'test': 46108}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
-    "print(drug_dataset.num_rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm a transformer called BERT\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import html\n",
-    "\n",
-    "text = \"I&#039;m a transformer called BERT\"\n",
-    "html.unescape(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_drug_dataset = drug_dataset.map(\n",
-    "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "\n",
-    "def tokenize_function(examples):\n",
-    "    return tokenizer(examples[\"review\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%time tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
-    "\n",
-    "\n",
-    "def slow_tokenize_function(examples):\n",
-    "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    return tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[128, 49]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
-    "[len(inp) for inp in result[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ArrowInvalid: Column 1 named condition expected length 1463 but got length 1000"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(\n",
-    "    tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(206772, 138514)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    result = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    # Extract mapping between new and old indices\n",
-    "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
-    "    for key, values in examples.items():\n",
-    "        result[key] = [values[i] for i in sample_map]\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 206772\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 68876\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.set_format(\"pandas\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df = drug_dataset[\"train\"][:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frequencies = (\n",
-    "    train_df[\"condition\"]\n",
-    "    .value_counts()\n",
-    "    .to_frame()\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
-    ")\n",
-    "frequencies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['condition', 'frequency'],\n",
-       "    num_rows: 819\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "freq_dataset = Dataset.from_pandas(frequencies)\n",
-    "freq_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
-    "# Rename the default \"test\" split to \"validation\"\n",
-    "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
-    "# Add the \"test\" set to our `DatasetDict`\n",
-    "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
-    "drug_dataset_clean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_from_disk\n",
-    "\n",
-    "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
-    "drug_dataset_reloaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split, dataset in drug_dataset_clean.items():\n",
-    "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\"patient_id\":141780,\"drugName\":\"Escitalopram\",\"condition\":\"depression\",\"review\":\"\\\"I seemed to experience the regular side effects of LEXAPRO, insomnia, low sex drive, sleepiness during the day. I am taking it at night because my doctor said if it made me tired to take it at night. I assumed it would and started out taking it at night. Strange dreams, some pleasant. I was diagnosed with fibromyalgia. Seems to be helping with the pain. Have had anxiety and depression in my family, and have tried quite a few other medications that haven't worked. Only have been on it for two weeks but feel more positive in my mind, want to accomplish more in my life. Hopefully the side effects will dwindle away, worth it to stick with it from hearing others responses. Great medication.\\\"\",\"rating\":9.0,\"date\":\"May 29, 2011\",\"usefulCount\":10,\"review_length\":125}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "!head -n 1 drug-reviews-train.jsonl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\n",
-    "    \"train\": \"drug-reviews-train.jsonl\",\n",
-    "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
-    "    \"test\": \"drug-reviews-test.jsonl\",\n",
-    "}\n",
-    "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Hora de fatiar e dividir os dados",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter5/section4.ipynb b/course/pt/chapter5/section4.ipynb
deleted file mode 100644
index d7d4725f..00000000
--- a/course/pt/chapter5/section4.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Big data? 🤗 Datasets ao resgate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install zstandard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['meta', 'text'],\n",
-       "    num_rows: 15518009\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\n",
-    "data_files = \"https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\n",
-    "pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "pubmed_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pubmed_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install psutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RAM used: 5678.33 MB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# Process.memory_info is expressed in bytes, so convert to megabytes\n",
-    "print(f\"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of files in dataset : 20979437051\n",
-       "Dataset size (cache file) : 19.54 GB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(f\"Number of files in dataset : {pubmed_dataset.dataset_size}\")\n",
-    "size_gb = pubmed_dataset.dataset_size / (1024**3)\n",
-    "print(f\"Dataset size (cache file) : {size_gb:.2f} GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Iterated over 15518009 examples (about 19.5 GB) in 64.2s, i.e. 0.304 GB/s'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import timeit\n",
-    "\n",
-    "code_snippet = \"\"\"batch_size = 1000\n",
-    "\n",
-    "for idx in range(0, len(pubmed_dataset), batch_size):\n",
-    "    _ = pubmed_dataset[idx:idx + batch_size]\n",
-    "\"\"\"\n",
-    "\n",
-    "time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())\n",
-    "print(\n",
-    "    f\"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in \"\n",
-    "    f\"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset_streamed = load_dataset(\n",
-    "    \"json\", data_files=data_files, split=\"train\", streaming=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iter(pubmed_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 4958, 5178, 4328, 6779, ...], 'attention_mask': [1, 1, 1, 1, 1, ...]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
-    "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"text\"]))\n",
-    "next(iter(tokenized_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11410799, 'language': 'eng'},\n",
-       " 'text': 'Randomized study of dose or schedule modification of granulocyte colony-stimulating factor in platinum-based chemotherapy for elderly patients with lung cancer ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)\n",
-    "next(iter(shuffled_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'pmid': 11409575, 'language': 'eng'},\n",
-       "  'text': 'Clinical signs of hypoxaemia in children with acute lower respiratory infection: indicators of oxygen therapy ...'},\n",
-       " {'meta': {'pmid': 11409576, 'language': 'eng'},\n",
-       "  'text': \"Hypoxaemia in children with severe pneumonia in Papua New Guinea ...\"},\n",
-       " {'meta': {'pmid': 11409577, 'language': 'eng'},\n",
-       "  'text': 'Oxygen concentrators and cylinders ...'},\n",
-       " {'meta': {'pmid': 11409578, 'language': 'eng'},\n",
-       "  'text': 'Oxygen supply in rural africa: a personal experience ...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_head = pubmed_dataset_streamed.take(5)\n",
-    "list(dataset_head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Skip the first 1,000 examples and include the rest in the training set\n",
-    "train_dataset = shuffled_dataset.skip(1000)\n",
-    "# Take the first 1,000 examples for the validation set\n",
-    "validation_dataset = shuffled_dataset.take(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'case_ID': '110921.json',\n",
-       "  'case_jurisdiction': 'scotus.tar.gz',\n",
-       "  'date_created': '2010-04-28T17:12:49Z'},\n",
-       " 'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "law_dataset_streamed = load_dataset(\n",
-    "    \"json\",\n",
-    "    data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\n",
-    "    split=\"train\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "next(iter(law_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'case_ID': '110921.json',\n",
-       "   'case_jurisdiction': 'scotus.tar.gz',\n",
-       "   'date_created': '2010-04-28T17:12:49Z'},\n",
-       "  'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from itertools import islice\n",
-    "from datasets import interleave_datasets\n",
-    "\n",
-    "combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])\n",
-    "list(islice(combined_dataset, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pile_set_name': 'Pile-CC'},\n",
-       " 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_url = \"https://the-eye.eu/public/AI/pile/\"\n",
-    "data_files = {\n",
-    "    \"train\": [base_url + \"train/\" + f\"{idx:02d}.jsonl.zst\" for idx in range(30)],\n",
-    "    \"validation\": base_url + \"val.jsonl.zst\",\n",
-    "    \"test\": base_url + \"test.jsonl.zst\",\n",
-    "}\n",
-    "pile_dataset = load_dataset(\"json\", data_files=data_files, streaming=True)\n",
-    "next(iter(pile_dataset[\"train\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Big data? 🤗 Datasets ao resgate",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter5/section5.ipynb b/course/pt/chapter5/section5.ipynb
deleted file mode 100644
index ceff9b0e..00000000
--- a/course/pt/chapter5/section5.ipynb
+++ /dev/null
@@ -1,524 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Criando seu próprio dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install requests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n",
-    "response = requests.get(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.status_code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'repository_url': 'https://api.github.com/repos/huggingface/datasets',\n",
-       "  'labels_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/labels{/name}',\n",
-       "  'comments_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/comments',\n",
-       "  'events_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/events',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "  'id': 968650274,\n",
-       "  'node_id': 'MDExOlB1bGxSZXF1ZXN0NzEwNzUyMjc0',\n",
-       "  'number': 2792,\n",
-       "  'title': 'Update GooAQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'labels': [],\n",
-       "  'state': 'open',\n",
-       "  'locked': False,\n",
-       "  'assignee': None,\n",
-       "  'assignees': [],\n",
-       "  'milestone': None,\n",
-       "  'comments': 1,\n",
-       "  'created_at': '2021-08-12T11:40:18Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'closed_at': None,\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'active_lock_reason': None,\n",
-       "  'pull_request': {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/2792',\n",
-       "   'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "   'diff_url': 'https://github.com/huggingface/datasets/pull/2792.diff',\n",
-       "   'patch_url': 'https://github.com/huggingface/datasets/pull/2792.patch'},\n",
-       "  'body': '[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.',\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "GITHUB_TOKEN = xxx  # Copy your GitHub token here\n",
-    "headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import math\n",
-    "from pathlib import Path\n",
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "\n",
-    "def fetch_issues(\n",
-    "    owner=\"huggingface\",\n",
-    "    repo=\"datasets\",\n",
-    "    num_issues=10_000,\n",
-    "    rate_limit=5_000,\n",
-    "    issues_path=Path(\".\"),\n",
-    "):\n",
-    "    if not issues_path.is_dir():\n",
-    "        issues_path.mkdir(exist_ok=True)\n",
-    "\n",
-    "    batch = []\n",
-    "    all_issues = []\n",
-    "    per_page = 100  # Number of issues to return per page\n",
-    "    num_pages = math.ceil(num_issues / per_page)\n",
-    "    base_url = \"https://api.github.com/repos\"\n",
-    "\n",
-    "    for page in tqdm(range(num_pages)):\n",
-    "        # Query with state=all to get both open and closed issues\n",
-    "        query = f\"issues?page={page}&per_page={per_page}&state=all\"\n",
-    "        issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n",
-    "        batch.extend(issues.json())\n",
-    "\n",
-    "        if len(batch) > rate_limit and len(all_issues) < num_issues:\n",
-    "            all_issues.extend(batch)\n",
-    "            batch = []  # Flush batch for next time period\n",
-    "            print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n",
-    "            time.sleep(60 * 60 + 1)\n",
-    "\n",
-    "    all_issues.extend(batch)\n",
-    "    df = pd.DataFrame.from_records(all_issues)\n",
-    "    df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n",
-    "    print(\n",
-    "        f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Depending on your internet connection, this can take several minutes to run...\n",
-    "fetch_issues()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'timeline_url', 'performed_via_github_app'],\n",
-       "    num_rows: 3019\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = load_dataset(\"json\", data_files=\"datasets-issues.jsonl\", split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">> URL: https://github.com/huggingface/datasets/pull/850\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/850', 'html_url': 'https://github.com/huggingface/datasets/pull/850', 'diff_url': 'https://github.com/huggingface/datasets/pull/850.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/850.patch'}\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/issues/2773\n",
-       ">> Pull request: None\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/pull/783\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/783', 'html_url': 'https://github.com/huggingface/datasets/pull/783', 'diff_url': 'https://github.com/huggingface/datasets/pull/783.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/783.patch'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = issues_dataset.shuffle(seed=666).select(range(3))\n",
-    "\n",
-    "# Print out the URL and pull request entries\n",
-    "for url, pr in zip(sample[\"html_url\"], sample[\"pull_request\"]):\n",
-    "    print(f\">> URL: {url}\")\n",
-    "    print(f\">> Pull request: {pr}\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"is_pull_request\": False if x[\"pull_request\"] is None else True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792#issuecomment-897594128',\n",
-       "  'issue_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'id': 897594128,\n",
-       "  'node_id': 'IC_kwDODunzps41gDMQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'created_at': '2021-08-12T12:21:52Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'body': \"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issue_number = 2792\n",
-    "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "response = requests.get(url, headers=headers)\n",
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\"]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def get_comments(issue_number):\n",
-    "    url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "    response = requests.get(url, headers=headers)\n",
-    "    return [r[\"body\"] for r in response.json()]\n",
-    "\n",
-    "\n",
-    "# Test our function works as expected\n",
-    "get_comments(2792)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Depending on your internet connection, this can take a few minutes...\n",
-    "issues_with_comments_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"comments\": get_comments(x[\"number\"])}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_with_comments_dataset.to_json(\"issues-datasets-with-comments.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of datasets on Hub: 1487\n",
-       "Dataset Name: acronym_identification, Tags: ['annotations_creators:expert-generated', 'language_creators:found', 'languages:en', 'licenses:mit', 'multilinguality:monolingual', 'size_categories:10K<n<100K', 'source_datasets:original', 'task_categories:structure-prediction', 'task_ids:structure-prediction-other-acronym-identification']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_datasets\n",
-    "\n",
-    "all_datasets = list_datasets()\n",
-    "print(f\"Number of datasets on Hub: {len(all_datasets)}\")\n",
-    "print(all_datasets[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/datasets/lewtun/github-issues'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "repo_url = create_repo(name=\"github-issues\", repo_type=\"dataset\")\n",
-    "repo_url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)\n",
-    "!cp datasets-issues-with-comments.jsonl github-issues/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.lfs_track(\"*.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remote_dataset = load_dataset(\"lewtun/github-issues\", split=\"train\")\n",
-    "remote_dataset"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Criando seu próprio dataset",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter5/section6_pt.ipynb b/course/pt/chapter5/section6_pt.ipynb
deleted file mode 100644
index 4f5598f1..00000000
--- a/course/pt/chapter5/section6_pt.ipynb
+++ /dev/null
@@ -1,518 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Busca semântica com o FAISS (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = AutoModel.from_pretrained(model_ckpt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
-    "    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).detach().cpu().numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Busca semântica com o FAISS (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter5/section6_tf.ipynb b/course/pt/chapter5/section6_tf.ipynb
deleted file mode 100644
index 139c702e..00000000
--- a/course/pt/chapter5/section6_tf.ipynb
+++ /dev/null
@@ -1,506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Busca semântica com o FAISS (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
-    "    )\n",
-    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Busca semântica com o FAISS (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter5/section8.ipynb b/course/pt/chapter5/section8.ipynb
deleted file mode 100644
index af212255..00000000
--- a/course/pt/chapter5/section8.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Questionário de fim de capítulo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"allocine\", streaming=True, split=\"train\")\n",
-    "dataset[0]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Questionário de fim de capítulo",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter6/section2.ipynb b/course/pt/chapter6/section2.ipynb
deleted file mode 100644
index a4beac75..00000000
--- a/course/pt/chapter6/section2.ipynb
+++ /dev/null
@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Treinando um novo tokenizador"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Isto pode levar alguns minutos para carregar, então pegue um copo de café enquanto espera!\n",
-    "raw_datasets = load_dataset(\"code_search_net\", \"python\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['repository_name', 'func_path_in_repository', 'func_name', 'whole_func_string', 'language', \n",
-       "      'func_code_string', 'func_code_tokens', 'func_documentation_string', 'func_documentation_tokens', 'split_name', \n",
-       "      'func_code_url'\n",
-       "    ],\n",
-       "    num_rows: 412178\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(raw_datasets[\"train\"][123456][\"whole_func_string\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Não remova o comentário da linha abaixo, a menos que o teu dataset seja pequeno!\n",
-    "# training_corpus = [raw_datasets[\"train\"][i: i + 1000][\"whole_func_string\"] for i in range(0, len(raw_datasets[\"train\"]), 1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_corpus = (\n",
-    "    raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "    for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
-       "[]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen = (i for i in range(10))\n",
-    "print(list(gen))\n",
-    "print(list(gen))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    return (\n",
-    "        raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "        for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "training_corpus = get_training_corpus()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    dataset = raw_datasets[\"train\"]\n",
-    "    for start_idx in range(0, len(dataset), 1000):\n",
-    "        samples = dataset[start_idx : start_idx + 1000]\n",
-    "        yield samples[\"whole_func_string\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "old_tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'n', 'umbers', '(', 'a', ',', 'Ġb', '):', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo',\n",
-       " 'Ġnumbers', 'Ġ`', 'a', '`', 'Ġand', 'Ġ`', 'b', '`', '.\"', '\"\"', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = '''def add_numbers(a, b):\n",
-    "    \"\"\"Add the two numbers `a` and `b`.\"\"\"\n",
-    "    return a + b'''\n",
-    "\n",
-    "tokens = old_tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'numbers', '(', 'a', ',', 'Ġb', '):', 'ĊĠĠĠ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo', 'Ġnumbers', 'Ġ`',\n",
-       " 'a', '`', 'Ġand', 'Ġ`', 'b', '`.\"\"\"', 'ĊĠĠĠ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokens = tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27\n",
-       "36"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(len(tokens))\n",
-    "print(len(old_tokenizer.tokenize(example)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['class', 'ĠLinear', 'Layer', '():', 'ĊĠĠĠ', 'Ġdef', 'Ġ__', 'init', '__(', 'self', ',', 'Ġinput', '_', 'size', ',',\n",
-       " 'Ġoutput', '_', 'size', '):', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'weight', 'Ġ=', 'Ġtorch', '.', 'randn', '(', 'input', '_',\n",
-       " 'size', ',', 'Ġoutput', '_', 'size', ')', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'bias', 'Ġ=', 'Ġtorch', '.', 'zeros', '(',\n",
-       " 'output', '_', 'size', ')', 'ĊĊĠĠĠ', 'Ġdef', 'Ġ__', 'call', '__(', 'self', ',', 'Ġx', '):', 'ĊĠĠĠĠĠĠĠ',\n",
-       " 'Ġreturn', 'Ġx', 'Ġ@', 'Ġself', '.', 'weights', 'Ġ+', 'Ġself', '.', 'bias', 'ĊĠĠĠĠ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = \"\"\"class LinearLayer():\n",
-    "    def __init__(self, input_size, output_size):\n",
-    "        self.weight = torch.randn(input_size, output_size)\n",
-    "        self.bias = torch.zeros(output_size)\n",
-    "\n",
-    "    def __call__(self, x):\n",
-    "        return x @ self.weights + self.bias\n",
-    "    \"\"\"\n",
-    "tokenizer.tokenize(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Substitua \"huggingface-course\" abaixo pelo seu namespace real para usar seu próprio tokenizador\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Treinando um novo tokenizador",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter6/section3_pt.ipynb b/course/pt/chapter6/section3_pt.ipynb
deleted file mode 100644
index 65ab40d4..00000000
--- a/course/pt/chapter6/section3_pt.ipynb
+++ /dev/null
@@ -1,515 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Os poderes especiais dos tokenizadores rápidos (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"pt\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 19])\n",
-       "torch.Size([1, 19, 9])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()\n",
-    "predictions = outputs.logits.argmax(dim=-1)[0].tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Removendo o B- ou I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Vamos pegar todos os tokens rotulados com I-\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # A pontuação é a média de todas as pontuações dos tokens da entidade agrupada\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Os poderes especiais dos tokenizadores rápidos (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter6/section3_tf.ipynb b/course/pt/chapter6/section3_tf.ipynb
deleted file mode 100644
index ea8cd9d8..00000000
--- a/course/pt/chapter6/section3_tf.ipynb
+++ /dev/null
@@ -1,517 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Os poderes especiais dos tokenizadores rápidos (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = TFAutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"tf\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 19)\n",
-       "(1, 19, 9)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "probabilities = tf.math.softmax(outputs.logits, axis=-1)[0]\n",
-    "probabilities = probabilities.numpy().tolist()\n",
-    "predictions = tf.math.argmax(outputs.logits, axis=-1)[0]\n",
-    "predictions = predictions.numpy().tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Removendo o B- ou I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Vamos pegar todos os tokens rotulados com I-\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # A pontuação é a média de todas as pontuações dos tokens da entidade agrupada\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Os poderes especiais dos tokenizadores rápidos (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter8/section2.ipynb b/course/pt/chapter8/section2.ipynb
deleted file mode 100644
index d01ecf23..00000000
--- a/course/pt/chapter8/section2.ipynb
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# O que fazer quando ocorrer um erro"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Clone the repo and extract the local path\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Create an empty repo on the Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Clone the empty repo\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Copy files\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Push to Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.gitattributes', 'README.md', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer_config.json', 'training_args.bin', 'vocab.txt']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.38669535517692566,\n",
-       " 'start': 34,\n",
-       " 'end': 95,\n",
-       " 'answer': 'the task of extracting an answer from a text given a question'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "---------------------------------------------------------------------------\n",
-       "AttributeError                            Traceback (most recent call last)\n",
-       "/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in <module>\n",
-       "      1 inputs = tokenizer(question, text, add_special_tokens=True)\n",
-       "      2 input_ids = inputs[\"input_ids\"]\n",
-       "----> 3 outputs = model(**inputs)\n",
-       "      4 answer_start_scores = outputs.start_logits\n",
-       "      5 answer_end_scores = outputs.end_logits\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\n",
-       "    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
-       "    724\n",
-       "--> 725         distilbert_output = self.distilbert(\n",
-       "    726             input_ids=input_ids,\n",
-       "    727             attention_mask=attention_mask,\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\n",
-       "    471             raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n",
-       "    472         elif input_ids is not None:\n",
-       "--> 473             input_shape = input_ids.size()\n",
-       "    474         elif inputs_embeds is not None:\n",
-       "    475             input_shape = inputs_embeds.size()[:-1]\n",
-       "\n",
-       "AttributeError: 'list' object has no attribute 'size'\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Get the most likely beginning of answer with the argmax of the score\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Get the most likely end of answer with the argmax of the score\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 2029, 7705, 2015, 2064]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "O que fazer quando ocorrer um erro",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/pt/chapter8/section3.ipynb b/course/pt/chapter8/section3.ipynb
deleted file mode 100644
index ffba96dc..00000000
--- a/course/pt/chapter8/section3.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pedindo ajuda nos fóruns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModel.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"\"\"\n",
-    "Generation One is a retroactive term for the Transformers characters that\n",
-    "appeared between 1984 and 1993. The Transformers began with the 1980s Japanese\n",
-    "toy lines Micro Change and Diaclone. They presented robots able to transform\n",
-    "into everyday vehicles, electronic items or weapons. Hasbro bought the Micro\n",
-    "Change and Diaclone toys, and partnered with Takara. Marvel Comics was hired by\n",
-    "Hasbro to create the backstory; editor-in-chief Jim Shooter wrote an overall\n",
-    "story, and gave the task of creating the characthers to writer Dennis O'Neil.\n",
-    "Unhappy with O'Neil's work (although O'Neil created the name \"Optimus Prime\"),\n",
-    "Shooter chose Bob Budiansky to create the characters.\n",
-    "\n",
-    "The Transformers mecha were largely designed by Shōji Kawamori, the creator of\n",
-    "the Japanese mecha anime franchise Macross (which was adapted into the Robotech\n",
-    "franchise in North America). Kawamori came up with the idea of transforming\n",
-    "mechs while working on the Diaclone and Macross franchises in the early 1980s\n",
-    "(such as the VF-1 Valkyrie in Macross and Robotech), with his Diaclone mechs\n",
-    "later providing the basis for Transformers.\n",
-    "\n",
-    "The primary concept of Generation One is that the heroic Optimus Prime, the\n",
-    "villainous Megatron, and their finest soldiers crash land on pre-historic Earth\n",
-    "in the Ark and the Nemesis before awakening in 1985, Cybertron hurtling through\n",
-    "the Neutral zone as an effect of the war. The Marvel comic was originally part\n",
-    "of the main Marvel Universe, with appearances from Spider-Man and Nick Fury,\n",
-    "plus some cameos, as well as a visit to the Savage Land.\n",
-    "\n",
-    "The Transformers TV series began around the same time. Produced by Sunbow\n",
-    "Productions and Marvel Productions, later Hasbro Productions, from the start it\n",
-    "contradicted Budiansky's backstories. The TV series shows the Autobots looking\n",
-    "for new energy sources, and crash landing as the Decepticons attack. Marvel\n",
-    "interpreted the Autobots as destroying a rogue asteroid approaching Cybertron.\n",
-    "Shockwave is loyal to Megatron in the TV series, keeping Cybertron in a\n",
-    "stalemate during his absence, but in the comic book he attempts to take command\n",
-    "of the Decepticons. The TV series would also differ wildly from the origins\n",
-    "Budiansky had created for the Dinobots, the Decepticon turned Autobot Jetfire\n",
-    "(known as Skyfire on TV), the Constructicons (who combine to form\n",
-    "Devastator),[19][20] and Omega Supreme. The Marvel comic establishes early on\n",
-    "that Prime wields the Creation Matrix, which gives life to machines. In the\n",
-    "second season, the two-part episode The Key to Vector Sigma introduced the\n",
-    "ancient Vector Sigma computer, which served the same original purpose as the\n",
-    "Creation Matrix (giving life to Transformers), and its guardian Alpha Trion.\n",
-    "\"\"\"\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "logits = model(**inputs).logits"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Pedindo ajuda nos fóruns",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter1/section3.ipynb b/course/ru/chapter1/section3.ipynb
deleted file mode 100644
index f6249ebe..00000000
--- a/course/ru/chapter1/section3.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Трансформеры, на что они способны?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Трансформеры, на что они способны?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter1/section8.ipynb b/course/ru/chapter1/section8.ipynb
deleted file mode 100644
index e4d8fad6..00000000
--- a/course/ru/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Предвзятости и ограничения"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Предвзятости и ограничения",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter2/section2_pt.ipynb b/course/ru/chapter2/section2_pt.ipynb
deleted file mode 100644
index 43a929fe..00000000
--- a/course/ru/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Внутри конвейера (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Внутри конвейера (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter2/section2_tf.ipynb b/course/ru/chapter2/section2_tf.ipynb
deleted file mode 100644
index d8bbb0de..00000000
--- a/course/ru/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Внутри конвейера (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Внутри конвейера (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter2/section3_pt.ipynb b/course/ru/chapter2/section3_pt.ipynb
deleted file mode 100644
index 47fb2cbb..00000000
--- a/course/ru/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Модели (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# Модель инициализируется случайным образом!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Модели (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter2/section3_tf.ipynb b/course/ru/chapter2/section3_tf.ipynb
deleted file mode 100644
index 8dc00fa4..00000000
--- a/course/ru/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Модели (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# Модель инициализируется случайным образом!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Модели (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter3/section2_pt.ipynb b/course/ru/chapter3/section2_pt.ipynb
deleted file mode 100644
index 546739bb..00000000
--- a/course/ru/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Предобработка данных (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Так же, как и в прошлый раз\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Эта часть новая\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Предобработка данных (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter3/section2_tf.ipynb b/course/ru/chapter3/section2_tf.ipynb
deleted file mode 100644
index 0a306337..00000000
--- a/course/ru/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Предобработка данных (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Так же, как и в прошлый раз\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# Эта часть новая\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Предобработка данных (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter3/section3.ipynb b/course/ru/chapter3/section3.ipynb
deleted file mode 100644
index a948d0f3..00000000
--- a/course/ru/chapter3/section3.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-tuning модели с использованием Trainer API"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Fine-tuning модели с использованием Trainer API",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter3/section3_tf.ipynb b/course/ru/chapter3/section3_tf.ipynb
deleted file mode 100644
index 294234c2..00000000
--- a/course/ru/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-tuning модели с использованием Trainer API"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Fine-tuning модели с использованием Trainer API",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter3/section4.ipynb b/course/ru/chapter3/section4.ipynb
deleted file mode 100644
index 527df9f3..00000000
--- a/course/ru/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Полное обучение модели"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Полное обучение модели",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter4/section2_pt.ipynb b/course/ru/chapter4/section2_pt.ipynb
deleted file mode 100644
index 0bd28071..00000000
--- a/course/ru/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Использование предобученных моделей (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Использование предобученных моделей (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter4/section2_tf.ipynb b/course/ru/chapter4/section2_tf.ipynb
deleted file mode 100644
index bfd36dce..00000000
--- a/course/ru/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Использование предобученных моделей (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Использование предобученных моделей (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter4/section3_pt.ipynb b/course/ru/chapter4/section3_pt.ipynb
deleted file mode 100644
index c5d18bcf..00000000
--- a/course/ru/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Публикация предобученных моделей в общий доступ (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Пользовательские настройки\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Создание и управление репозиториями\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # И несколько способов для получения или изменения информации о содержимом\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Сделайте с моделью что угодно: обучите с нуля, дообучите и тд\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Публикация предобученных моделей в общий доступ (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter4/section3_tf.ipynb b/course/ru/chapter4/section3_tf.ipynb
deleted file mode 100644
index 0b9cb6c5..00000000
--- a/course/ru/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Публикация предобученных моделей в общий доступ (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Пользовательские настройки\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Создание и управление репозиториями\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # И несколько способов для получения или изменения информации о содержимом\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Публикация предобученных моделей в общий доступ (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter5/section2.ipynb b/course/ru/chapter5/section2.ipynb
deleted file mode 100644
index c3fc51da..00000000
--- a/course/ru/chapter5/section2.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Что делать, если моего датасета на нет на Hub?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz\n",
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gzip -dkv SQuAD_it-*.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=\"SQuAD_it-train.json\", field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    \"title\": \"Terremoto del Sichuan del 2008\",\n",
-       "    \"paragraphs\": [\n",
-       "        {\n",
-       "            \"context\": \"Il terremoto del Sichuan del 2008 o il terremoto...\",\n",
-       "            \"qas\": [\n",
-       "                {\n",
-       "                    \"answers\": [{\"answer_start\": 29, \"text\": \"2008\"}],\n",
-       "                    \"id\": \"56cdca7862d2951400fa6826\",\n",
-       "                    \"question\": \"In quale anno si è verificato il terremoto nel Sichuan?\",\n",
-       "                },\n",
-       "                ...\n",
-       "            ],\n",
-       "        },\n",
-       "        ...\n",
-       "    ],\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 48\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json\", \"test\": \"SQuAD_it-test.json\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")\n",
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json.gz\", \"test\": \"SQuAD_it-test.json.gz\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/crux82/squad-it/raw/master/\"\n",
-    "data_files = {\n",
-    "    \"train\": url + \"SQuAD_it-train.json.gz\",\n",
-    "    \"test\": url + \"SQuAD_it-test.json.gz\",\n",
-    "}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Что делать, если моего датасета на нет на Hub?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter5/section3.ipynb b/course/ru/chapter5/section3.ipynb
deleted file mode 100644
index f1b6207e..00000000
--- a/course/ru/chapter5/section3.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Препарируем 🤗 Datasets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
-    "!unzip drugsCom_raw.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
-    "# \\t is the tab character in Python\n",
-    "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Unnamed: 0': [87571, 178045, 80482],\n",
-       " 'drugName': ['Naproxen', 'Duloxetine', 'Mobic'],\n",
-       " 'condition': ['Gout, Acute', 'ibromyalgia', 'Inflammatory Conditions'],\n",
-       " 'review': ['\"like the previous person mention, I&#039;m a strong believer of aleve, it works faster for my gout than the prescription meds I take. No more going to the doctor for refills.....Aleve works!\"',\n",
-       "  '\"I have taken Cymbalta for about a year and a half for fibromyalgia pain. It is great\\r\\nas a pain reducer and an anti-depressant, however, the side effects outweighed \\r\\nany benefit I got from it. I had trouble with restlessness, being tired constantly,\\r\\ndizziness, dry mouth, numbness and tingling in my feet, and horrible sweating. I am\\r\\nbeing weaned off of it now. Went from 60 mg to 30mg and now to 15 mg. I will be\\r\\noff completely in about a week. The fibro pain is coming back, but I would rather deal with it than the side effects.\"',\n",
-       "  '\"I have been taking Mobic for over a year with no side effects other than an elevated blood pressure.  I had severe knee and ankle pain which completely went away after taking Mobic.  I attempted to stop the medication however pain returned after a few days.\"'],\n",
-       " 'rating': [9.0, 3.0, 10.0],\n",
-       " 'date': ['September 2, 2015', 'November 7, 2011', 'June 5, 2013'],\n",
-       " 'usefulCount': [36, 13, 128]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
-    "# Peek at the first few examples\n",
-    "drug_sample[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split in drug_dataset.keys():\n",
-    "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 161297\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 53766\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.rename_column(\n",
-    "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
-    ")\n",
-    "drug_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AttributeError: 'NoneType' object has no attribute 'lower'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def lowercase_condition(example):\n",
-    "    return {\"condition\": example[\"condition\"].lower()}\n",
-    "\n",
-    "\n",
-    "drug_dataset.map(lowercase_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_nones(x):\n",
-    "    return x[\"condition\"] is not None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x * x)(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda base, height: 0.5 * base * height)(4, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['left ventricular dysfunction', 'adhd', 'birth control']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(lowercase_condition)\n",
-    "# Check that lowercasing worked\n",
-    "drug_dataset[\"train\"][\"condition\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_review_length(example):\n",
-    "    return {\"review_length\": len(example[\"review\"].split())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': 206461,\n",
-       " 'drugName': 'Valsartan',\n",
-       " 'condition': 'left ventricular dysfunction',\n",
-       " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
-       " 'rating': 9.0,\n",
-       " 'date': 'May 20, 2012',\n",
-       " 'usefulCount': 27,\n",
-       " 'review_length': 17}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(compute_review_length)\n",
-    "# Посмотрим на первый объект обучающей части датасета\n",
-    "drug_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': [103488, 23627, 20558],\n",
-       " 'drugName': ['Loestrin 21 1 / 20', 'Chlorzoxazone', 'Nucynta'],\n",
-       " 'condition': ['birth control', 'muscle spasm', 'pain'],\n",
-       " 'review': ['\"Excellent.\"', '\"useless\"', '\"ok\"'],\n",
-       " 'rating': [10.0, 1.0, 6.0],\n",
-       " 'date': ['November 4, 2008', 'March 24, 2017', 'August 20, 2016'],\n",
-       " 'usefulCount': [5, 2, 10],\n",
-       " 'review_length': [1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'train': 138514, 'test': 46108}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
-    "print(drug_dataset.num_rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm a transformer called BERT\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import html\n",
-    "\n",
-    "text = \"I&#039;m a transformer called BERT\"\n",
-    "html.unescape(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_drug_dataset = drug_dataset.map(\n",
-    "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "\n",
-    "def tokenize_function(examples):\n",
-    "    return tokenizer(examples[\"review\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%time tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
-    "\n",
-    "\n",
-    "def slow_tokenize_function(examples):\n",
-    "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    return tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[128, 49]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
-    "[len(inp) for inp in result[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ArrowInvalid: Column 1 named condition expected length 1463 but got length 1000"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(\n",
-    "    tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(206772, 138514)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    result = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    # Extract mapping between new and old indices\n",
-    "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
-    "    for key, values in examples.items():\n",
-    "        result[key] = [values[i] for i in sample_map]\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 206772\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 68876\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.set_format(\"pandas\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df = drug_dataset[\"train\"][:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frequencies = (\n",
-    "    train_df[\"condition\"]\n",
-    "    .value_counts()\n",
-    "    .to_frame()\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
-    ")\n",
-    "frequencies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['condition', 'frequency'],\n",
-       "    num_rows: 819\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "freq_dataset = Dataset.from_pandas(frequencies)\n",
-    "freq_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
-    "# Переименуем \"test\"  в \"validation\"\n",
-    "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
-    "# Добавим \"test\" в наш `DatasetDict`\n",
-    "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
-    "drug_dataset_clean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_from_disk\n",
-    "\n",
-    "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
-    "drug_dataset_reloaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split, dataset in drug_dataset_clean.items():\n",
-    "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\"patient_id\":141780,\"drugName\":\"Escitalopram\",\"condition\":\"depression\",\"review\":\"\\\"I seemed to experience the regular side effects of LEXAPRO, insomnia, low sex drive, sleepiness during the day. I am taking it at night because my doctor said if it made me tired to take it at night. I assumed it would and started out taking it at night. Strange dreams, some pleasant. I was diagnosed with fibromyalgia. Seems to be helping with the pain. Have had anxiety and depression in my family, and have tried quite a few other medications that haven't worked. Only have been on it for two weeks but feel more positive in my mind, want to accomplish more in my life. Hopefully the side effects will dwindle away, worth it to stick with it from hearing others responses. Great medication.\\\"\",\"rating\":9.0,\"date\":\"May 29, 2011\",\"usefulCount\":10,\"review_length\":125}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "!head -n 1 drug-reviews-train.jsonl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\n",
-    "    \"train\": \"drug-reviews-train.jsonl\",\n",
-    "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
-    "    \"test\": \"drug-reviews-test.jsonl\",\n",
-    "}\n",
-    "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Препарируем 🤗 Datasets",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter5/section4.ipynb b/course/ru/chapter5/section4.ipynb
deleted file mode 100644
index 67747af4..00000000
--- a/course/ru/chapter5/section4.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Big data? 🤗 Datasets спешат на помощь!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install zstandard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['meta', 'text'],\n",
-       "    num_rows: 15518009\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Этой займет несколько минут, пока ожидаете – сделайте кофе или чай :)\n",
-    "data_files = \"https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\n",
-    "pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "pubmed_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pubmed_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install psutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RAM used: 5678.33 MB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# Process.memory_info вовзращает объем в байтах, мы пересчитаем в мегабайты\n",
-    "print(f\"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of files in dataset : 20979437051\n",
-       "Dataset size (cache file) : 19.54 GB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(f\"Number of files in dataset : {pubmed_dataset.dataset_size}\")\n",
-    "size_gb = pubmed_dataset.dataset_size / (1024**3)\n",
-    "print(f\"Dataset size (cache file) : {size_gb:.2f} GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Iterated over 15518009 examples (about 19.5 GB) in 64.2s, i.e. 0.304 GB/s'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import timeit\n",
-    "\n",
-    "code_snippet = \"\"\"batch_size = 1000\n",
-    "\n",
-    "for idx in range(0, len(pubmed_dataset), batch_size):\n",
-    "    _ = pubmed_dataset[idx:idx + batch_size]\n",
-    "\"\"\"\n",
-    "\n",
-    "time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())\n",
-    "print(\n",
-    "    f\"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in \"\n",
-    "    f\"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset_streamed = load_dataset(\n",
-    "    \"json\", data_files=data_files, split=\"train\", streaming=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iter(pubmed_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 4958, 5178, 4328, 6779, ...], 'attention_mask': [1, 1, 1, 1, 1, ...]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
-    "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"text\"]))\n",
-    "next(iter(tokenized_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11410799, 'language': 'eng'},\n",
-       " 'text': 'Randomized study of dose or schedule modification of granulocyte colony-stimulating factor in platinum-based chemotherapy for elderly patients with lung cancer ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)\n",
-    "next(iter(shuffled_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'pmid': 11409575, 'language': 'eng'},\n",
-       "  'text': 'Clinical signs of hypoxaemia in children with acute lower respiratory infection: indicators of oxygen therapy ...'},\n",
-       " {'meta': {'pmid': 11409576, 'language': 'eng'},\n",
-       "  'text': \"Hypoxaemia in children with severe pneumonia in Papua New Guinea ...\"},\n",
-       " {'meta': {'pmid': 11409577, 'language': 'eng'},\n",
-       "  'text': 'Oxygen concentrators and cylinders ...'},\n",
-       " {'meta': {'pmid': 11409578, 'language': 'eng'},\n",
-       "  'text': 'Oxygen supply in rural africa: a personal experience ...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_head = pubmed_dataset_streamed.take(5)\n",
-    "list(dataset_head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Пропустить первые 1000 объектов и включить остальные в обучающую выборку\n",
-    "train_dataset = shuffled_dataset.skip(1000)\n",
-    "# Взять первые 1000 объектов в валидационную выборку\n",
-    "validation_dataset = shuffled_dataset.take(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'case_ID': '110921.json',\n",
-       "  'case_jurisdiction': 'scotus.tar.gz',\n",
-       "  'date_created': '2010-04-28T17:12:49Z'},\n",
-       " 'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "law_dataset_streamed = load_dataset(\n",
-    "    \"json\",\n",
-    "    data_files=\"https://mystic.the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\n",
-    "    split=\"train\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "next(iter(law_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'case_ID': '110921.json',\n",
-       "   'case_jurisdiction': 'scotus.tar.gz',\n",
-       "   'date_created': '2010-04-28T17:12:49Z'},\n",
-       "  'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from itertools import islice\n",
-    "from datasets import interleave_datasets\n",
-    "\n",
-    "combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])\n",
-    "list(islice(combined_dataset, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pile_set_name': 'Pile-CC'},\n",
-       " 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_url = \"https://mystic.the-eye.eu/public/AI/pile/\"\n",
-    "data_files = {\n",
-    "    \"train\": [base_url + \"train/\" + f\"{idx:02d}.jsonl.zst\" for idx in range(30)],\n",
-    "    \"validation\": base_url + \"val.jsonl.zst\",\n",
-    "    \"test\": base_url + \"test.jsonl.zst\",\n",
-    "}\n",
-    "pile_dataset = load_dataset(\"json\", data_files=data_files, streaming=True)\n",
-    "next(iter(pile_dataset[\"train\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Big data? 🤗 Datasets спешат на помощь!",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter5/section6_pt.ipynb b/course/ru/chapter5/section6_pt.ipynb
deleted file mode 100644
index dc292192..00000000
--- a/course/ru/chapter5/section6_pt.ipynb
+++ /dev/null
@@ -1,518 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Семантический поиск с помощью FAISS (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = AutoModel.from_pretrained(model_ckpt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
-    "    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).detach().cpu().numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Семантический поиск с помощью FAISS (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter5/section6_tf.ipynb b/course/ru/chapter5/section6_tf.ipynb
deleted file mode 100644
index dabd204d..00000000
--- a/course/ru/chapter5/section6_tf.ipynb
+++ /dev/null
@@ -1,506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Семантический поиск с помощью FAISS (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
-    "    )\n",
-    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Семантический поиск с помощью FAISS (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter5/section8.ipynb b/course/ru/chapter5/section8.ipynb
deleted file mode 100644
index 08e420a6..00000000
--- a/course/ru/chapter5/section8.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Тест по главе 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"allocine\", streaming=True, split=\"train\")\n",
-    "dataset[0]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Тест по главе 5",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/ru/chapter6/section2.ipynb b/course/ru/chapter6/section2.ipynb
deleted file mode 100644
index effd0cb1..00000000
--- a/course/ru/chapter6/section2.ipynb
+++ /dev/null
@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Обучение токенизатора на основе существующего"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Это может занять некоторое время – заварите себе чаю!\n",
-    "raw_datasets = load_dataset(\"code_search_net\", \"python\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['repository_name', 'func_path_in_repository', 'func_name', 'whole_func_string', 'language', \n",
-       "      'func_code_string', 'func_code_tokens', 'func_documentation_string', 'func_documentation_tokens', 'split_name', \n",
-       "      'func_code_url'\n",
-       "    ],\n",
-       "    num_rows: 412178\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(raw_datasets[\"train\"][123456][\"whole_func_string\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Если ваш датасет маленький – оставьте эту строку закомментированной!\n",
-    "# training_corpus = [raw_datasets[\"train\"][i: i + 1000][\"whole_func_string\"] for i in range(0, len(raw_datasets[\"train\"]), 1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_corpus = (\n",
-    "    raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "    for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
-       "[]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen = (i for i in range(10))\n",
-    "print(list(gen))\n",
-    "print(list(gen))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    return (\n",
-    "        raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "        for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "training_corpus = get_training_corpus()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    dataset = raw_datasets[\"train\"]\n",
-    "    for start_idx in range(0, len(dataset), 1000):\n",
-    "        samples = dataset[start_idx : start_idx + 1000]\n",
-    "        yield samples[\"whole_func_string\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "old_tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'n', 'umbers', '(', 'a', ',', 'Ġb', '):', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo',\n",
-       " 'Ġnumbers', 'Ġ`', 'a', '`', 'Ġand', 'Ġ`', 'b', '`', '.\"', '\"\"', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = '''def add_numbers(a, b):\n",
-    "    \"\"\"Add the two numbers `a` and `b`.\"\"\"\n",
-    "    return a + b'''\n",
-    "\n",
-    "tokens = old_tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'numbers', '(', 'a', ',', 'Ġb', '):', 'ĊĠĠĠ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo', 'Ġnumbers', 'Ġ`',\n",
-       " 'a', '`', 'Ġand', 'Ġ`', 'b', '`.\"\"\"', 'ĊĠĠĠ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokens = tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27\n",
-       "36"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(len(tokens))\n",
-    "print(len(old_tokenizer.tokenize(example)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['class', 'ĠLinear', 'Layer', '():', 'ĊĠĠĠ', 'Ġdef', 'Ġ__', 'init', '__(', 'self', ',', 'Ġinput', '_', 'size', ',',\n",
-       " 'Ġoutput', '_', 'size', '):', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'weight', 'Ġ=', 'Ġtorch', '.', 'randn', '(', 'input', '_',\n",
-       " 'size', ',', 'Ġoutput', '_', 'size', ')', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'bias', 'Ġ=', 'Ġtorch', '.', 'zeros', '(',\n",
-       " 'output', '_', 'size', ')', 'ĊĊĠĠĠ', 'Ġdef', 'Ġ__', 'call', '__(', 'self', ',', 'Ġx', '):', 'ĊĠĠĠĠĠĠĠ',\n",
-       " 'Ġreturn', 'Ġx', 'Ġ@', 'Ġself', '.', 'weights', 'Ġ+', 'Ġself', '.', 'bias', 'ĊĠĠĠĠ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = \"\"\"class LinearLayer():\n",
-    "    def __init__(self, input_size, output_size):\n",
-    "        self.weight = torch.randn(input_size, output_size)\n",
-    "        self.bias = torch.zeros(output_size)\n",
-    "\n",
-    "    def __call__(self, x):\n",
-    "        return x @ self.weights + self.bias\n",
-    "    \"\"\"\n",
-    "tokenizer.tokenize(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Измените \"huggingface-course\" на ваше название пространства\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Обучение токенизатора на основе существующего",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter1/section10.ipynb b/course/th/chapter1/section10.ipynb
deleted file mode 100644
index 6af13336..00000000
--- a/course/th/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# คำถามท้ายบท"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "คำถามท้ายบท",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter1/section3.ipynb b/course/th/chapter1/section3.ipynb
deleted file mode 100644
index ddbbc589..00000000
--- a/course/th/chapter1/section3.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformers ชื่อนี้มีดียังไง?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformers ชื่อนี้มีดียังไง?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter1/section8.ipynb b/course/th/chapter1/section8.ipynb
deleted file mode 100644
index f249e77e..00000000
--- a/course/th/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ข้อจำกัดจากอคติของข้อมูล"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "ข้อจำกัดจากอคติของข้อมูล",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section2_pt.ipynb b/course/th/chapter2/section2_pt.ipynb
deleted file mode 100644
index 0a0dc6c7..00000000
--- a/course/th/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# เบื้องหลังของ pipeline (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "เบื้องหลังของ pipeline (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section2_tf.ipynb b/course/th/chapter2/section2_tf.ipynb
deleted file mode 100644
index e37c8c0d..00000000
--- a/course/th/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# เบื้องหลังของ pipeline (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "เบื้องหลังของ pipeline (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section3_pt.ipynb b/course/th/chapter2/section3_pt.ipynb
deleted file mode 100644
index 957078b7..00000000
--- a/course/th/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# โมเดล (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# โมเดลถูกกำหนดค่าเริ่มต้นด้วยการสุ่ม!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "โมเดล (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section3_tf.ipynb b/course/th/chapter2/section3_tf.ipynb
deleted file mode 100644
index 351db9df..00000000
--- a/course/th/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# โมเดล (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# โมเดลถูกกำหนดค่าเริ่มต้นด้วยการสุ่ม!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "โมเดล (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section4_pt.ipynb b/course/th/chapter2/section4_pt.ipynb
deleted file mode 100644
index fadf2b34..00000000
--- a/course/th/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section4_tf.ipynb b/course/th/chapter2/section4_tf.ipynb
deleted file mode 100644
index 15343d71..00000000
--- a/course/th/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section5_pt.ipynb b/course/th/chapter2/section5_pt.ipynb
deleted file mode 100644
index c0f0f927..00000000
--- a/course/th/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การจัดการกับหลายๆประโยค(multiple sequences) (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การจัดการกับหลายๆประโยค(multiple sequences) (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section5_tf.ipynb b/course/th/chapter2/section5_tf.ipynb
deleted file mode 100644
index 7a97485a..00000000
--- a/course/th/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การจัดการกับหลายๆประโยค(multiple sequences) (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การจัดการกับหลายๆประโยค(multiple sequences) (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section6_pt.ipynb b/course/th/chapter2/section6_pt.ipynb
deleted file mode 100644
index 79ce78bd..00000000
--- a/course/th/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ประกอบทุกอย่างเข้าด้วยกัน (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# จะเติมประโยคไปจนถึงความยาวที่ยาวที่สุดของประโยค\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# จะเติมประโยคไปจนถึงความยาวที่ยาวที่สุดที่โมเดลรับได้\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# จะเติมประโยคไปจนถึงความยาวที่ยาวที่สุดที่ระบุไว้\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# จะตัดประโยคที่มีความยาวเกินกว่าความยาวที่โมเดลรับได้\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# จะตัดประโยคที่มีความยาวเกินกว่าความยาวที่ระบุไว้\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "ประกอบทุกอย่างเข้าด้วยกัน (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section6_tf.ipynb b/course/th/chapter2/section6_tf.ipynb
deleted file mode 100644
index e6f214ad..00000000
--- a/course/th/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ประกอบทุกอย่างเข้าด้วยกัน (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# จะเติมประโยคไปจนถึงความยาวที่ยาวที่สุดของประโยค\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# จะเติมประโยคไปจนถึงความยาวที่ยาวที่สุดที่โมเดลรับได้\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# จะเติมประโยคไปจนถึงความยาวที่ยาวที่สุดที่ระบุไว้\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# จะตัดประโยคที่มีความยาวเกินกว่าความยาวที่โมเดลรับได้\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# จะตัดประโยคที่มีความยาวเกินกว่าความยาวที่ระบุไว้\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "ประกอบทุกอย่างเข้าด้วยกัน (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section8_pt.ipynb b/course/th/chapter2/section8_pt.ipynb
deleted file mode 100644
index 5f6cf779..00000000
--- a/course/th/chapter2/section8_pt.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# แบบทดสอบท้ายบท (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = AutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "แบบทดสอบท้ายบท (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter2/section8_tf.ipynb b/course/th/chapter2/section8_tf.ipynb
deleted file mode 100644
index 3d36a00a..00000000
--- a/course/th/chapter2/section8_tf.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# แบบทดสอบท้ายบท (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = TFAutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "แบบทดสอบท้ายบท (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter3/section2_pt.ipynb b/course/th/chapter3/section2_pt.ipynb
deleted file mode 100644
index 41ac8f78..00000000
--- a/course/th/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การประมวลผลข้อมูล (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# This is new\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การประมวลผลข้อมูล (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter3/section2_tf.ipynb b/course/th/chapter3/section2_tf.ipynb
deleted file mode 100644
index 7e3600fa..00000000
--- a/course/th/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การประมวลผลข้อมูล (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การประมวลผลข้อมูล (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter3/section3.ipynb b/course/th/chapter3/section3.ipynb
deleted file mode 100644
index a4564608..00000000
--- a/course/th/chapter3/section3.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การ Fine-tune โมเดลด้วย Trainer API หรือ Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การ Fine-tune โมเดลด้วย Trainer API หรือ Keras",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter3/section3_tf.ipynb b/course/th/chapter3/section3_tf.ipynb
deleted file mode 100644
index cffc5a61..00000000
--- a/course/th/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การ Fine-tune โมเดลด้วย Trainer API หรือ Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การ Fine-tune โมเดลด้วย Trainer API หรือ Keras",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter3/section4.ipynb b/course/th/chapter3/section4.ipynb
deleted file mode 100644
index 8d1ec943..00000000
--- a/course/th/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การเทรนโมเดลฉบับสมบูรณ์"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การเทรนโมเดลฉบับสมบูรณ์",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter4/section2_pt.ipynb b/course/th/chapter4/section2_pt.ipynb
deleted file mode 100644
index 3597a0b9..00000000
--- a/course/th/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การใช้งานโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การใช้งานโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter4/section2_tf.ipynb b/course/th/chapter4/section2_tf.ipynb
deleted file mode 100644
index 54009713..00000000
--- a/course/th/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การใช้งานโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การใช้งานโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter4/section3_pt.ipynb b/course/th/chapter4/section3_pt.ipynb
deleted file mode 100644
index 34eccbee..00000000
--- a/course/th/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การแบ่งปันโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository creation and management\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # And some methods to retrieve/change information about the content\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การแบ่งปันโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter4/section3_tf.ipynb b/course/th/chapter4/section3_tf.ipynb
deleted file mode 100644
index 34e3563e..00000000
--- a/course/th/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การแบ่งปันโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository creation and management\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # And some methods to retrieve/change information about the content\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การแบ่งปันโมเดลที่ผ่านการเทรนมาแล้ว (pretrained models) (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section2.ipynb b/course/th/chapter6/section2.ipynb
deleted file mode 100644
index d7f9a7fa..00000000
--- a/course/th/chapter6/section2.ipynb
+++ /dev/null
@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การเทรน tokenizer จาก tokenizer ที่มีอยู่แล้ว"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# This can take a few minutes to load, so grab a coffee or tea while you wait!\n",
-    "raw_datasets = load_dataset(\"code_search_net\", \"python\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['repository_name', 'func_path_in_repository', 'func_name', 'whole_func_string', 'language', \n",
-       "      'func_code_string', 'func_code_tokens', 'func_documentation_string', 'func_documentation_tokens', 'split_name', \n",
-       "      'func_code_url'\n",
-       "    ],\n",
-       "    num_rows: 412178\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(raw_datasets[\"train\"][123456][\"whole_func_string\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Don't uncomment the following line unless your dataset is small!\n",
-    "# training_corpus = [raw_datasets[\"train\"][i: i + 1000][\"whole_func_string\"] for i in range(0, len(raw_datasets[\"train\"]), 1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_corpus = (\n",
-    "    raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "    for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
-       "[]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen = (i for i in range(10))\n",
-    "print(list(gen))\n",
-    "print(list(gen))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    return (\n",
-    "        raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "        for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "training_corpus = get_training_corpus()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    dataset = raw_datasets[\"train\"]\n",
-    "    for start_idx in range(0, len(dataset), 1000):\n",
-    "        samples = dataset[start_idx : start_idx + 1000]\n",
-    "        yield samples[\"whole_func_string\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "old_tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'n', 'umbers', '(', 'a', ',', 'Ġb', '):', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo',\n",
-       " 'Ġnumbers', 'Ġ`', 'a', '`', 'Ġand', 'Ġ`', 'b', '`', '.\"', '\"\"', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = '''def add_numbers(a, b):\n",
-    "    \"\"\"Add the two numbers `a` and `b`.\"\"\"\n",
-    "    return a + b'''\n",
-    "\n",
-    "tokens = old_tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'numbers', '(', 'a', ',', 'Ġb', '):', 'ĊĠĠĠ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo', 'Ġnumbers', 'Ġ`',\n",
-       " 'a', '`', 'Ġand', 'Ġ`', 'b', '`.\"\"\"', 'ĊĠĠĠ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokens = tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27\n",
-       "36"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(len(tokens))\n",
-    "print(len(old_tokenizer.tokenize(example)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['class', 'ĠLinear', 'Layer', '():', 'ĊĠĠĠ', 'Ġdef', 'Ġ__', 'init', '__(', 'self', ',', 'Ġinput', '_', 'size', ',',\n",
-       " 'Ġoutput', '_', 'size', '):', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'weight', 'Ġ=', 'Ġtorch', '.', 'randn', '(', 'input', '_',\n",
-       " 'size', ',', 'Ġoutput', '_', 'size', ')', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'bias', 'Ġ=', 'Ġtorch', '.', 'zeros', '(',\n",
-       " 'output', '_', 'size', ')', 'ĊĊĠĠĠ', 'Ġdef', 'Ġ__', 'call', '__(', 'self', ',', 'Ġx', '):', 'ĊĠĠĠĠĠĠĠ',\n",
-       " 'Ġreturn', 'Ġx', 'Ġ@', 'Ġself', '.', 'weights', 'Ġ+', 'Ġself', '.', 'bias', 'ĊĠĠĠĠ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = \"\"\"class LinearLayer():\n",
-    "    def __init__(self, input_size, output_size):\n",
-    "        self.weight = torch.randn(input_size, output_size)\n",
-    "        self.bias = torch.zeros(output_size)\n",
-    "\n",
-    "    def __call__(self, x):\n",
-    "        return x @ self.weights + self.bias\n",
-    "    \"\"\"\n",
-    "tokenizer.tokenize(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Replace \"huggingface-course\" below with your actual namespace to use your own tokenizer\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การเทรน tokenizer จาก tokenizer ที่มีอยู่แล้ว",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section3_pt.ipynb b/course/th/chapter6/section3_pt.ipynb
deleted file mode 100644
index 92c30f34..00000000
--- a/course/th/chapter6/section3_pt.ipynb
+++ /dev/null
@@ -1,515 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ความสามารถพิเศษของตัวตัดคำแบบเร็ว (fast tokenizers) (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"pt\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 19])\n",
-       "torch.Size([1, 19, 9])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()\n",
-    "predictions = outputs.logits.argmax(dim=-1)[0].tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Remove the B- or I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Grab all the tokens labeled with I-label\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # The score is the mean of all the scores of the tokens in that grouped entity\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "ความสามารถพิเศษของตัวตัดคำแบบเร็ว (fast tokenizers) (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section3_tf.ipynb b/course/th/chapter6/section3_tf.ipynb
deleted file mode 100644
index 49617324..00000000
--- a/course/th/chapter6/section3_tf.ipynb
+++ /dev/null
@@ -1,517 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ความสามารถพิเศษของตัวตัดคำแบบเร็ว (fast tokenizers) (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = TFAutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"tf\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 19)\n",
-       "(1, 19, 9)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "probabilities = tf.math.softmax(outputs.logits, axis=-1)[0]\n",
-    "probabilities = probabilities.numpy().tolist()\n",
-    "predictions = tf.math.argmax(outputs.logits, axis=-1)[0]\n",
-    "predictions = predictions.numpy().tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Remove the B- or I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Grab all the tokens labeled with I-label\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # The score is the mean of all the scores of the tokens in that grouped entity\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "ความสามารถพิเศษของตัวตัดคำแบบเร็ว (fast tokenizers) (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section3b_pt.ipynb b/course/th/chapter6/section3b_pt.ipynb
deleted file mode 100644
index 2e5c420f..00000000
--- a/course/th/chapter6/section3b_pt.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การใช้งานตัวตัดคำแบบเร็ว (Fast tokenizers) ใน QA pipeline (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97773,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch, and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97149,\n",
-       " 'start': 1892,\n",
-       " 'end': 1919,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "long_context = \"\"\"\n",
-    "🤗 Transformers: State of the Art NLP\n",
-    "\n",
-    "🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-    "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-    "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-    "\n",
-    "🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-    "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-    "can be modified to enable quick research experiments.\n",
-    "\n",
-    "Why should I use transformers?\n",
-    "\n",
-    "1. Easy-to-use state-of-the-art models:\n",
-    "  - High performance on NLU and NLG tasks.\n",
-    "  - Low barrier to entry for educators and practitioners.\n",
-    "  - Few user-facing abstractions with just three classes to learn.\n",
-    "  - A unified API for using all our pretrained models.\n",
-    "  - Lower compute costs, smaller carbon footprint:\n",
-    "\n",
-    "2. Researchers can share trained models instead of always retraining.\n",
-    "  - Practitioners can reduce compute time and production costs.\n",
-    "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-    "\n",
-    "3. Choose the right framework for every part of a model's lifetime:\n",
-    "  - Train state-of-the-art models in 3 lines of code.\n",
-    "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-    "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-    "\n",
-    "4. Easily customize a model or an example to your needs:\n",
-    "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-    "  - Model internals are exposed as consistently as possible.\n",
-    "  - Model files can be used independently of the library for quick experiments.\n",
-    "\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question_answerer(question=question, context=long_context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "inputs = tokenizer(question, context, return_tensors=\"pt\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 66]) torch.Size([1, 66])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "mask = torch.tensor(mask)[None]\n",
-    "\n",
-    "start_logits[mask] = -10000\n",
-    "end_logits[mask] = -10000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)[0]\n",
-    "end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = start_probabilities[:, None] * end_probabilities[None, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = torch.triu(scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97773"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_index = scores.argmax().item()\n",
-    "start_index = max_index // scores.shape[1]\n",
-    "end_index = max_index % scores.shape[1]\n",
-    "print(scores[start_index, end_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "start_char, _ = offsets[start_index]\n",
-    "_, end_char = offsets[end_index]\n",
-    "answer = context[start_char:end_char]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': 'Jax, PyTorch and TensorFlow',\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'score': 0.97773}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = {\n",
-    "    \"answer\": answer,\n",
-    "    \"start\": start_char,\n",
-    "    \"end\": end_char,\n",
-    "    \"score\": scores[start_index, end_index],\n",
-    "}\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "461"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context)\n",
-    "print(len(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "[CLS] Which deep learning libraries back [UNK] Transformers? [SEP] [UNK] Transformers : State of the Art NLP\n",
-       "\n",
-       "[UNK] Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-       "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-       "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-       "\n",
-       "[UNK] Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-       "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-       "can be modified to enable quick research experiments.\n",
-       "\n",
-       "Why should I use transformers?\n",
-       "\n",
-       "1. Easy-to-use state-of-the-art models:\n",
-       "  - High performance on NLU and NLG tasks.\n",
-       "  - Low barrier to entry for educators and practitioners.\n",
-       "  - Few user-facing abstractions with just three classes to learn.\n",
-       "  - A unified API for using all our pretrained models.\n",
-       "  - Lower compute costs, smaller carbon footprint:\n",
-       "\n",
-       "2. Researchers can share trained models instead of always retraining.\n",
-       "  - Practitioners can reduce compute time and production costs.\n",
-       "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-       "\n",
-       "3. Choose the right framework for every part of a model's lifetime:\n",
-       "  - Train state-of-the-art models in 3 lines of code.\n",
-       "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-       "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-       "\n",
-       "4. Easily customize a model or an example to your needs:\n",
-       "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-       "  - Model internal [SEP]\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n",
-    "print(tokenizer.decode(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] This sentence is not [SEP]'\n",
-       "'[CLS] is not too long [SEP]'\n",
-       "'[CLS] too long but we [SEP]'\n",
-       "'[CLS] but we are going [SEP]'\n",
-       "'[CLS] are going to split [SEP]'\n",
-       "'[CLS] to split it anyway [SEP]'\n",
-       "'[CLS] it anyway. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"This sentence is not too long but we are going to split it anyway.\"\n",
-    "inputs = tokenizer(\n",
-    "    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'attention_mask', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentences = [\n",
-    "    \"This sentence is not too long but we are going to split it anyway.\",\n",
-    "    \"This sentence is shorter but will still get split.\",\n",
-    "]\n",
-    "inputs = tokenizer(\n",
-    "    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    long_context,\n",
-    "    stride=128,\n",
-    "    max_length=384,\n",
-    "    padding=\"longest\",\n",
-    "    truncation=\"only_second\",\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 384])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_ = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "offsets = inputs.pop(\"offset_mapping\")\n",
-    "\n",
-    "inputs = inputs.convert_to_tensors(\"pt\")\n",
-    "print(inputs[\"input_ids\"].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 384]) torch.Size([2, 384])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "\n",
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "# Mask all the [PAD] tokens\n",
-    "mask = torch.logical_or(torch.tensor(mask)[None], (inputs[\"attention_mask\"] == 0))\n",
-    "\n",
-    "start_logits[mask] = -10000\n",
-    "end_logits[mask] = -10000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)\n",
-    "end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 18, 0.33867), (173, 184, 0.97149)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidates = []\n",
-    "for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n",
-    "    scores = start_probs[:, None] * end_probs[None, :]\n",
-    "    idx = torch.triu(scores).argmax().item()\n",
-    "\n",
-    "    start_idx = idx // scores.shape[1]\n",
-    "    end_idx = idx % scores.shape[1]\n",
-    "    score = scores[start_idx, end_idx].item()\n",
-    "    candidates.append((start_idx, end_idx, score))\n",
-    "\n",
-    "print(candidates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': '\\n🤗 Transformers: State of the Art NLP', 'start': 0, 'end': 37, 'score': 0.33867}\n",
-       "{'answer': 'Jax, PyTorch and TensorFlow', 'start': 1892, 'end': 1919, 'score': 0.97149}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for candidate, offset in zip(candidates, offsets):\n",
-    "    start_token, end_token, score = candidate\n",
-    "    start_char, _ = offset[start_token]\n",
-    "    _, end_char = offset[end_token]\n",
-    "    answer = long_context[start_char:end_char]\n",
-    "    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n",
-    "    print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การใช้งานตัวตัดคำแบบเร็ว (Fast tokenizers) ใน QA pipeline (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section3b_tf.ipynb b/course/th/chapter6/section3b_tf.ipynb
deleted file mode 100644
index 3d914fc9..00000000
--- a/course/th/chapter6/section3b_tf.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การใช้งานตัวตัดคำแบบเร็ว (Fast tokenizers) ใน QA pipeline (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97773,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch, and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97149,\n",
-       " 'start': 1892,\n",
-       " 'end': 1919,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "long_context = \"\"\"\n",
-    "🤗 Transformers: State of the Art NLP\n",
-    "\n",
-    "🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-    "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-    "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-    "\n",
-    "🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-    "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-    "can be modified to enable quick research experiments.\n",
-    "\n",
-    "Why should I use transformers?\n",
-    "\n",
-    "1. Easy-to-use state-of-the-art models:\n",
-    "  - High performance on NLU and NLG tasks.\n",
-    "  - Low barrier to entry for educators and practitioners.\n",
-    "  - Few user-facing abstractions with just three classes to learn.\n",
-    "  - A unified API for using all our pretrained models.\n",
-    "  - Lower compute costs, smaller carbon footprint:\n",
-    "\n",
-    "2. Researchers can share trained models instead of always retraining.\n",
-    "  - Practitioners can reduce compute time and production costs.\n",
-    "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-    "\n",
-    "3. Choose the right framework for every part of a model's lifetime:\n",
-    "  - Train state-of-the-art models in 3 lines of code.\n",
-    "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-    "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-    "\n",
-    "4. Easily customize a model or an example to your needs:\n",
-    "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-    "  - Model internals are exposed as consistently as possible.\n",
-    "  - Model files can be used independently of the library for quick experiments.\n",
-    "\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question_answerer(question=question, context=long_context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "inputs = tokenizer(question, context, return_tensors=\"tf\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 66) (1, 66)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "mask = tf.constant(mask)[None]\n",
-    "\n",
-    "start_logits = tf.where(mask, -10000, start_logits)\n",
-    "end_logits = tf.where(mask, -10000, end_logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = tf.math.softmax(start_logits, axis=-1)[0].numpy()\n",
-    "end_probabilities = tf.math.softmax(end_logits, axis=-1)[0].numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = start_probabilities[:, None] * end_probabilities[None, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = np.triu(scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97773"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_index = scores.argmax().item()\n",
-    "start_index = max_index // scores.shape[1]\n",
-    "end_index = max_index % scores.shape[1]\n",
-    "print(scores[start_index, end_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "start_char, _ = offsets[start_index]\n",
-    "_, end_char = offsets[end_index]\n",
-    "answer = context[start_char:end_char]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': 'Jax, PyTorch and TensorFlow',\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'score': 0.97773}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = {\n",
-    "    \"answer\": answer,\n",
-    "    \"start\": start_char,\n",
-    "    \"end\": end_char,\n",
-    "    \"score\": scores[start_index, end_index],\n",
-    "}\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "461"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context)\n",
-    "print(len(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "[CLS] Which deep learning libraries back [UNK] Transformers? [SEP] [UNK] Transformers : State of the Art NLP\n",
-       "\n",
-       "[UNK] Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-       "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-       "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-       "\n",
-       "[UNK] Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-       "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-       "can be modified to enable quick research experiments.\n",
-       "\n",
-       "Why should I use transformers?\n",
-       "\n",
-       "1. Easy-to-use state-of-the-art models:\n",
-       "  - High performance on NLU and NLG tasks.\n",
-       "  - Low barrier to entry for educators and practitioners.\n",
-       "  - Few user-facing abstractions with just three classes to learn.\n",
-       "  - A unified API for using all our pretrained models.\n",
-       "  - Lower compute costs, smaller carbon footprint:\n",
-       "\n",
-       "2. Researchers can share trained models instead of always retraining.\n",
-       "  - Practitioners can reduce compute time and production costs.\n",
-       "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-       "\n",
-       "3. Choose the right framework for every part of a model's lifetime:\n",
-       "  - Train state-of-the-art models in 3 lines of code.\n",
-       "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-       "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-       "\n",
-       "4. Easily customize a model or an example to your needs:\n",
-       "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-       "  - Model internal [SEP]\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n",
-    "print(tokenizer.decode(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] This sentence is not [SEP]'\n",
-       "'[CLS] is not too long [SEP]'\n",
-       "'[CLS] too long but we [SEP]'\n",
-       "'[CLS] but we are going [SEP]'\n",
-       "'[CLS] are going to split [SEP]'\n",
-       "'[CLS] to split it anyway [SEP]'\n",
-       "'[CLS] it anyway. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"This sentence is not too long but we are going to split it anyway.\"\n",
-    "inputs = tokenizer(\n",
-    "    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'attention_mask', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentences = [\n",
-    "    \"This sentence is not too long but we are going to split it anyway.\",\n",
-    "    \"This sentence is shorter but will still get split.\",\n",
-    "]\n",
-    "inputs = tokenizer(\n",
-    "    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    long_context,\n",
-    "    stride=128,\n",
-    "    max_length=384,\n",
-    "    padding=\"longest\",\n",
-    "    truncation=\"only_second\",\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 384)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_ = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "offsets = inputs.pop(\"offset_mapping\")\n",
-    "\n",
-    "inputs = inputs.convert_to_tensors(\"tf\")\n",
-    "print(inputs[\"input_ids\"].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 384) (2, 384)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "\n",
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "# Mask all the [PAD] tokens\n",
-    "mask = tf.math.logical_or(tf.constant(mask)[None], inputs[\"attention_mask\"] == 0)\n",
-    "\n",
-    "start_logits = tf.where(mask, -10000, start_logits)\n",
-    "end_logits = tf.where(mask, -10000, end_logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = tf.math.softmax(start_logits, axis=-1).numpy()\n",
-    "end_probabilities = tf.math.softmax(end_logits, axis=-1).numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 18, 0.33867), (173, 184, 0.97149)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidates = []\n",
-    "for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n",
-    "    scores = start_probs[:, None] * end_probs[None, :]\n",
-    "    idx = np.triu(scores).argmax().item()\n",
-    "\n",
-    "    start_idx = idx // scores.shape[1]\n",
-    "    end_idx = idx % scores.shape[1]\n",
-    "    score = scores[start_idx, end_idx].item()\n",
-    "    candidates.append((start_idx, end_idx, score))\n",
-    "\n",
-    "print(candidates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': '\\n🤗 Transformers: State of the Art NLP', 'start': 0, 'end': 37, 'score': 0.33867}\n",
-       "{'answer': 'Jax, PyTorch and TensorFlow', 'start': 1892, 'end': 1919, 'score': 0.97149}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for candidate, offset in zip(candidates, offsets):\n",
-    "    start_token, end_token, score = candidate\n",
-    "    start_char, _ = offset[start_token]\n",
-    "    _, end_char = offset[end_token]\n",
-    "    answer = long_context[start_char:end_char]\n",
-    "    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n",
-    "    print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การใช้งานตัวตัดคำแบบเร็ว (Fast tokenizers) ใน QA pipeline (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section4.ipynb b/course/th/chapter6/section4.ipynb
deleted file mode 100644
index de0090ea..00000000
--- a/course/th/chapter6/section4.ipynb
+++ /dev/null
@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Normalization และ pre-tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'tokenizers.Tokenizer'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
-    "print(type(tokenizer.backend_tokenizer))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello how are u?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.backend_tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Hello', (0, 5)), (',', (5, 6)), ('how', (7, 10)), ('are', (11, 14)), ('you', (16, 19)), ('?', (19, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Hello', (0, 5)), (',', (5, 6)), ('Ġhow', (6, 10)), ('Ġare', (10, 14)), ('Ġ', (14, 15)), ('Ġyou', (15, 19)),\n",
-       " ('?', (19, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('▁Hello,', (0, 6)), ('▁how', (7, 10)), ('▁are', (11, 14)), ('▁you?', (16, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n",
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Normalization และ pre-tokenization",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section5.ipynb b/course/th/chapter6/section5.ipynb
deleted file mode 100644
index 1f38ff83..00000000
--- a/course/th/chapter6/section5.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Byte-Pair Encoding tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(int, {'This': 3, 'Ġis': 2, 'Ġthe': 1, 'ĠHugging': 1, 'ĠFace': 1, 'ĠCourse': 1, '.': 4, 'Ġchapter': 1,\n",
-       "    'Ġabout': 1, 'Ġtokenization': 1, 'Ġsection': 1, 'Ġshows': 1, 'Ġseveral': 1, 'Ġtokenizer': 1, 'Ġalgorithms': 1,\n",
-       "    'Hopefully': 1, ',': 1, 'Ġyou': 1, 'Ġwill': 1, 'Ġbe': 1, 'Ġable': 1, 'Ġto': 1, 'Ġunderstand': 1, 'Ġhow': 1,\n",
-       "    'Ġthey': 1, 'Ġare': 1, 'Ġtrained': 1, 'Ġand': 1, 'Ġgenerate': 1, 'Ġtokens': 1})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "print(word_freqs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's',\n",
-       "  't', 'u', 'v', 'w', 'y', 'z', 'Ġ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alphabet = []\n",
-    "\n",
-    "for word in word_freqs.keys():\n",
-    "    for letter in word:\n",
-    "        if letter not in alphabet:\n",
-    "            alphabet.append(letter)\n",
-    "alphabet.sort()\n",
-    "\n",
-    "print(alphabet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab = [\"<|endoftext|>\"] + alphabet.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "splits = {word: [c for c in word] for word in word_freqs.keys()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pair_freqs(splits):\n",
-    "    pair_freqs = defaultdict(int)\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "        for i in range(len(split) - 1):\n",
-    "            pair = (split[i], split[i + 1])\n",
-    "            pair_freqs[pair] += freq\n",
-    "    return pair_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('T', 'h'): 3\n",
-       "('h', 'i'): 3\n",
-       "('i', 's'): 5\n",
-       "('Ġ', 'i'): 2\n",
-       "('Ġ', 't'): 7\n",
-       "('t', 'h'): 3"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pair_freqs = compute_pair_freqs(splits)\n",
-    "\n",
-    "for i, key in enumerate(pair_freqs.keys()):\n",
-    "    print(f\"{key}: {pair_freqs[key]}\")\n",
-    "    if i >= 5:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Ġ', 't') 7"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_pair = \"\"\n",
-    "max_freq = None\n",
-    "\n",
-    "for pair, freq in pair_freqs.items():\n",
-    "    if max_freq is None or max_freq < freq:\n",
-    "        best_pair = pair\n",
-    "        max_freq = freq\n",
-    "\n",
-    "print(best_pair, max_freq)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "merges = {(\"Ġ\", \"t\"): \"Ġt\"}\n",
-    "vocab.append(\"Ġt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_pair(a, b, splits):\n",
-    "    for word in word_freqs:\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "\n",
-    "        i = 0\n",
-    "        while i < len(split) - 1:\n",
-    "            if split[i] == a and split[i + 1] == b:\n",
-    "                split = split[:i] + [a + b] + split[i + 2 :]\n",
-    "            else:\n",
-    "                i += 1\n",
-    "        splits[word] = split\n",
-    "    return splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Ġt', 'r', 'a', 'i', 'n', 'e', 'd']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "splits = merge_pair(\"Ġ\", \"t\", splits)\n",
-    "print(splits[\"Ġtrained\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab_size = 50\n",
-    "\n",
-    "while len(vocab) < vocab_size:\n",
-    "    pair_freqs = compute_pair_freqs(splits)\n",
-    "    best_pair = \"\"\n",
-    "    max_freq = None\n",
-    "    for pair, freq in pair_freqs.items():\n",
-    "        if max_freq is None or max_freq < freq:\n",
-    "            best_pair = pair\n",
-    "            max_freq = freq\n",
-    "    splits = merge_pair(*best_pair, splits)\n",
-    "    merges[best_pair] = best_pair[0] + best_pair[1]\n",
-    "    vocab.append(best_pair[0] + best_pair[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{('Ġ', 't'): 'Ġt', ('i', 's'): 'is', ('e', 'r'): 'er', ('Ġ', 'a'): 'Ġa', ('Ġt', 'o'): 'Ġto', ('e', 'n'): 'en',\n",
-       " ('T', 'h'): 'Th', ('Th', 'is'): 'This', ('o', 'u'): 'ou', ('s', 'e'): 'se', ('Ġto', 'k'): 'Ġtok',\n",
-       " ('Ġtok', 'en'): 'Ġtoken', ('n', 'd'): 'nd', ('Ġ', 'is'): 'Ġis', ('Ġt', 'h'): 'Ġth', ('Ġth', 'e'): 'Ġthe',\n",
-       " ('i', 'n'): 'in', ('Ġa', 'b'): 'Ġab', ('Ġtoken', 'i'): 'Ġtokeni'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(merges)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['<|endoftext|>', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o',\n",
-       " 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'Ġ', 'Ġt', 'is', 'er', 'Ġa', 'Ġto', 'en', 'Th', 'This', 'ou', 'se',\n",
-       " 'Ġtok', 'Ġtoken', 'nd', 'Ġis', 'Ġth', 'Ġthe', 'in', 'Ġab', 'Ġtokeni']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize(text):\n",
-    "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
-    "    splits = [[l for l in word] for word in pre_tokenized_text]\n",
-    "    for pair, merge in merges.items():\n",
-    "        for idx, split in enumerate(splits):\n",
-    "            i = 0\n",
-    "            while i < len(split) - 1:\n",
-    "                if split[i] == pair[0] and split[i + 1] == pair[1]:\n",
-    "                    split = split[:i] + [merge] + split[i + 2 :]\n",
-    "                else:\n",
-    "                    i += 1\n",
-    "            splits[idx] = split\n",
-    "\n",
-    "    return sum(splits, [])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['This', 'Ġis', 'Ġ', 'n', 'o', 't', 'Ġa', 'Ġtoken', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenize(\"This is not a token.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Byte-Pair Encoding tokenization",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section6.ipynb b/course/th/chapter6/section6.ipynb
deleted file mode 100644
index 8e098757..00000000
--- a/course/th/chapter6/section6.ipynb
+++ /dev/null
@@ -1,406 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# WordPiece tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(\n",
-       "    int, {'This': 3, 'is': 2, 'the': 1, 'Hugging': 1, 'Face': 1, 'Course': 1, '.': 4, 'chapter': 1, 'about': 1,\n",
-       "    'tokenization': 1, 'section': 1, 'shows': 1, 'several': 1, 'tokenizer': 1, 'algorithms': 1, 'Hopefully': 1,\n",
-       "    ',': 1, 'you': 1, 'will': 1, 'be': 1, 'able': 1, 'to': 1, 'understand': 1, 'how': 1, 'they': 1, 'are': 1,\n",
-       "    'trained': 1, 'and': 1, 'generate': 1, 'tokens': 1})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "word_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['##a', '##b', '##c', '##d', '##e', '##f', '##g', '##h', '##i', '##k', '##l', '##m', '##n', '##o', '##p', '##r', '##s',\n",
-       " '##t', '##u', '##v', '##w', '##y', '##z', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'g', 'h', 'i', 's', 't', 'u',\n",
-       " 'w', 'y']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alphabet = []\n",
-    "for word in word_freqs.keys():\n",
-    "    if word[0] not in alphabet:\n",
-    "        alphabet.append(word[0])\n",
-    "    for letter in word[1:]:\n",
-    "        if f\"##{letter}\" not in alphabet:\n",
-    "            alphabet.append(f\"##{letter}\")\n",
-    "\n",
-    "alphabet.sort()\n",
-    "alphabet\n",
-    "\n",
-    "print(alphabet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab = [\"[PAD]\", \"[UNK]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"] + alphabet.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "splits = {\n",
-    "    word: [c if i == 0 else f\"##{c}\" for i, c in enumerate(word)]\n",
-    "    for word in word_freqs.keys()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pair_scores(splits):\n",
-    "    letter_freqs = defaultdict(int)\n",
-    "    pair_freqs = defaultdict(int)\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            letter_freqs[split[0]] += freq\n",
-    "            continue\n",
-    "        for i in range(len(split) - 1):\n",
-    "            pair = (split[i], split[i + 1])\n",
-    "            letter_freqs[split[i]] += freq\n",
-    "            pair_freqs[pair] += freq\n",
-    "        letter_freqs[split[-1]] += freq\n",
-    "\n",
-    "    scores = {\n",
-    "        pair: freq / (letter_freqs[pair[0]] * letter_freqs[pair[1]])\n",
-    "        for pair, freq in pair_freqs.items()\n",
-    "    }\n",
-    "    return scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('T', '##h'): 0.125\n",
-       "('##h', '##i'): 0.03409090909090909\n",
-       "('##i', '##s'): 0.02727272727272727\n",
-       "('i', '##s'): 0.1\n",
-       "('t', '##h'): 0.03571428571428571\n",
-       "('##h', '##e'): 0.011904761904761904"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pair_scores = compute_pair_scores(splits)\n",
-    "for i, key in enumerate(pair_scores.keys()):\n",
-    "    print(f\"{key}: {pair_scores[key]}\")\n",
-    "    if i >= 5:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('a', '##b') 0.2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_pair = \"\"\n",
-    "max_score = None\n",
-    "for pair, score in pair_scores.items():\n",
-    "    if max_score is None or max_score < score:\n",
-    "        best_pair = pair\n",
-    "        max_score = score\n",
-    "\n",
-    "print(best_pair, max_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab.append(\"ab\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_pair(a, b, splits):\n",
-    "    for word in word_freqs:\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "        i = 0\n",
-    "        while i < len(split) - 1:\n",
-    "            if split[i] == a and split[i + 1] == b:\n",
-    "                merge = a + b[2:] if b.startswith(\"##\") else a + b\n",
-    "                split = split[:i] + [merge] + split[i + 2 :]\n",
-    "            else:\n",
-    "                i += 1\n",
-    "        splits[word] = split\n",
-    "    return splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ab', '##o', '##u', '##t']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "splits = merge_pair(\"a\", \"##b\", splits)\n",
-    "splits[\"about\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab_size = 70\n",
-    "while len(vocab) < vocab_size:\n",
-    "    scores = compute_pair_scores(splits)\n",
-    "    best_pair, max_score = \"\", None\n",
-    "    for pair, score in scores.items():\n",
-    "        if max_score is None or max_score < score:\n",
-    "            best_pair = pair\n",
-    "            max_score = score\n",
-    "    splits = merge_pair(*best_pair, splits)\n",
-    "    new_token = (\n",
-    "        best_pair[0] + best_pair[1][2:]\n",
-    "        if best_pair[1].startswith(\"##\")\n",
-    "        else best_pair[0] + best_pair[1]\n",
-    "    )\n",
-    "    vocab.append(new_token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]', '##a', '##b', '##c', '##d', '##e', '##f', '##g', '##h', '##i', '##k',\n",
-       " '##l', '##m', '##n', '##o', '##p', '##r', '##s', '##t', '##u', '##v', '##w', '##y', '##z', ',', '.', 'C', 'F', 'H',\n",
-       " 'T', 'a', 'b', 'c', 'g', 'h', 'i', 's', 't', 'u', 'w', 'y', '##fu', 'Fa', 'Fac', '##ct', '##ful', '##full', '##fully',\n",
-       " 'Th', 'ch', '##hm', 'cha', 'chap', 'chapt', '##thm', 'Hu', 'Hug', 'Hugg', 'sh', 'th', 'is', '##thms', '##za', '##zat',\n",
-       " '##ut']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_word(word):\n",
-    "    tokens = []\n",
-    "    while len(word) > 0:\n",
-    "        i = len(word)\n",
-    "        while i > 0 and word[:i] not in vocab:\n",
-    "            i -= 1\n",
-    "        if i == 0:\n",
-    "            return [\"[UNK]\"]\n",
-    "        tokens.append(word[:i])\n",
-    "        word = word[i:]\n",
-    "        if len(word) > 0:\n",
-    "            word = f\"##{word}\"\n",
-    "    return tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Hugg', '##i', '##n', '##g']\n",
-       "['[UNK]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(encode_word(\"Hugging\"))\n",
-    "print(encode_word(\"HOgging\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize(text):\n",
-    "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
-    "    encoded_words = [encode_word(word) for word in pre_tokenized_text]\n",
-    "    return sum(encoded_words, [])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Th', '##i', '##s', 'is', 'th', '##e', 'Hugg', '##i', '##n', '##g', 'Fac', '##e', 'c', '##o', '##u', '##r', '##s',\n",
-       " '##e', '[UNK]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenize(\"This is the Hugging Face course!\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "WordPiece tokenization",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section7.ipynb b/course/th/chapter6/section7.ipynb
deleted file mode 100644
index 40c823e3..00000000
--- a/course/th/chapter6/section7.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Unigram tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"xlnet-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "word_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('▁t', 7), ('is', 5), ('er', 5), ('▁a', 5), ('▁to', 4), ('to', 4), ('en', 4), ('▁T', 3), ('▁Th', 3), ('▁Thi', 3)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "char_freqs = defaultdict(int)\n",
-    "subwords_freqs = defaultdict(int)\n",
-    "for word, freq in word_freqs.items():\n",
-    "    for i in range(len(word)):\n",
-    "        char_freqs[word[i]] += freq\n",
-    "        # Loop through the subwords of length at least 2\n",
-    "        for j in range(i + 2, len(word) + 1):\n",
-    "            subwords_freqs[word[i:j]] += freq\n",
-    "\n",
-    "# Sort subwords by frequency\n",
-    "sorted_subwords = sorted(subwords_freqs.items(), key=lambda x: x[1], reverse=True)\n",
-    "sorted_subwords[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "token_freqs = list(char_freqs.items()) + sorted_subwords[: 300 - len(char_freqs)]\n",
-    "token_freqs = {token: freq for token, freq in token_freqs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from math import log\n",
-    "\n",
-    "total_sum = sum([freq for token, freq in token_freqs.items()])\n",
-    "model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_word(word, model):\n",
-    "    best_segmentations = [{\"start\": 0, \"score\": 1}] + [\n",
-    "        {\"start\": None, \"score\": None} for _ in range(len(word))\n",
-    "    ]\n",
-    "    for start_idx in range(len(word)):\n",
-    "        # This should be properly filled by the previous steps of the loop\n",
-    "        best_score_at_start = best_segmentations[start_idx][\"score\"]\n",
-    "        for end_idx in range(start_idx + 1, len(word) + 1):\n",
-    "            token = word[start_idx:end_idx]\n",
-    "            if token in model and best_score_at_start is not None:\n",
-    "                score = model[token] + best_score_at_start\n",
-    "                # If we have found a better segmentation ending at end_idx, we update\n",
-    "                if (\n",
-    "                    best_segmentations[end_idx][\"score\"] is None\n",
-    "                    or best_segmentations[end_idx][\"score\"] > score\n",
-    "                ):\n",
-    "                    best_segmentations[end_idx] = {\"start\": start_idx, \"score\": score}\n",
-    "\n",
-    "    segmentation = best_segmentations[-1]\n",
-    "    if segmentation[\"score\"] is None:\n",
-    "        # We did not find a tokenization of the word -> unknown\n",
-    "        return [\"<unk>\"], None\n",
-    "\n",
-    "    score = segmentation[\"score\"]\n",
-    "    start = segmentation[\"start\"]\n",
-    "    end = len(word)\n",
-    "    tokens = []\n",
-    "    while start != 0:\n",
-    "        tokens.insert(0, word[start:end])\n",
-    "        next_start = best_segmentations[start][\"start\"]\n",
-    "        end = start\n",
-    "        start = next_start\n",
-    "    tokens.insert(0, word[start:end])\n",
-    "    return tokens, score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(['H', 'o', 'p', 'e', 'f', 'u', 'll', 'y'], 41.5157494601402)\n",
-       "(['This'], 6.288267030694535)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(encode_word(\"Hopefully\", model))\n",
-    "print(encode_word(\"This\", model))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_loss(model):\n",
-    "    loss = 0\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        _, word_loss = encode_word(word, model)\n",
-    "        loss += freq * word_loss\n",
-    "    return loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "413.10377642940875"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_loss(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "\n",
-    "\n",
-    "def compute_scores(model):\n",
-    "    scores = {}\n",
-    "    model_loss = compute_loss(model)\n",
-    "    for token, score in model.items():\n",
-    "        # We always keep tokens of length 1\n",
-    "        if len(token) == 1:\n",
-    "            continue\n",
-    "        model_without_token = copy.deepcopy(model)\n",
-    "        _ = model_without_token.pop(token)\n",
-    "        scores[token] = compute_loss(model_without_token) - model_loss\n",
-    "    return scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.376412403623874\n",
-       "0.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = compute_scores(model)\n",
-    "print(scores[\"ll\"])\n",
-    "print(scores[\"his\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "percent_to_remove = 0.1\n",
-    "while len(model) > 100:\n",
-    "    scores = compute_scores(model)\n",
-    "    sorted_scores = sorted(scores.items(), key=lambda x: x[1])\n",
-    "    # Remove percent_to_remove tokens with the lowest scores.\n",
-    "    for i in range(int(len(model) * percent_to_remove)):\n",
-    "        _ = token_freqs.pop(sorted_scores[i][0])\n",
-    "\n",
-    "    total_sum = sum([freq for token, freq in token_freqs.items()])\n",
-    "    model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁This', '▁is', '▁the', '▁Hugging', '▁Face', '▁', 'c', 'ou', 'r', 's', 'e', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(text, model):\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in words_with_offsets]\n",
-    "    encoded_words = [encode_word(word, model)[0] for word in pre_tokenized_text]\n",
-    "    return sum(encoded_words, [])\n",
-    "\n",
-    "\n",
-    "tokenize(\"This is the Hugging Face course.\", model)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Unigram tokenization",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/th/chapter6/section8.ipynb b/course/th/chapter6/section8.ipynb
deleted file mode 100644
index bbbef231..00000000
--- a/course/th/chapter6/section8.ipynb
+++ /dev/null
@@ -1,779 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# การสร้าง tokenizer ทีละขั้นตอน"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"wikitext\", name=\"wikitext-2-raw-v1\", split=\"train\")\n",
-    "\n",
-    "\n",
-    "def get_training_corpus():\n",
-    "    for i in range(0, len(dataset), 1000):\n",
-    "        yield dataset[i : i + 1000][\"text\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"wikitext-2.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "    for i in range(len(dataset)):\n",
-    "        f.write(dataset[i][\"text\"] + \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tokenizers import (\n",
-    "    decoders,\n",
-    "    models,\n",
-    "    normalizers,\n",
-    "    pre_tokenizers,\n",
-    "    processors,\n",
-    "    trainers,\n",
-    "    Tokenizer,\n",
-    ")\n",
-    "\n",
-    "tokenizer = Tokenizer(models.WordPiece(unk_token=\"[UNK]\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.normalizer = normalizers.Sequence(\n",
-    "    [normalizers.NFD(), normalizers.Lowercase(), normalizers.StripAccents()]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "hello how are u?"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'\", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),\n",
-       " ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(\"Let's\", (0, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre-tokenizer.', (14, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pre_tokenizer = pre_tokenizers.WhitespaceSplit()\n",
-    "pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'\", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),\n",
-       " ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pre_tokenizer = pre_tokenizers.Sequence(\n",
-    "    [pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Punctuation()]\n",
-    ")\n",
-    "pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "special_tokens = [\"[UNK]\", \"[PAD]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"]\n",
-    "trainer = trainers.WordPieceTrainer(vocab_size=25000, special_tokens=special_tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.WordPiece(unk_token=\"[UNK]\")\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cls_token_id = tokenizer.token_to_id(\"[CLS]\")\n",
-    "sep_token_id = tokenizer.token_to_id(\"[SEP]\")\n",
-    "print(cls_token_id, sep_token_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.TemplateProcessing(\n",
-    "    single=f\"[CLS]:0 $A:0 [SEP]:0\",\n",
-    "    pair=f\"[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1\",\n",
-    "    special_tokens=[(\"[CLS]\", cls_token_id), (\"[SEP]\", sep_token_id)],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '...', '[SEP]', 'on', 'a', 'pair', 'of', 'sentences', '.', '[SEP]']\n",
-       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer...\", \"on a pair of sentences.\")\n",
-    "print(encoding.tokens)\n",
-    "print(encoding.type_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.WordPiece(prefix=\"##\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"let's test this tokenizer... on a pair of sentences.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(encoding.ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save(\"tokenizer.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_tokenizer = Tokenizer.from_file(\"tokenizer.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    # tokenizer_file=\"tokenizer.json\", # You can load from the tokenizer file, alternatively\n",
-    "    unk_token=\"[UNK]\",\n",
-    "    pad_token=\"[PAD]\",\n",
-    "    cls_token=\"[CLS]\",\n",
-    "    sep_token=\"[SEP]\",\n",
-    "    mask_token=\"[MASK]\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = BertTokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = Tokenizer(models.BPE())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'s\", (3, 5)), ('Ġtest', (5, 10)), ('Ġpre', (10, 14)), ('-', (14, 15)),\n",
-       " ('tokenization', (15, 27)), ('!', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test pre-tokenization!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = trainers.BpeTrainer(vocab_size=25000, special_tokens=[\"<|endoftext|>\"])\n",
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.BPE()\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['L', 'et', \"'\", 's', 'Ġtest', 'Ġthis', 'Ġto', 'ken', 'izer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' test'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"Let's test this tokenizer.\"\n",
-    "encoding = tokenizer.encode(sentence)\n",
-    "start, end = encoding.offsets[4]\n",
-    "sentence[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.ByteLevel()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Let's test this tokenizer.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(encoding.ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    bos_token=\"<|endoftext|>\",\n",
-    "    eos_token=\"<|endoftext|>\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT2TokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = Tokenizer(models.Unigram())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tokenizers import Regex\n",
-    "\n",
-    "tokenizer.normalizer = normalizers.Sequence(\n",
-    "    [\n",
-    "        normalizers.Replace(\"``\", '\"'),\n",
-    "        normalizers.Replace(\"''\", '\"'),\n",
-    "        normalizers.NFKD(),\n",
-    "        normalizers.StripAccents(),\n",
-    "        normalizers.Replace(Regex(\" {2,}\"), \" \"),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(\"▁Let's\", (0, 5)), ('▁test', (5, 10)), ('▁the', (10, 14)), ('▁pre-tokenizer!', (14, 29))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test the pre-tokenizer!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "special_tokens = [\"<cls>\", \"<sep>\", \"<unk>\", \"<pad>\", \"<mask>\", \"<s>\", \"</s>\"]\n",
-    "trainer = trainers.UnigramTrainer(\n",
-    "    vocab_size=25000, special_tokens=special_tokens, unk_token=\"<unk>\"\n",
-    ")\n",
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.Unigram()\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Let', \"'\", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0 1"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cls_token_id = tokenizer.token_to_id(\"<cls>\")\n",
-    "sep_token_id = tokenizer.token_to_id(\"<sep>\")\n",
-    "print(cls_token_id, sep_token_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.TemplateProcessing(\n",
-    "    single=\"$A:0 <sep>:0 <cls>:2\",\n",
-    "    pair=\"$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2\",\n",
-    "    special_tokens=[(\"<sep>\", sep_token_id), (\"<cls>\", cls_token_id)],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Let', \"'\", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.', '.', '.', '<sep>', '▁', 'on', '▁', 'a', '▁pair',\n",
-       "  '▁of', '▁sentence', 's', '!', '<sep>', '<cls>']\n",
-       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer...\", \"on a pair of sentences!\")\n",
-    "print(encoding.tokens)\n",
-    "print(encoding.type_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.Metaspace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    bos_token=\"<s>\",\n",
-    "    eos_token=\"</s>\",\n",
-    "    unk_token=\"<unk>\",\n",
-    "    pad_token=\"<pad>\",\n",
-    "    cls_token=\"<cls>\",\n",
-    "    sep_token=\"<sep>\",\n",
-    "    mask_token=\"<mask>\",\n",
-    "    padding_side=\"left\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import XLNetTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = XLNetTokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "การสร้าง tokenizer ทีละขั้นตอน",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter1/section10.ipynb b/course/vi/chapter1/section10.ipynb
deleted file mode 100644
index fa994604..00000000
--- a/course/vi/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Đố vui cuối chương"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Đố vui cuối chương",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter1/section3.ipynb b/course/vi/chapter1/section3.ipynb
deleted file mode 100644
index eb07af1d..00000000
--- a/course/vi/chapter1/section3.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformers có thể làm những gì?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformers có thể làm những gì?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter1/section8.ipynb b/course/vi/chapter1/section8.ipynb
deleted file mode 100644
index a53f2957..00000000
--- a/course/vi/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Thiên kiến và hạn chế"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Thiên kiến và hạn chế",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section2_pt.ipynb b/course/vi/chapter2/section2_pt.ipynb
deleted file mode 100644
index 706d19f8..00000000
--- a/course/vi/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Đằng sau pipeline (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Đằng sau pipeline (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section2_tf.ipynb b/course/vi/chapter2/section2_tf.ipynb
deleted file mode 100644
index 030c227c..00000000
--- a/course/vi/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Đằng sau pipeline (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Đằng sau pipeline (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section3_pt.ipynb b/course/vi/chapter2/section3_pt.ipynb
deleted file mode 100644
index b15fd266..00000000
--- a/course/vi/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Các mô hình (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Các mô hình (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section3_tf.ipynb b/course/vi/chapter2/section3_tf.ipynb
deleted file mode 100644
index 9c6e9c24..00000000
--- a/course/vi/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Các mô hình (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Các mô hình (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section4_pt.ipynb b/course/vi/chapter2/section4_pt.ipynb
deleted file mode 100644
index fadf2b34..00000000
--- a/course/vi/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section4_tf.ipynb b/course/vi/chapter2/section4_tf.ipynb
deleted file mode 100644
index 15343d71..00000000
--- a/course/vi/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section5_pt.ipynb b/course/vi/chapter2/section5_pt.ipynb
deleted file mode 100644
index c4bc67bb..00000000
--- a/course/vi/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xử lý đa chuỗi (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Xử lý đa chuỗi (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section5_tf.ipynb b/course/vi/chapter2/section5_tf.ipynb
deleted file mode 100644
index 41f6c86a..00000000
--- a/course/vi/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xử lý đa chuỗi (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Xử lý đa chuỗi (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section6_pt.ipynb b/course/vi/chapter2/section6_pt.ipynb
deleted file mode 100644
index d5f3768d..00000000
--- a/course/vi/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Kết hợp lại (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sẽ đệm thêm vào chuỗi sao cho độ dài bằng độ dài tối đa của chuỗi\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Sẽ đệm thêm vào chuỗi sao cho độ dài bằng độ dài tối đa của mô hình\n",
-    "# (512 cho BERT hoặc DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Sẽ đệm thêm vào chuỗi sao cho độ dài bằng độ dài tối đa được chỉ định\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Sẽ cắt bớt chuỗi cho bằng độ dài tối đa của mô hình\n",
-    "# (512 cho BERT hoặc DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Sẽ cắt bớt chuỗi có độ dài dài hơn độ dài tối đa được chỉ định\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Trả về tensor PyTorch\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Trả về tensor TensorFlow\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Trả về mảng NumPy\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Kết hợp lại (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section6_tf.ipynb b/course/vi/chapter2/section6_tf.ipynb
deleted file mode 100644
index 44cc6029..00000000
--- a/course/vi/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Kết hợp lại (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sẽ đệm thêm vào chuỗi sao cho độ dài bằng độ dài tối đa của chuỗi\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Sẽ đệm thêm vào chuỗi sao cho độ dài bằng độ dài tối đa của mô hình\n",
-    "# (512 cho BERT hoặc DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Sẽ đệm thêm vào chuỗi sao cho độ dài bằng độ dài tối đa được chỉ định\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Sẽ cắt bớt chuỗi cho bằng độ dài tối đa của mô hình\n",
-    "# (512 cho BERT hoặc DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Sẽ cắt bớt chuỗi có độ dài dài hơn độ dài tối đa được chỉ định\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Trả về tensor PyTorch\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Trả về tensor TensorFlow\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Trả về mảng NumPy\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Kết hợp lại (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section8_pt.ipynb b/course/vi/chapter2/section8_pt.ipynb
deleted file mode 100644
index e682529a..00000000
--- a/course/vi/chapter2/section8_pt.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Đố vui cuối chương (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = AutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Đố vui cuối chương (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter2/section8_tf.ipynb b/course/vi/chapter2/section8_tf.ipynb
deleted file mode 100644
index 7363fd4a..00000000
--- a/course/vi/chapter2/section8_tf.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Đố vui cuối chương (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = TFAutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Đố vui cuối chương (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter3/section2_pt.ipynb b/course/vi/chapter3/section2_pt.ipynb
deleted file mode 100644
index c765d04a..00000000
--- a/course/vi/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xử lý dữ liệu (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Tương tự như ví dụ trước\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Đây là phần mới\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Xử lý dữ liệu (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter3/section2_tf.ipynb b/course/vi/chapter3/section2_tf.ipynb
deleted file mode 100644
index 655a48db..00000000
--- a/course/vi/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xử lý dữ liệu (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Tương tự như ví dụ trước\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# Đây là phần mới\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Xử lý dữ liệu (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter3/section3.ipynb b/course/vi/chapter3/section3.ipynb
deleted file mode 100644
index f31eed86..00000000
--- a/course/vi/chapter3/section3.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tinh chỉnh một mô hình với Trainer API hoặc Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tinh chỉnh một mô hình với Trainer API hoặc Keras",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter3/section3_tf.ipynb b/course/vi/chapter3/section3_tf.ipynb
deleted file mode 100644
index b43cbf9e..00000000
--- a/course/vi/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tinh chỉnh một mô hình với Trainer API hoặc Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# Số bước huấn luyện là số lượng mẫu trong tập dữ liệu, chia cho kích thước lô sau đó nhân\n",
-    "# với tổng số epoch. Lưu ý rằng tf_train_dataset ở đây là tf.data.Dataset theo lô,\n",
-    "# không phải là Hugging Face Dataset, vì vậy len() của nó đã là num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tinh chỉnh một mô hình với Trainer API hoặc Keras",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter3/section4.ipynb b/course/vi/chapter3/section4.ipynb
deleted file mode 100644
index 8c815860..00000000
--- a/course/vi/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Bản huấn luyện hoàn chỉnh"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Bản huấn luyện hoàn chỉnh",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter4/section2_pt.ipynb b/course/vi/chapter4/section2_pt.ipynb
deleted file mode 100644
index 7b230344..00000000
--- a/course/vi/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sử dụng các mô hình huấn luyện trước (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'},\n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'},\n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'},\n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'},\n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Sử dụng các mô hình huấn luyện trước (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter4/section2_tf.ipynb b/course/vi/chapter4/section2_tf.ipynb
deleted file mode 100644
index 6ead7496..00000000
--- a/course/vi/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sử dụng các mô hình huấn luyện trước (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'},\n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'},\n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'},\n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'},\n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Sử dụng các mô hình huấn luyện trước (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter4/section3_pt.ipynb b/course/vi/chapter4/section3_pt.ipynb
deleted file mode 100644
index 1c6ae298..00000000
--- a/course/vi/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chia sẻ các mô hình huấn luyện trước (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Quản lý người dùng\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Tạo và quản lý kho dữ liệu\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # Và một số phương thức truy xuất/thay đổi thông tin về mặt nội dung \n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Thêm mô hình và tệp tokenizer\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Làm bất cứ điều gì với mô hình, huấn luyện nó, tinh chỉnh nó ...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Chia sẻ các mô hình huấn luyện trước (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter4/section3_tf.ipynb b/course/vi/chapter4/section3_tf.ipynb
deleted file mode 100644
index 20a5acf5..00000000
--- a/course/vi/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chia sẻ các mô hình huấn luyện trước (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # Quản lý người dùng\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Tạo và quản lý kho dữ liệu\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # Và một số phương thức truy xuất/thay đổi thông tin về mặt nội dung \n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Thêm mô hình và tệp tokenizer\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Làm bất cứ điều gì với mô hình, huấn luyện nó, tinh chỉnh nó ...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Chia sẻ các mô hình huấn luyện trước (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter5/section2.ipynb b/course/vi/chapter5/section2.ipynb
deleted file mode 100644
index 482e349a..00000000
--- a/course/vi/chapter5/section2.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nếu như dữ liệu của ta không trên Hub thì sao?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz\n",
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gzip -dkv SQuAD_it-*.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=\"SQuAD_it-train.json\", field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    \"title\": \"Terremoto del Sichuan del 2008\",\n",
-       "    \"paragraphs\": [\n",
-       "        {\n",
-       "            \"context\": \"Il terremoto del Sichuan del 2008 o il terremoto...\",\n",
-       "            \"qas\": [\n",
-       "                {\n",
-       "                    \"answers\": [{\"answer_start\": 29, \"text\": \"2008\"}],\n",
-       "                    \"id\": \"56cdca7862d2951400fa6826\",\n",
-       "                    \"question\": \"In quale anno si è verificato il terremoto nel Sichuan?\",\n",
-       "                },\n",
-       "                ...\n",
-       "            ],\n",
-       "        },\n",
-       "        ...\n",
-       "    ],\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 48\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json\", \"test\": \"SQuAD_it-test.json\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")\n",
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json.gz\", \"test\": \"SQuAD_it-test.json.gz\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/crux82/squad-it/raw/master/\"\n",
-    "data_files = {\n",
-    "    \"train\": url + \"SQuAD_it-train.json.gz\",\n",
-    "    \"test\": url + \"SQuAD_it-test.json.gz\",\n",
-    "}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Nếu như dữ liệu của ta không trên Hub thì sao?",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter5/section3.ipynb b/course/vi/chapter5/section3.ipynb
deleted file mode 100644
index 843d6d06..00000000
--- a/course/vi/chapter5/section3.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sắp xếp dữ liệu"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
-    "!unzip drugsCom_raw.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
-    "# \\t is the tab character in Python\n",
-    "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Unnamed: 0': [87571, 178045, 80482],\n",
-       " 'drugName': ['Naproxen', 'Duloxetine', 'Mobic'],\n",
-       " 'condition': ['Gout, Acute', 'ibromyalgia', 'Inflammatory Conditions'],\n",
-       " 'review': ['\"like the previous person mention, I&#039;m a strong believer of aleve, it works faster for my gout than the prescription meds I take. No more going to the doctor for refills.....Aleve works!\"',\n",
-       "  '\"I have taken Cymbalta for about a year and a half for fibromyalgia pain. It is great\\r\\nas a pain reducer and an anti-depressant, however, the side effects outweighed \\r\\nany benefit I got from it. I had trouble with restlessness, being tired constantly,\\r\\ndizziness, dry mouth, numbness and tingling in my feet, and horrible sweating. I am\\r\\nbeing weaned off of it now. Went from 60 mg to 30mg and now to 15 mg. I will be\\r\\noff completely in about a week. The fibro pain is coming back, but I would rather deal with it than the side effects.\"',\n",
-       "  '\"I have been taking Mobic for over a year with no side effects other than an elevated blood pressure.  I had severe knee and ankle pain which completely went away after taking Mobic.  I attempted to stop the medication however pain returned after a few days.\"'],\n",
-       " 'rating': [9.0, 3.0, 10.0],\n",
-       " 'date': ['September 2, 2015', 'November 7, 2011', 'June 5, 2013'],\n",
-       " 'usefulCount': [36, 13, 128]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
-    "# Xem qua một số ví dụ đầu tiên\n",
-    "drug_sample[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split in drug_dataset.keys():\n",
-    "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 161297\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 53766\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.rename_column(\n",
-    "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
-    ")\n",
-    "drug_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AttributeError: 'NoneType' object has no attribute 'lower'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def lowercase_condition(example):\n",
-    "    return {\"condition\": example[\"condition\"].lower()}\n",
-    "\n",
-    "\n",
-    "drug_dataset.map(lowercase_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_nones(x):\n",
-    "    return x[\"condition\"] is not None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x * x)(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda base, height: 0.5 * base * height)(4, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['left ventricular dysfunction', 'adhd', 'birth control']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(lowercase_condition)\n",
-    "# Kiểm tra xem chữ viết thường đã hoạt động chưa\n",
-    "drug_dataset[\"train\"][\"condition\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_review_length(example):\n",
-    "    return {\"review_length\": len(example[\"review\"].split())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': 206461,\n",
-       " 'drugName': 'Valsartan',\n",
-       " 'condition': 'left ventricular dysfunction',\n",
-       " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
-       " 'rating': 9.0,\n",
-       " 'date': 'May 20, 2012',\n",
-       " 'usefulCount': 27,\n",
-       " 'review_length': 17}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(compute_review_length)\n",
-    "# Kiểm tra mẫu huấn luyện đầu tiên\n",
-    "drug_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': [103488, 23627, 20558],\n",
-       " 'drugName': ['Loestrin 21 1 / 20', 'Chlorzoxazone', 'Nucynta'],\n",
-       " 'condition': ['birth control', 'muscle spasm', 'pain'],\n",
-       " 'review': ['\"Excellent.\"', '\"useless\"', '\"ok\"'],\n",
-       " 'rating': [10.0, 1.0, 6.0],\n",
-       " 'date': ['November 4, 2008', 'March 24, 2017', 'August 20, 2016'],\n",
-       " 'usefulCount': [5, 2, 10],\n",
-       " 'review_length': [1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'train': 138514, 'test': 46108}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
-    "print(drug_dataset.num_rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm a transformer called BERT\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import html\n",
-    "\n",
-    "text = \"I&#039;m a transformer called BERT\"\n",
-    "html.unescape(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_drug_dataset = drug_dataset.map(\n",
-    "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "\n",
-    "def tokenize_function(examples):\n",
-    "    return tokenizer(examples[\"review\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%time tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
-    "\n",
-    "\n",
-    "def slow_tokenize_function(examples):\n",
-    "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    return tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[128, 49]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
-    "[len(inp) for inp in result[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ArrowInvalid: Column 1 named condition expected length 1463 but got length 1000"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(\n",
-    "    tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(206772, 138514)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    result = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    # Extract mapping between new and old indices\n",
-    "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
-    "    for key, values in examples.items():\n",
-    "        result[key] = [values[i] for i in sample_map]\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 206772\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 68876\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.set_format(\"pandas\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df = drug_dataset[\"train\"][:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frequencies = (\n",
-    "    train_df[\"condition\"]\n",
-    "    .value_counts()\n",
-    "    .to_frame()\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
-    ")\n",
-    "frequencies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['condition', 'frequency'],\n",
-       "    num_rows: 819\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "freq_dataset = Dataset.from_pandas(frequencies)\n",
-    "freq_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
-    "# Thay đổi tên mặc định \"test\" thành \"validation\"\n",
-    "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
-    "# Thêm \"test\" vào `DatasetDict`\n",
-    "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
-    "drug_dataset_clean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_from_disk\n",
-    "\n",
-    "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
-    "drug_dataset_reloaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split, dataset in drug_dataset_clean.items():\n",
-    "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\"patient_id\":141780,\"drugName\":\"Escitalopram\",\"condition\":\"depression\",\"review\":\"\\\"I seemed to experience the regular side effects of LEXAPRO, insomnia, low sex drive, sleepiness during the day. I am taking it at night because my doctor said if it made me tired to take it at night. I assumed it would and started out taking it at night. Strange dreams, some pleasant. I was diagnosed with fibromyalgia. Seems to be helping with the pain. Have had anxiety and depression in my family, and have tried quite a few other medications that haven't worked. Only have been on it for two weeks but feel more positive in my mind, want to accomplish more in my life. Hopefully the side effects will dwindle away, worth it to stick with it from hearing others responses. Great medication.\\\"\",\"rating\":9.0,\"date\":\"May 29, 2011\",\"usefulCount\":10,\"review_length\":125}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "!head -n 1 drug-reviews-train.jsonl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\n",
-    "    \"train\": \"drug-reviews-train.jsonl\",\n",
-    "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
-    "    \"test\": \"drug-reviews-test.jsonl\",\n",
-    "}\n",
-    "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Sắp xếp dữ liệu",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter5/section4.ipynb b/course/vi/chapter5/section4.ipynb
deleted file mode 100644
index deee1272..00000000
--- a/course/vi/chapter5/section4.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dữ liệu lớn? 🤗 Bộ dữ liệu để giải cứu!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install zstandard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['meta', 'text'],\n",
-       "    num_rows: 15518009\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Quá trình này mất một vài phút để chạy, vì vậy hãy làm cốc trà hoặc cà phê trong khi chờ đợi :)\n",
-    "data_files = \"https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\n",
-    "pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "pubmed_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pubmed_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install psutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RAM used: 5678.33 MB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# Process.memory_info được biểu thị bằng bytes, sau đó chuyển sang megabytes\n",
-    "print(f\"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of files in dataset : 20979437051\n",
-       "Dataset size (cache file) : 19.54 GB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(f\"Number of files in dataset : {pubmed_dataset.dataset_size}\")\n",
-    "size_gb = pubmed_dataset.dataset_size / (1024**3)\n",
-    "print(f\"Dataset size (cache file) : {size_gb:.2f} GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Iterated over 15518009 examples (about 19.5 GB) in 64.2s, i.e. 0.304 GB/s'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import timeit\n",
-    "\n",
-    "code_snippet = \"\"\"batch_size = 1000\n",
-    "\n",
-    "for idx in range(0, len(pubmed_dataset), batch_size):\n",
-    "    _ = pubmed_dataset[idx:idx + batch_size]\n",
-    "\"\"\"\n",
-    "\n",
-    "time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())\n",
-    "print(\n",
-    "    f\"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in \"\n",
-    "    f\"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset_streamed = load_dataset(\n",
-    "    \"json\", data_files=data_files, split=\"train\", streaming=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iter(pubmed_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 4958, 5178, 4328, 6779, ...], 'attention_mask': [1, 1, 1, 1, 1, ...]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
-    "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"text\"]))\n",
-    "next(iter(tokenized_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11410799, 'language': 'eng'},\n",
-       " 'text': 'Randomized study of dose or schedule modification of granulocyte colony-stimulating factor in platinum-based chemotherapy for elderly patients with lung cancer ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)\n",
-    "next(iter(shuffled_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'pmid': 11409575, 'language': 'eng'},\n",
-       "  'text': 'Clinical signs of hypoxaemia in children with acute lower respiratory infection: indicators of oxygen therapy ...'},\n",
-       " {'meta': {'pmid': 11409576, 'language': 'eng'},\n",
-       "  'text': \"Hypoxaemia in children with severe pneumonia in Papua New Guinea ...\"},\n",
-       " {'meta': {'pmid': 11409577, 'language': 'eng'},\n",
-       "  'text': 'Oxygen concentrators and cylinders ...'},\n",
-       " {'meta': {'pmid': 11409578, 'language': 'eng'},\n",
-       "  'text': 'Oxygen supply in rural africa: a personal experience ...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_head = pubmed_dataset_streamed.take(5)\n",
-    "list(dataset_head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Bỏ qua 1,000 mẫu đầu tiên và đưa phần còn lại vào tập huấn luyện\n",
-    "train_dataset = shuffled_dataset.skip(1000)\n",
-    "# Lấy 1,000 ví dụ đầu tiên cho tập kiểm định\n",
-    "validation_dataset = shuffled_dataset.take(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'case_ID': '110921.json',\n",
-       "  'case_jurisdiction': 'scotus.tar.gz',\n",
-       "  'date_created': '2010-04-28T17:12:49Z'},\n",
-       " 'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "law_dataset_streamed = load_dataset(\n",
-    "    \"json\",\n",
-    "    data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\n",
-    "    split=\"train\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "next(iter(law_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'case_ID': '110921.json',\n",
-       "   'case_jurisdiction': 'scotus.tar.gz',\n",
-       "   'date_created': '2010-04-28T17:12:49Z'},\n",
-       "  'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from itertools import islice\n",
-    "from datasets import interleave_datasets\n",
-    "\n",
-    "combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])\n",
-    "list(islice(combined_dataset, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pile_set_name': 'Pile-CC'},\n",
-       " 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_url = \"https://the-eye.eu/public/AI/pile/\"\n",
-    "data_files = {\n",
-    "    \"train\": [base_url + \"train/\" + f\"{idx:02d}.jsonl.zst\" for idx in range(30)],\n",
-    "    \"validation\": base_url + \"val.jsonl.zst\",\n",
-    "    \"test\": base_url + \"test.jsonl.zst\",\n",
-    "}\n",
-    "pile_dataset = load_dataset(\"json\", data_files=data_files, streaming=True)\n",
-    "next(iter(pile_dataset[\"train\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Dữ liệu lớn? 🤗 Bộ dữ liệu để giải cứu!",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter5/section5.ipynb b/course/vi/chapter5/section5.ipynb
deleted file mode 100644
index e060b522..00000000
--- a/course/vi/chapter5/section5.ipynb
+++ /dev/null
@@ -1,524 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tạo tập dữ liệu của riêng bạn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install requests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n",
-    "response = requests.get(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.status_code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'repository_url': 'https://api.github.com/repos/huggingface/datasets',\n",
-       "  'labels_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/labels{/name}',\n",
-       "  'comments_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/comments',\n",
-       "  'events_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/events',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "  'id': 968650274,\n",
-       "  'node_id': 'MDExOlB1bGxSZXF1ZXN0NzEwNzUyMjc0',\n",
-       "  'number': 2792,\n",
-       "  'title': 'Update GooAQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'labels': [],\n",
-       "  'state': 'open',\n",
-       "  'locked': False,\n",
-       "  'assignee': None,\n",
-       "  'assignees': [],\n",
-       "  'milestone': None,\n",
-       "  'comments': 1,\n",
-       "  'created_at': '2021-08-12T11:40:18Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'closed_at': None,\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'active_lock_reason': None,\n",
-       "  'pull_request': {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/2792',\n",
-       "   'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "   'diff_url': 'https://github.com/huggingface/datasets/pull/2792.diff',\n",
-       "   'patch_url': 'https://github.com/huggingface/datasets/pull/2792.patch'},\n",
-       "  'body': '[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.',\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "GITHUB_TOKEN = xxx  # Sao chép token GitHub của bạn tại đây\n",
-    "headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import math\n",
-    "from pathlib import Path\n",
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "\n",
-    "def fetch_issues(\n",
-    "    owner=\"huggingface\",\n",
-    "    repo=\"datasets\",\n",
-    "    num_issues=10_000,\n",
-    "    rate_limit=5_000,\n",
-    "    issues_path=Path(\".\"),\n",
-    "):\n",
-    "    if not issues_path.is_dir():\n",
-    "        issues_path.mkdir(exist_ok=True)\n",
-    "\n",
-    "    batch = []\n",
-    "    all_issues = []\n",
-    "    per_page = 100  # Số vấn đề phải trả về trên mỗi trang\n",
-    "    num_pages = math.ceil(num_issues / per_page)\n",
-    "    base_url = \"https://api.github.com/repos\"\n",
-    "\n",
-    "    for page in tqdm(range(num_pages)):\n",
-    "        # Truy vấn với trạng thái state=all để nhận được cả vấn đề mở và đóng\n",
-    "        query = f\"issues?page={page}&per_page={per_page}&state=all\"\n",
-    "        issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n",
-    "        batch.extend(issues.json())\n",
-    "\n",
-    "        if len(batch) > rate_limit and len(all_issues) < num_issues:\n",
-    "            all_issues.extend(batch)\n",
-    "            batch = []  # Xả lô cho khoảng thời gian tiếp theo\n",
-    "            print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n",
-    "            time.sleep(60 * 60 + 1)\n",
-    "\n",
-    "    all_issues.extend(batch)\n",
-    "    df = pd.DataFrame.from_records(all_issues)\n",
-    "    df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n",
-    "    print(\n",
-    "        f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Tùy thuộc vào kết nối internet của bạn, quá trình này có thể mất vài phút để chạy ...\n",
-    "fetch_issues()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'timeline_url', 'performed_via_github_app'],\n",
-       "    num_rows: 3019\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = load_dataset(\"json\", data_files=\"datasets-issues.jsonl\", split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">> URL: https://github.com/huggingface/datasets/pull/850\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/850', 'html_url': 'https://github.com/huggingface/datasets/pull/850', 'diff_url': 'https://github.com/huggingface/datasets/pull/850.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/850.patch'}\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/issues/2773\n",
-       ">> Pull request: None\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/pull/783\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/783', 'html_url': 'https://github.com/huggingface/datasets/pull/783', 'diff_url': 'https://github.com/huggingface/datasets/pull/783.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/783.patch'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = issues_dataset.shuffle(seed=666).select(range(3))\n",
-    "\n",
-    "# In ra URL và kéo về các mục yêu cầu\n",
-    "for url, pr in zip(sample[\"html_url\"], sample[\"pull_request\"]):\n",
-    "    print(f\">> URL: {url}\")\n",
-    "    print(f\">> Pull request: {pr}\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"is_pull_request\": False if x[\"pull_request\"] is None else True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792#issuecomment-897594128',\n",
-       "  'issue_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'id': 897594128,\n",
-       "  'node_id': 'IC_kwDODunzps41gDMQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'created_at': '2021-08-12T12:21:52Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'body': \"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issue_number = 2792\n",
-    "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "response = requests.get(url, headers=headers)\n",
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\"]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def get_comments(issue_number):\n",
-    "    url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "    response = requests.get(url, headers=headers)\n",
-    "    return [r[\"body\"] for r in response.json()]\n",
-    "\n",
-    "\n",
-    "# Kiểm tra hàm có hoạt động như mong đợi không\n",
-    "get_comments(2792)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Tùy thuộc vào kết nối internet của bạn, quá trình này có thể mất vài phút ...\n",
-    "issues_with_comments_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"comments\": get_comments(x[\"number\"])}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_with_comments_dataset.to_json(\"issues-datasets-with-comments.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of datasets on Hub: 1487\n",
-       "Dataset Name: acronym_identification, Tags: ['annotations_creators:expert-generated', 'language_creators:found', 'languages:en', 'licenses:mit', 'multilinguality:monolingual', 'size_categories:10K<n<100K', 'source_datasets:original', 'task_categories:structure-prediction', 'task_ids:structure-prediction-other-acronym-identification']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_datasets\n",
-    "\n",
-    "all_datasets = list_datasets()\n",
-    "print(f\"Number of datasets on Hub: {len(all_datasets)}\")\n",
-    "print(all_datasets[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/datasets/lewtun/github-issues'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "repo_url = create_repo(name=\"github-issues\", repo_type=\"dataset\")\n",
-    "repo_url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)\n",
-    "!cp issues-datasets-with-comments.jsonl github-issues/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.lfs_track(\"*.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remote_dataset = load_dataset(\"lewtun/github-issues\", split=\"train\")\n",
-    "remote_dataset"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tạo tập dữ liệu của riêng bạn",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter5/section6_pt.ipynb b/course/vi/chapter5/section6_pt.ipynb
deleted file mode 100644
index 6720abfa..00000000
--- a/course/vi/chapter5/section6_pt.ipynb
+++ /dev/null
@@ -1,518 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tìm kiếm ngữ nghĩa với FAISS (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = AutoModel.from_pretrained(model_ckpt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
-    "    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).detach().cpu().numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tìm kiếm ngữ nghĩa với FAISS (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter5/section6_tf.ipynb b/course/vi/chapter5/section6_tf.ipynb
deleted file mode 100644
index 8bbd58c1..00000000
--- a/course/vi/chapter5/section6_tf.ipynb
+++ /dev/null
@@ -1,506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tìm kiếm ngữ nghĩa với FAISS (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
-    "    )\n",
-    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tìm kiếm ngữ nghĩa với FAISS (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter5/section8.ipynb b/course/vi/chapter5/section8.ipynb
deleted file mode 100644
index 444b44c1..00000000
--- a/course/vi/chapter5/section8.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Đố vui cuối chương"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"allocine\", streaming=True, split=\"train\")\n",
-    "dataset[0]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Đố vui cuối chương",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section2.ipynb b/course/vi/chapter6/section2.ipynb
deleted file mode 100644
index 51cb5d64..00000000
--- a/course/vi/chapter6/section2.ipynb
+++ /dev/null
@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Huấn luyện một tokenizer mới từ cái cũ"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# Quá trình này có thể mất một vài phút để tải, vì vậy hãy lấy cà phê hoặc trà trong khi chờ đợi!\n",
-    "raw_datasets = load_dataset(\"code_search_net\", \"python\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['repository_name', 'func_path_in_repository', 'func_name', 'whole_func_string', 'language', \n",
-       "      'func_code_string', 'func_code_tokens', 'func_documentation_string', 'func_documentation_tokens', 'split_name', \n",
-       "      'func_code_url'\n",
-       "    ],\n",
-       "    num_rows: 412178\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(raw_datasets[\"train\"][123456][\"whole_func_string\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Đừng bỏ ghi chú dòng bên dưới trừ khi tập dữ liệu của bạn nhỏ!\n",
-    "# training_corpus = [raw_datasets[\"train\"][i: i + 1000][\"whole_func_string\"] for i in range(0, len(raw_datasets[\"train\"]), 1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_corpus = (\n",
-    "    raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "    for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
-       "[]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen = (i for i in range(10))\n",
-    "print(list(gen))\n",
-    "print(list(gen))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    return (\n",
-    "        raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "        for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "training_corpus = get_training_corpus()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    dataset = raw_datasets[\"train\"]\n",
-    "    for start_idx in range(0, len(dataset), 1000):\n",
-    "        samples = dataset[start_idx : start_idx + 1000]\n",
-    "        yield samples[\"whole_func_string\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "old_tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'n', 'umbers', '(', 'a', ',', 'Ġb', '):', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo',\n",
-       " 'Ġnumbers', 'Ġ`', 'a', '`', 'Ġand', 'Ġ`', 'b', '`', '.\"', '\"\"', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = '''def add_numbers(a, b):\n",
-    "    \"\"\"Add the two numbers `a` and `b`.\"\"\"\n",
-    "    return a + b'''\n",
-    "\n",
-    "tokens = old_tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'numbers', '(', 'a', ',', 'Ġb', '):', 'ĊĠĠĠ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo', 'Ġnumbers', 'Ġ`',\n",
-       " 'a', '`', 'Ġand', 'Ġ`', 'b', '`.\"\"\"', 'ĊĠĠĠ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokens = tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27\n",
-       "36"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(len(tokens))\n",
-    "print(len(old_tokenizer.tokenize(example)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['class', 'ĠLinear', 'Layer', '():', 'ĊĠĠĠ', 'Ġdef', 'Ġ__', 'init', '__(', 'self', ',', 'Ġinput', '_', 'size', ',',\n",
-       " 'Ġoutput', '_', 'size', '):', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'weight', 'Ġ=', 'Ġtorch', '.', 'randn', '(', 'input', '_',\n",
-       " 'size', ',', 'Ġoutput', '_', 'size', ')', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'bias', 'Ġ=', 'Ġtorch', '.', 'zeros', '(',\n",
-       " 'output', '_', 'size', ')', 'ĊĊĠĠĠ', 'Ġdef', 'Ġ__', 'call', '__(', 'self', ',', 'Ġx', '):', 'ĊĠĠĠĠĠĠĠ',\n",
-       " 'Ġreturn', 'Ġx', 'Ġ@', 'Ġself', '.', 'weights', 'Ġ+', 'Ġself', '.', 'bias', 'ĊĠĠĠĠ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = \"\"\"class LinearLayer():\n",
-    "    def __init__(self, input_size, output_size):\n",
-    "        self.weight = torch.randn(input_size, output_size)\n",
-    "        self.bias = torch.zeros(output_size)\n",
-    "\n",
-    "    def __call__(self, x):\n",
-    "        return x @ self.weights + self.bias\n",
-    "    \"\"\"\n",
-    "tokenizer.tokenize(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Thay \"huggingface-course\" dưới đấy với tên không gian thực sự sử dụng tokenizer riêng của bạn\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Huấn luyện một tokenizer mới từ cái cũ",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section3_pt.ipynb b/course/vi/chapter6/section3_pt.ipynb
deleted file mode 100644
index 0d2b14b0..00000000
--- a/course/vi/chapter6/section3_pt.ipynb
+++ /dev/null
@@ -1,515 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sức mạnh đặc biệt của tokenizer nhanh (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"pt\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 19])\n",
-       "torch.Size([1, 19, 9])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()\n",
-    "predictions = outputs.logits.argmax(dim=-1)[0].tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Xoá B- hoặc I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Lấy tất cả các tokens có nhãn I-\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # Điểm là giá trị trung bình của tất cả điểm của các token trong thực thể được nhóm đó\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Sức mạnh đặc biệt của tokenizer nhanh (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section3_tf.ipynb b/course/vi/chapter6/section3_tf.ipynb
deleted file mode 100644
index 070d290a..00000000
--- a/course/vi/chapter6/section3_tf.ipynb
+++ /dev/null
@@ -1,517 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sức mạnh đặc biệt của tokenizer nhanh (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = TFAutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"tf\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 19)\n",
-       "(1, 19, 9)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "probabilities = tf.math.softmax(outputs.logits, axis=-1)[0]\n",
-    "probabilities = probabilities.numpy().tolist()\n",
-    "predictions = tf.math.argmax(outputs.logits, axis=-1)[0]\n",
-    "predictions = predictions.numpy().tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Xoá B- hoặc I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Lấy tất cả các tokens có nhãn I-\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # Điểm là giá trị trung bình của tất cả điểm của các token trong thực thể được nhóm đó\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Sức mạnh đặc biệt của tokenizer nhanh (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section3b_pt.ipynb b/course/vi/chapter6/section3b_pt.ipynb
deleted file mode 100644
index 889ef302..00000000
--- a/course/vi/chapter6/section3b_pt.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizer nhanh trong pipeline QA (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97773,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch, and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97149,\n",
-       " 'start': 1892,\n",
-       " 'end': 1919,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "long_context = \"\"\"\n",
-    "🤗 Transformers: State of the Art NLP\n",
-    "\n",
-    "🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-    "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-    "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-    "\n",
-    "🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-    "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-    "can be modified to enable quick research experiments.\n",
-    "\n",
-    "Why should I use transformers?\n",
-    "\n",
-    "1. Easy-to-use state-of-the-art models:\n",
-    "  - High performance on NLU and NLG tasks.\n",
-    "  - Low barrier to entry for educators and practitioners.\n",
-    "  - Few user-facing abstractions with just three classes to learn.\n",
-    "  - A unified API for using all our pretrained models.\n",
-    "  - Lower compute costs, smaller carbon footprint:\n",
-    "\n",
-    "2. Researchers can share trained models instead of always retraining.\n",
-    "  - Practitioners can reduce compute time and production costs.\n",
-    "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-    "\n",
-    "3. Choose the right framework for every part of a model's lifetime:\n",
-    "  - Train state-of-the-art models in 3 lines of code.\n",
-    "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-    "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-    "\n",
-    "4. Easily customize a model or an example to your needs:\n",
-    "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-    "  - Model internals are exposed as consistently as possible.\n",
-    "  - Model files can be used independently of the library for quick experiments.\n",
-    "\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question_answerer(question=question, context=long_context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "inputs = tokenizer(question, context, return_tensors=\"pt\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 66]) torch.Size([1, 66])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Che tất cả mọi thứ trừ token của ngữ cảnh\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Hiển thị token [CLS]\n",
-    "mask[0] = False\n",
-    "mask = torch.tensor(mask)[None]\n",
-    "\n",
-    "start_logits[mask] = -10000\n",
-    "end_logits[mask] = -10000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)[0]\n",
-    "end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = start_probabilities[:, None] * end_probabilities[None, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = torch.triu(scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97773"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_index = scores.argmax().item()\n",
-    "start_index = max_index // scores.shape[1]\n",
-    "end_index = max_index % scores.shape[1]\n",
-    "print(scores[start_index, end_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "start_char, _ = offsets[start_index]\n",
-    "_, end_char = offsets[end_index]\n",
-    "answer = context[start_char:end_char]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': 'Jax, PyTorch and TensorFlow',\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'score': 0.97773}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = {\n",
-    "    \"answer\": answer,\n",
-    "    \"start\": start_char,\n",
-    "    \"end\": end_char,\n",
-    "    \"score\": scores[start_index, end_index],\n",
-    "}\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "461"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context)\n",
-    "print(len(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "[CLS] Which deep learning libraries back [UNK] Transformers? [SEP] [UNK] Transformers : State of the Art NLP\n",
-       "\n",
-       "[UNK] Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-       "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-       "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-       "\n",
-       "[UNK] Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-       "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-       "can be modified to enable quick research experiments.\n",
-       "\n",
-       "Why should I use transformers?\n",
-       "\n",
-       "1. Easy-to-use state-of-the-art models:\n",
-       "  - High performance on NLU and NLG tasks.\n",
-       "  - Low barrier to entry for educators and practitioners.\n",
-       "  - Few user-facing abstractions with just three classes to learn.\n",
-       "  - A unified API for using all our pretrained models.\n",
-       "  - Lower compute costs, smaller carbon footprint:\n",
-       "\n",
-       "2. Researchers can share trained models instead of always retraining.\n",
-       "  - Practitioners can reduce compute time and production costs.\n",
-       "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-       "\n",
-       "3. Choose the right framework for every part of a model's lifetime:\n",
-       "  - Train state-of-the-art models in 3 lines of code.\n",
-       "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-       "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-       "\n",
-       "4. Easily customize a model or an example to your needs:\n",
-       "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-       "  - Model internal [SEP]\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n",
-    "print(tokenizer.decode(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] This sentence is not [SEP]'\n",
-       "'[CLS] is not too long [SEP]'\n",
-       "'[CLS] too long but we [SEP]'\n",
-       "'[CLS] but we are going [SEP]'\n",
-       "'[CLS] are going to split [SEP]'\n",
-       "'[CLS] to split it anyway [SEP]'\n",
-       "'[CLS] it anyway. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"This sentence is not too long but we are going to split it anyway.\"\n",
-    "inputs = tokenizer(\n",
-    "    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'attention_mask', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentences = [\n",
-    "    \"This sentence is not too long but we are going to split it anyway.\",\n",
-    "    \"This sentence is shorter but will still get split.\",\n",
-    "]\n",
-    "inputs = tokenizer(\n",
-    "    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    long_context,\n",
-    "    stride=128,\n",
-    "    max_length=384,\n",
-    "    padding=\"longest\",\n",
-    "    truncation=\"only_second\",\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 384])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_ = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "offsets = inputs.pop(\"offset_mapping\")\n",
-    "\n",
-    "inputs = inputs.convert_to_tensors(\"pt\")\n",
-    "print(inputs[\"input_ids\"].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 384]) torch.Size([2, 384])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "\n",
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Che tất cả mọi thứ trừ token của ngữ cảnh\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Hiển thị token [CLS]\n",
-    "mask[0] = False\n",
-    "# Che tất cả token [PAD]\n",
-    "mask = torch.logical_or(torch.tensor(mask)[None], (inputs[\"attention_mask\"] == 0))\n",
-    "\n",
-    "start_logits[mask] = -10000\n",
-    "end_logits[mask] = -10000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)\n",
-    "end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 18, 0.33867), (173, 184, 0.97149)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidates = []\n",
-    "for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n",
-    "    scores = start_probs[:, None] * end_probs[None, :]\n",
-    "    idx = torch.triu(scores).argmax().item()\n",
-    "\n",
-    "    start_idx = idx // scores.shape[1]\n",
-    "    end_idx = idx % scores.shape[1]\n",
-    "    score = scores[start_idx, end_idx].item()\n",
-    "    candidates.append((start_idx, end_idx, score))\n",
-    "\n",
-    "print(candidates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': '\\n🤗 Transformers: State of the Art NLP', 'start': 0, 'end': 37, 'score': 0.33867}\n",
-       "{'answer': 'Jax, PyTorch and TensorFlow', 'start': 1892, 'end': 1919, 'score': 0.97149}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for candidate, offset in zip(candidates, offsets):\n",
-    "    start_token, end_token, score = candidate\n",
-    "    start_char, _ = offset[start_token]\n",
-    "    _, end_char = offset[end_token]\n",
-    "    answer = long_context[start_char:end_char]\n",
-    "    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n",
-    "    print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizer nhanh trong pipeline QA (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section3b_tf.ipynb b/course/vi/chapter6/section3b_tf.ipynb
deleted file mode 100644
index 3df49aea..00000000
--- a/course/vi/chapter6/section3b_tf.ipynb
+++ /dev/null
@@ -1,604 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizer nhanh trong pipeline QA (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97773,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch, and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97149,\n",
-       " 'start': 1892,\n",
-       " 'end': 1919,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "long_context = \"\"\"\n",
-    "🤗 Transformers: State of the Art NLP\n",
-    "\n",
-    "🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-    "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-    "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-    "\n",
-    "🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-    "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-    "can be modified to enable quick research experiments.\n",
-    "\n",
-    "Why should I use transformers?\n",
-    "\n",
-    "1. Easy-to-use state-of-the-art models:\n",
-    "  - High performance on NLU and NLG tasks.\n",
-    "  - Low barrier to entry for educators and practitioners.\n",
-    "  - Few user-facing abstractions with just three classes to learn.\n",
-    "  - A unified API for using all our pretrained models.\n",
-    "  - Lower compute costs, smaller carbon footprint:\n",
-    "\n",
-    "2. Researchers can share trained models instead of always retraining.\n",
-    "  - Practitioners can reduce compute time and production costs.\n",
-    "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-    "\n",
-    "3. Choose the right framework for every part of a model's lifetime:\n",
-    "  - Train state-of-the-art models in 3 lines of code.\n",
-    "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-    "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-    "\n",
-    "4. Easily customize a model or an example to your needs:\n",
-    "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-    "  - Model internals are exposed as consistently as possible.\n",
-    "  - Model files can be used independently of the library for quick experiments.\n",
-    "\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question_answerer(question=question, context=long_context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "inputs = tokenizer(question, context, return_tensors=\"tf\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 66) (1, 66)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Che tất cả mọi thứ trừ token của ngữ cảnh\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Hiển thị token [CLS]\n",
-    "mask[0] = False\n",
-    "mask = tf.constant(mask)[None]\n",
-    "\n",
-    "start_logits = tf.where(mask, -10000, start_logits)\n",
-    "end_logits = tf.where(mask, -10000, end_logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = tf.math.softmax(start_logits, axis=-1)[0].numpy()\n",
-    "end_probabilities = tf.math.softmax(end_logits, axis=-1)[0].numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = start_probabilities[:, None] * end_probabilities[None, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "scores = np.triu(scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97773"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_index = scores.argmax().item()\n",
-    "start_index = max_index // scores.shape[1]\n",
-    "end_index = max_index % scores.shape[1]\n",
-    "print(scores[start_index, end_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "start_char, _ = offsets[start_index]\n",
-    "_, end_char = offsets[end_index]\n",
-    "answer = context[start_char:end_char]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': 'Jax, PyTorch and TensorFlow',\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'score': 0.97773}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = {\n",
-    "    \"answer\": answer,\n",
-    "    \"start\": start_char,\n",
-    "    \"end\": end_char,\n",
-    "    \"score\": scores[start_index, end_index],\n",
-    "}\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "461"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context)\n",
-    "print(len(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "[CLS] Which deep learning libraries back [UNK] Transformers? [SEP] [UNK] Transformers : State of the Art NLP\n",
-       "\n",
-       "[UNK] Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-       "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-       "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-       "\n",
-       "[UNK] Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-       "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-       "can be modified to enable quick research experiments.\n",
-       "\n",
-       "Why should I use transformers?\n",
-       "\n",
-       "1. Easy-to-use state-of-the-art models:\n",
-       "  - High performance on NLU and NLG tasks.\n",
-       "  - Low barrier to entry for educators and practitioners.\n",
-       "  - Few user-facing abstractions with just three classes to learn.\n",
-       "  - A unified API for using all our pretrained models.\n",
-       "  - Lower compute costs, smaller carbon footprint:\n",
-       "\n",
-       "2. Researchers can share trained models instead of always retraining.\n",
-       "  - Practitioners can reduce compute time and production costs.\n",
-       "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-       "\n",
-       "3. Choose the right framework for every part of a model's lifetime:\n",
-       "  - Train state-of-the-art models in 3 lines of code.\n",
-       "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-       "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-       "\n",
-       "4. Easily customize a model or an example to your needs:\n",
-       "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-       "  - Model internal [SEP]\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n",
-    "print(tokenizer.decode(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] This sentence is not [SEP]'\n",
-       "'[CLS] is not too long [SEP]'\n",
-       "'[CLS] too long but we [SEP]'\n",
-       "'[CLS] but we are going [SEP]'\n",
-       "'[CLS] are going to split [SEP]'\n",
-       "'[CLS] to split it anyway [SEP]'\n",
-       "'[CLS] it anyway. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"This sentence is not too long but we are going to split it anyway.\"\n",
-    "inputs = tokenizer(\n",
-    "    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'attention_mask', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentences = [\n",
-    "    \"This sentence is not too long but we are going to split it anyway.\",\n",
-    "    \"This sentence is shorter but will still get split.\",\n",
-    "]\n",
-    "inputs = tokenizer(\n",
-    "    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    long_context,\n",
-    "    stride=128,\n",
-    "    max_length=384,\n",
-    "    padding=\"longest\",\n",
-    "    truncation=\"only_second\",\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 384)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_ = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "offsets = inputs.pop(\"offset_mapping\")\n",
-    "\n",
-    "inputs = inputs.convert_to_tensors(\"tf\")\n",
-    "print(inputs[\"input_ids\"].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 384) (2, 384)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "\n",
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Che tất cả mọi thứ trừ token của ngữ cảnh\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Hiển thị token [CLS]\n",
-    "mask[0] = False\n",
-    "# Che tất cả token [PAD]\n",
-    "mask = tf.math.logical_or(tf.constant(mask)[None], inputs[\"attention_mask\"] == 0)\n",
-    "\n",
-    "start_logits = tf.where(mask, -10000, start_logits)\n",
-    "end_logits = tf.where(mask, -10000, end_logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = tf.math.softmax(start_logits, axis=-1).numpy()\n",
-    "end_probabilities = tf.math.softmax(end_logits, axis=-1).numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 18, 0.33867), (173, 184, 0.97149)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidates = []\n",
-    "for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n",
-    "    scores = start_probs[:, None] * end_probs[None, :]\n",
-    "    idx = np.triu(scores).argmax().item()\n",
-    "\n",
-    "    start_idx = idx // scores.shape[1]\n",
-    "    end_idx = idx % scores.shape[1]\n",
-    "    score = scores[start_idx, end_idx].item()\n",
-    "    candidates.append((start_idx, end_idx, score))\n",
-    "\n",
-    "print(candidates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': '\\n🤗 Transformers: State of the Art NLP', 'start': 0, 'end': 37, 'score': 0.33867}\n",
-       "{'answer': 'Jax, PyTorch and TensorFlow', 'start': 1892, 'end': 1919, 'score': 0.97149}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for candidate, offset in zip(candidates, offsets):\n",
-    "    start_token, end_token, score = candidate\n",
-    "    start_char, _ = offset[start_token]\n",
-    "    _, end_char = offset[end_token]\n",
-    "    answer = long_context[start_char:end_char]\n",
-    "    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n",
-    "    print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizer nhanh trong pipeline QA (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section4.ipynb b/course/vi/chapter6/section4.ipynb
deleted file mode 100644
index 3e8db524..00000000
--- a/course/vi/chapter6/section4.ipynb
+++ /dev/null
@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chuẩn hoá và tiền tokenize"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'tokenizers.Tokenizer'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
-    "print(type(tokenizer.backend_tokenizer))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello how are u?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.backend_tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Hello', (0, 5)), (',', (5, 6)), ('how', (7, 10)), ('are', (11, 14)), ('you', (16, 19)), ('?', (19, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Hello', (0, 5)), (',', (5, 6)), ('Ġhow', (6, 10)), ('Ġare', (10, 14)), ('Ġ', (14, 15)), ('Ġyou', (15, 19)),\n",
-       " ('?', (19, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('▁Hello,', (0, 6)), ('▁how', (7, 10)), ('▁are', (11, 14)), ('▁you?', (16, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n",
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Chuẩn hoá và tiền tokenize",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section5.ipynb b/course/vi/chapter6/section5.ipynb
deleted file mode 100644
index 9c7a9cc8..00000000
--- a/course/vi/chapter6/section5.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Byte-Pair Encoding tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face Course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(int, {'This': 3, 'Ġis': 2, 'Ġthe': 1, 'ĠHugging': 1, 'ĠFace': 1, 'ĠCourse': 1, '.': 4, 'Ġchapter': 1,\n",
-       "    'Ġabout': 1, 'Ġtokenization': 1, 'Ġsection': 1, 'Ġshows': 1, 'Ġseveral': 1, 'Ġtokenizer': 1, 'Ġalgorithms': 1,\n",
-       "    'Hopefully': 1, ',': 1, 'Ġyou': 1, 'Ġwill': 1, 'Ġbe': 1, 'Ġable': 1, 'Ġto': 1, 'Ġunderstand': 1, 'Ġhow': 1,\n",
-       "    'Ġthey': 1, 'Ġare': 1, 'Ġtrained': 1, 'Ġand': 1, 'Ġgenerate': 1, 'Ġtokens': 1})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "print(word_freqs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's',\n",
-       "  't', 'u', 'v', 'w', 'y', 'z', 'Ġ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alphabet = []\n",
-    "\n",
-    "for word in word_freqs.keys():\n",
-    "    for letter in word:\n",
-    "        if letter not in alphabet:\n",
-    "            alphabet.append(letter)\n",
-    "alphabet.sort()\n",
-    "\n",
-    "print(alphabet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab = [\"<|endoftext|>\"] + alphabet.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "splits = {word: [c for c in word] for word in word_freqs.keys()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pair_freqs(splits):\n",
-    "    pair_freqs = defaultdict(int)\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "        for i in range(len(split) - 1):\n",
-    "            pair = (split[i], split[i + 1])\n",
-    "            pair_freqs[pair] += freq\n",
-    "    return pair_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('T', 'h'): 3\n",
-       "('h', 'i'): 3\n",
-       "('i', 's'): 5\n",
-       "('Ġ', 'i'): 2\n",
-       "('Ġ', 't'): 7\n",
-       "('t', 'h'): 3"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pair_freqs = compute_pair_freqs(splits)\n",
-    "\n",
-    "for i, key in enumerate(pair_freqs.keys()):\n",
-    "    print(f\"{key}: {pair_freqs[key]}\")\n",
-    "    if i >= 5:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Ġ', 't') 7"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_pair = \"\"\n",
-    "max_freq = None\n",
-    "\n",
-    "for pair, freq in pair_freqs.items():\n",
-    "    if max_freq is None or max_freq < freq:\n",
-    "        best_pair = pair\n",
-    "        max_freq = freq\n",
-    "\n",
-    "print(best_pair, max_freq)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "merges = {(\"Ġ\", \"t\"): \"Ġt\"}\n",
-    "vocab.append(\"Ġt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_pair(a, b, splits):\n",
-    "    for word in word_freqs:\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "\n",
-    "        i = 0\n",
-    "        while i < len(split) - 1:\n",
-    "            if split[i] == a and split[i + 1] == b:\n",
-    "                split = split[:i] + [a + b] + split[i + 2 :]\n",
-    "            else:\n",
-    "                i += 1\n",
-    "        splits[word] = split\n",
-    "    return splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Ġt', 'r', 'a', 'i', 'n', 'e', 'd']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "splits = merge_pair(\"Ġ\", \"t\", splits)\n",
-    "print(splits[\"Ġtrained\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab_size = 50\n",
-    "\n",
-    "while len(vocab) < vocab_size:\n",
-    "    pair_freqs = compute_pair_freqs(splits)\n",
-    "    best_pair = \"\"\n",
-    "    max_freq = None\n",
-    "    for pair, freq in pair_freqs.items():\n",
-    "        if max_freq is None or max_freq < freq:\n",
-    "            best_pair = pair\n",
-    "            max_freq = freq\n",
-    "    splits = merge_pair(*best_pair, splits)\n",
-    "    merges[best_pair] = best_pair[0] + best_pair[1]\n",
-    "    vocab.append(best_pair[0] + best_pair[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{('Ġ', 't'): 'Ġt', ('i', 's'): 'is', ('e', 'r'): 'er', ('Ġ', 'a'): 'Ġa', ('Ġt', 'o'): 'Ġto', ('e', 'n'): 'en',\n",
-       " ('T', 'h'): 'Th', ('Th', 'is'): 'This', ('o', 'u'): 'ou', ('s', 'e'): 'se', ('Ġto', 'k'): 'Ġtok',\n",
-       " ('Ġtok', 'en'): 'Ġtoken', ('n', 'd'): 'nd', ('Ġ', 'is'): 'Ġis', ('Ġt', 'h'): 'Ġth', ('Ġth', 'e'): 'Ġthe',\n",
-       " ('i', 'n'): 'in', ('Ġa', 'b'): 'Ġab', ('Ġtoken', 'i'): 'Ġtokeni'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(merges)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['<|endoftext|>', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o',\n",
-       " 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'Ġ', 'Ġt', 'is', 'er', 'Ġa', 'Ġto', 'en', 'Th', 'This', 'ou', 'se',\n",
-       " 'Ġtok', 'Ġtoken', 'nd', 'Ġis', 'Ġth', 'Ġthe', 'in', 'Ġab', 'Ġtokeni']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize(text):\n",
-    "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
-    "    splits = [[l for l in word] for word in pre_tokenized_text]\n",
-    "    for pair, merge in merges.items():\n",
-    "        for idx, split in enumerate(splits):\n",
-    "            i = 0\n",
-    "            while i < len(split) - 1:\n",
-    "                if split[i] == pair[0] and split[i + 1] == pair[1]:\n",
-    "                    split = split[:i] + [merge] + split[i + 2 :]\n",
-    "                else:\n",
-    "                    i += 1\n",
-    "            splits[idx] = split\n",
-    "\n",
-    "    return sum(splits, [])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['This', 'Ġis', 'Ġ', 'n', 'o', 't', 'Ġa', 'Ġtoken', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenize(\"This is not a token.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Byte-Pair Encoding tokenization",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section6.ipynb b/course/vi/chapter6/section6.ipynb
deleted file mode 100644
index bd05a08e..00000000
--- a/course/vi/chapter6/section6.ipynb
+++ /dev/null
@@ -1,406 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# WordPiece tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face Course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(\n",
-       "    int, {'This': 3, 'is': 2, 'the': 1, 'Hugging': 1, 'Face': 1, 'Course': 1, '.': 4, 'chapter': 1, 'about': 1,\n",
-       "    'tokenization': 1, 'section': 1, 'shows': 1, 'several': 1, 'tokenizer': 1, 'algorithms': 1, 'Hopefully': 1,\n",
-       "    ',': 1, 'you': 1, 'will': 1, 'be': 1, 'able': 1, 'to': 1, 'understand': 1, 'how': 1, 'they': 1, 'are': 1,\n",
-       "    'trained': 1, 'and': 1, 'generate': 1, 'tokens': 1})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "word_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['##a', '##b', '##c', '##d', '##e', '##f', '##g', '##h', '##i', '##k', '##l', '##m', '##n', '##o', '##p', '##r', '##s',\n",
-       " '##t', '##u', '##v', '##w', '##y', '##z', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'g', 'h', 'i', 's', 't', 'u',\n",
-       " 'w', 'y']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alphabet = []\n",
-    "for word in word_freqs.keys():\n",
-    "    if word[0] not in alphabet:\n",
-    "        alphabet.append(word[0])\n",
-    "    for letter in word[1:]:\n",
-    "        if f\"##{letter}\" not in alphabet:\n",
-    "            alphabet.append(f\"##{letter}\")\n",
-    "\n",
-    "alphabet.sort()\n",
-    "alphabet\n",
-    "\n",
-    "print(alphabet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab = [\"[PAD]\", \"[UNK]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"] + alphabet.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "splits = {\n",
-    "    word: [c if i == 0 else f\"##{c}\" for i, c in enumerate(word)]\n",
-    "    for word in word_freqs.keys()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pair_scores(splits):\n",
-    "    letter_freqs = defaultdict(int)\n",
-    "    pair_freqs = defaultdict(int)\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            letter_freqs[split[0]] += freq\n",
-    "            continue\n",
-    "        for i in range(len(split) - 1):\n",
-    "            pair = (split[i], split[i + 1])\n",
-    "            letter_freqs[split[i]] += freq\n",
-    "            pair_freqs[pair] += freq\n",
-    "        letter_freqs[split[-1]] += freq\n",
-    "\n",
-    "    scores = {\n",
-    "        pair: freq / (letter_freqs[pair[0]] * letter_freqs[pair[1]])\n",
-    "        for pair, freq in pair_freqs.items()\n",
-    "    }\n",
-    "    return scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('T', '##h'): 0.125\n",
-       "('##h', '##i'): 0.03409090909090909\n",
-       "('##i', '##s'): 0.02727272727272727\n",
-       "('i', '##s'): 0.1\n",
-       "('t', '##h'): 0.03571428571428571\n",
-       "('##h', '##e'): 0.011904761904761904"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pair_scores = compute_pair_scores(splits)\n",
-    "for i, key in enumerate(pair_scores.keys()):\n",
-    "    print(f\"{key}: {pair_scores[key]}\")\n",
-    "    if i >= 5:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('a', '##b') 0.2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_pair = \"\"\n",
-    "max_score = None\n",
-    "for pair, score in pair_scores.items():\n",
-    "    if max_score is None or max_score < score:\n",
-    "        best_pair = pair\n",
-    "        max_score = score\n",
-    "\n",
-    "print(best_pair, max_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab.append(\"ab\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_pair(a, b, splits):\n",
-    "    for word in word_freqs:\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "        i = 0\n",
-    "        while i < len(split) - 1:\n",
-    "            if split[i] == a and split[i + 1] == b:\n",
-    "                merge = a + b[2:] if b.startswith(\"##\") else a + b\n",
-    "                split = split[:i] + [merge] + split[i + 2 :]\n",
-    "            else:\n",
-    "                i += 1\n",
-    "        splits[word] = split\n",
-    "    return splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ab', '##o', '##u', '##t']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "splits = merge_pair(\"a\", \"##b\", splits)\n",
-    "splits[\"about\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab_size = 70\n",
-    "while len(vocab) < vocab_size:\n",
-    "    scores = compute_pair_scores(splits)\n",
-    "    best_pair, max_score = \"\", None\n",
-    "    for pair, score in scores.items():\n",
-    "        if max_score is None or max_score < score:\n",
-    "            best_pair = pair\n",
-    "            max_score = score\n",
-    "    splits = merge_pair(*best_pair, splits)\n",
-    "    new_token = (\n",
-    "        best_pair[0] + best_pair[1][2:]\n",
-    "        if best_pair[1].startswith(\"##\")\n",
-    "        else best_pair[0] + best_pair[1]\n",
-    "    )\n",
-    "    vocab.append(new_token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]', '##a', '##b', '##c', '##d', '##e', '##f', '##g', '##h', '##i', '##k',\n",
-       " '##l', '##m', '##n', '##o', '##p', '##r', '##s', '##t', '##u', '##v', '##w', '##y', '##z', ',', '.', 'C', 'F', 'H',\n",
-       " 'T', 'a', 'b', 'c', 'g', 'h', 'i', 's', 't', 'u', 'w', 'y', 'ab', '##fu', 'Fa', 'Fac', '##ct', '##ful', '##full', '##fully',\n",
-       " 'Th', 'ch', '##hm', 'cha', 'chap', 'chapt', '##thm', 'Hu', 'Hug', 'Hugg', 'sh', 'th', 'is', '##thms', '##za', '##zat',\n",
-       " '##ut']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_word(word):\n",
-    "    tokens = []\n",
-    "    while len(word) > 0:\n",
-    "        i = len(word)\n",
-    "        while i > 0 and word[:i] not in vocab:\n",
-    "            i -= 1\n",
-    "        if i == 0:\n",
-    "            return [\"[UNK]\"]\n",
-    "        tokens.append(word[:i])\n",
-    "        word = word[i:]\n",
-    "        if len(word) > 0:\n",
-    "            word = f\"##{word}\"\n",
-    "    return tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Hugg', '##i', '##n', '##g']\n",
-       "['[UNK]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(encode_word(\"Hugging\"))\n",
-    "print(encode_word(\"HOgging\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize(text):\n",
-    "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
-    "    encoded_words = [encode_word(word) for word in pre_tokenized_text]\n",
-    "    return sum(encoded_words, [])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Th', '##i', '##s', 'is', 'th', '##e', 'Hugg', '##i', '##n', '##g', 'Fac', '##e', 'c', '##o', '##u', '##r', '##s',\n",
-       " '##e', '[UNK]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenize(\"This is the Hugging Face course!\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "WordPiece tokenization",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section7.ipynb b/course/vi/chapter6/section7.ipynb
deleted file mode 100644
index 38dc8502..00000000
--- a/course/vi/chapter6/section7.ipynb
+++ /dev/null
@@ -1,318 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Unigram tokenization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face Course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"xlnet-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "word_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('▁t', 7), ('is', 5), ('er', 5), ('▁a', 5), ('▁to', 4), ('to', 4), ('en', 4), ('▁T', 3), ('▁Th', 3), ('▁Thi', 3)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "char_freqs = defaultdict(int)\n",
-    "subwords_freqs = defaultdict(int)\n",
-    "for word, freq in word_freqs.items():\n",
-    "    for i in range(len(word)):\n",
-    "        char_freqs[word[i]] += freq\n",
-    "        # Lặp qua các từ con có độ dài >= 2\n",
-    "        for j in range(i + 2, len(word) + 1):\n",
-    "            subwords_freqs[word[i:j]] += freq\n",
-    "\n",
-    "# Sắp xếp các từ con theo tần suất\n",
-    "sorted_subwords = sorted(subwords_freqs.items(), key=lambda x: x[1], reverse=True)\n",
-    "sorted_subwords[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "token_freqs = list(char_freqs.items()) + sorted_subwords[: 300 - len(char_freqs)]\n",
-    "token_freqs = {token: freq for token, freq in token_freqs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from math import log\n",
-    "\n",
-    "total_sum = sum([freq for token, freq in token_freqs.items()])\n",
-    "model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_word(word, model):\n",
-    "    best_segmentations = [{\"start\": 0, \"score\": 1}] + [\n",
-    "        {\"start\": None, \"score\": None} for _ in range(len(word))\n",
-    "    ]\n",
-    "    for start_idx in range(len(word)):\n",
-    "        # Nó nên được lấp đầy bởi các bước phía trước của vòng lặp\n",
-    "        best_score_at_start = best_segmentations[start_idx][\"score\"]\n",
-    "        for end_idx in range(start_idx + 1, len(word) + 1):\n",
-    "            token = word[start_idx:end_idx]\n",
-    "            if token in model and best_score_at_start is not None:\n",
-    "                score = model[token] + best_score_at_start\n",
-    "                # Nếu chúng ta tìm thấy một phân đoạn kết thúc tốt hơn tại end_idx, chúng ta cập nhật\n",
-    "                if (\n",
-    "                    best_segmentations[end_idx][\"score\"] is None\n",
-    "                    or best_segmentations[end_idx][\"score\"] > score\n",
-    "                ):\n",
-    "                    best_segmentations[end_idx] = {\"start\": start_idx, \"score\": score}\n",
-    "\n",
-    "    segmentation = best_segmentations[-1]\n",
-    "    if segmentation[\"score\"] is None:\n",
-    "        # Ta đã không tìm thấy tokenize của từ -> không xác định\n",
-    "        return [\"<unk>\"], None\n",
-    "\n",
-    "    score = segmentation[\"score\"]\n",
-    "    start = segmentation[\"start\"]\n",
-    "    end = len(word)\n",
-    "    tokens = []\n",
-    "    while start != 0:\n",
-    "        tokens.insert(0, word[start:end])\n",
-    "        next_start = best_segmentations[start][\"start\"]\n",
-    "        end = start\n",
-    "        start = next_start\n",
-    "    tokens.insert(0, word[start:end])\n",
-    "    return tokens, score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(['H', 'o', 'p', 'e', 'f', 'u', 'll', 'y'], 41.5157494601402)\n",
-       "(['This'], 6.288267030694535)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(encode_word(\"Hopefully\", model))\n",
-    "print(encode_word(\"This\", model))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_loss(model):\n",
-    "    loss = 0\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        _, word_loss = encode_word(word, model)\n",
-    "        loss += freq * word_loss\n",
-    "    return loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "413.10377642940875"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_loss(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "\n",
-    "\n",
-    "def compute_scores(model):\n",
-    "    scores = {}\n",
-    "    model_loss = compute_loss(model)\n",
-    "    for token, score in model.items():\n",
-    "        # Ta luôn giữ độ dài các token bằng 1\n",
-    "        if len(token) == 1:\n",
-    "            continue\n",
-    "        model_without_token = copy.deepcopy(model)\n",
-    "        _ = model_without_token.pop(token)\n",
-    "        scores[token] = compute_loss(model_without_token) - model_loss\n",
-    "    return scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.376412403623874\n",
-       "0.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = compute_scores(model)\n",
-    "print(scores[\"ll\"])\n",
-    "print(scores[\"his\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "percent_to_remove = 0.1\n",
-    "while len(model) > 100:\n",
-    "    scores = compute_scores(model)\n",
-    "    sorted_scores = sorted(scores.items(), key=lambda x: x[1])\n",
-    "    # Loại token percent_to_remove với điểm thấp nhất.\n",
-    "    for i in range(int(len(model) * percent_to_remove)):\n",
-    "        _ = token_freqs.pop(sorted_scores[i][0])\n",
-    "    total_sum = sum([freq for token, freq in token_freqs.items()])\n",
-    "    model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁This', '▁is', '▁the', '▁Hugging', '▁Face', '▁', 'c', 'ou', 'r', 's', 'e', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(text, model):\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in words_with_offsets]\n",
-    "    encoded_words = [encode_word(word, model)[0] for word in pre_tokenized_text]\n",
-    "    return sum(encoded_words, [])\n",
-    "\n",
-    "\n",
-    "tokenize(\"This is the Hugging Face course.\", model)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Unigram tokenization",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter6/section8.ipynb b/course/vi/chapter6/section8.ipynb
deleted file mode 100644
index 95ed31ea..00000000
--- a/course/vi/chapter6/section8.ipynb
+++ /dev/null
@@ -1,779 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xây dựng từng khối tokenizer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"wikitext\", name=\"wikitext-2-raw-v1\", split=\"train\")\n",
-    "\n",
-    "\n",
-    "def get_training_corpus():\n",
-    "    for i in range(0, len(dataset), 1000):\n",
-    "        yield dataset[i : i + 1000][\"text\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"wikitext-2.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "    for i in range(len(dataset)):\n",
-    "        f.write(dataset[i][\"text\"] + \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tokenizers import (\n",
-    "    decoders,\n",
-    "    models,\n",
-    "    normalizers,\n",
-    "    pre_tokenizers,\n",
-    "    processors,\n",
-    "    trainers,\n",
-    "    Tokenizer,\n",
-    ")\n",
-    "\n",
-    "tokenizer = Tokenizer(models.WordPiece(unk_token=\"[UNK]\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.normalizer = normalizers.Sequence(\n",
-    "    [normalizers.NFD(), normalizers.Lowercase(), normalizers.StripAccents()]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "hello how are u?"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'\", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),\n",
-       " ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(\"Let's\", (0, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre-tokenizer.', (14, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pre_tokenizer = pre_tokenizers.WhitespaceSplit()\n",
-    "pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'\", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),\n",
-       " ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pre_tokenizer = pre_tokenizers.Sequence(\n",
-    "    [pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Punctuation()]\n",
-    ")\n",
-    "pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "special_tokens = [\"[UNK]\", \"[PAD]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"]\n",
-    "trainer = trainers.WordPieceTrainer(vocab_size=25000, special_tokens=special_tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.WordPiece(unk_token=\"[UNK]\")\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cls_token_id = tokenizer.token_to_id(\"[CLS]\")\n",
-    "sep_token_id = tokenizer.token_to_id(\"[SEP]\")\n",
-    "print(cls_token_id, sep_token_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.TemplateProcessing(\n",
-    "    single=f\"[CLS]:0 $A:0 [SEP]:0\",\n",
-    "    pair=f\"[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1\",\n",
-    "    special_tokens=[(\"[CLS]\", cls_token_id), (\"[SEP]\", sep_token_id)],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '...', '[SEP]', 'on', 'a', 'pair', 'of', 'sentences', '.', '[SEP]']\n",
-       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer...\", \"on a pair of sentences.\")\n",
-    "print(encoding.tokens)\n",
-    "print(encoding.type_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.WordPiece(prefix=\"##\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"let's test this tokenizer... on a pair of sentences.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(encoding.ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save(\"tokenizer.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_tokenizer = Tokenizer.from_file(\"tokenizer.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    # tokenizer_file=\"tokenizer.json\", # Bạn có thể tải từ tệp tokenizer\n",
-    "    unk_token=\"[UNK]\",\n",
-    "    pad_token=\"[PAD]\",\n",
-    "    cls_token=\"[CLS]\",\n",
-    "    sep_token=\"[SEP]\",\n",
-    "    mask_token=\"[MASK]\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = BertTokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = Tokenizer(models.BPE())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'s\", (3, 5)), ('Ġtest', (5, 10)), ('Ġpre', (10, 14)), ('-', (14, 15)),\n",
-       " ('tokenization', (15, 27)), ('!', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test pre-tokenization!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = trainers.BpeTrainer(vocab_size=25000, special_tokens=[\"<|endoftext|>\"])\n",
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.BPE()\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['L', 'et', \"'\", 's', 'Ġtest', 'Ġthis', 'Ġto', 'ken', 'izer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' test'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"Let's test this tokenizer.\"\n",
-    "encoding = tokenizer.encode(sentence)\n",
-    "start, end = encoding.offsets[4]\n",
-    "sentence[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.ByteLevel()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Let's test this tokenizer.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(encoding.ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    bos_token=\"<|endoftext|>\",\n",
-    "    eos_token=\"<|endoftext|>\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT2TokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = Tokenizer(models.Unigram())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tokenizers import Regex\n",
-    "\n",
-    "tokenizer.normalizer = normalizers.Sequence(\n",
-    "    [\n",
-    "        normalizers.Replace(\"``\", '\"'),\n",
-    "        normalizers.Replace(\"''\", '\"'),\n",
-    "        normalizers.NFKD(),\n",
-    "        normalizers.StripAccents(),\n",
-    "        normalizers.Replace(Regex(\" {2,}\"), \" \"),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(\"▁Let's\", (0, 5)), ('▁test', (5, 10)), ('▁the', (10, 14)), ('▁pre-tokenizer!', (14, 29))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test the pre-tokenizer!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "special_tokens = [\"<cls>\", \"<sep>\", \"<unk>\", \"<pad>\", \"<mask>\", \"<s>\", \"</s>\"]\n",
-    "trainer = trainers.UnigramTrainer(\n",
-    "    vocab_size=25000, special_tokens=special_tokens, unk_token=\"<unk>\"\n",
-    ")\n",
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.Unigram()\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Let', \"'\", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0 1"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cls_token_id = tokenizer.token_to_id(\"<cls>\")\n",
-    "sep_token_id = tokenizer.token_to_id(\"<sep>\")\n",
-    "print(cls_token_id, sep_token_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.TemplateProcessing(\n",
-    "    single=\"$A:0 <sep>:0 <cls>:2\",\n",
-    "    pair=\"$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2\",\n",
-    "    special_tokens=[(\"<sep>\", sep_token_id), (\"<cls>\", cls_token_id)],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Let', \"'\", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.', '.', '.', '<sep>', '▁', 'on', '▁', 'a', '▁pair', \n",
-       "  '▁of', '▁sentence', 's', '!', '<sep>', '<cls>']\n",
-       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer...\", \"on a pair of sentences!\")\n",
-    "print(encoding.tokens)\n",
-    "print(encoding.type_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.Metaspace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    bos_token=\"<s>\",\n",
-    "    eos_token=\"</s>\",\n",
-    "    unk_token=\"<unk>\",\n",
-    "    pad_token=\"<pad>\",\n",
-    "    cls_token=\"<cls>\",\n",
-    "    sep_token=\"<sep>\",\n",
-    "    mask_token=\"<mask>\",\n",
-    "    padding_side=\"left\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import XLNetTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = XLNetTokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Xây dựng từng khối tokenizer",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section2_pt.ipynb b/course/vi/chapter7/section2_pt.ipynb
deleted file mode 100644
index 9e6084b1..00000000
--- a/course/vi/chapter7/section2_pt.ipynb
+++ /dev/null
@@ -1,891 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Phân loại token (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"conll2003\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 14041\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3250\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3453\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"tokens\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"ner_tags\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], names_file=None, id=None), length=-1, id=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n",
-    "ner_feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label_names = ner_feature.feature.names\n",
-    "label_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'EU    rejects German call to boycott British lamb .'\n",
-       "'B-ORG O       B-MISC O    O  O       B-MISC  O    O'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "words = raw_datasets[\"train\"][0][\"tokens\"]\n",
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "line1 = \"\"\n",
-    "line2 = \"\"\n",
-    "for word, label in zip(words, labels):\n",
-    "    full_label = label_names[label]\n",
-    "    max_length = max(len(word), len(full_label))\n",
-    "    line1 += word + \" \" * (max_length - len(word) + 1)\n",
-    "    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
-    "\n",
-    "print(line1)\n",
-    "print(line2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'la', '##mb', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
-    "inputs.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def align_labels_with_tokens(labels, word_ids):\n",
-    "    new_labels = []\n",
-    "    current_word = None\n",
-    "    for word_id in word_ids:\n",
-    "        if word_id != current_word:\n",
-    "            # Bắt đầu một từ mới!\n",
-    "            current_word = word_id\n",
-    "            label = -100 if word_id is None else labels[word_id]\n",
-    "            new_labels.append(label)\n",
-    "        elif word_id is None:\n",
-    "            # Token đặc biệt\n",
-    "            new_labels.append(-100)\n",
-    "        else:\n",
-    "            # Từ giống với token trước đó\n",
-    "            label = labels[word_id]\n",
-    "            # Nếu nhãn là B-XXX, ta đổi sang I-XXX\n",
-    "            if label % 2 == 1:\n",
-    "                label += 1\n",
-    "            new_labels.append(label)\n",
-    "\n",
-    "    return new_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n",
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "word_ids = inputs.word_ids()\n",
-    "print(labels)\n",
-    "print(align_labels_with_tokens(labels, word_ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_align_labels(examples):\n",
-    "    tokenized_inputs = tokenizer(\n",
-    "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
-    "    )\n",
-    "    all_labels = examples[\"ner_tags\"]\n",
-    "    new_labels = []\n",
-    "    for i, labels in enumerate(all_labels):\n",
-    "        word_ids = tokenized_inputs.word_ids(i)\n",
-    "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
-    "\n",
-    "    tokenized_inputs[\"labels\"] = new_labels\n",
-    "    return tokenized_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize_and_align_labels,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForTokenClassification\n",
-    "\n",
-    "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
-       "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
-       "[-100, 1, 2, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(2):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install seqeval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"seqeval\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "labels = [label_names[i] for i in labels]\n",
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MISC': {'precision': 1.0, 'recall': 0.5, 'f1': 0.67, 'number': 2},\n",
-       " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
-       " 'overall_precision': 1.0,\n",
-       " 'overall_recall': 0.67,\n",
-       " 'overall_f1': 0.8,\n",
-       " 'overall_accuracy': 0.89}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = labels.copy()\n",
-    "predictions[2] = \"O\"\n",
-    "metric.compute(predictions=[predictions], references=[labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_preds):\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "\n",
-    "    # Xoá những chỉ mục bị ngó lơ (token đặc biệt) và chuyển chúng thành nhãn\n",
-    "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
-    "    true_predictions = [\n",
-    "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
-    "    return {\n",
-    "        \"precision\": all_metrics[\"overall_precision\"],\n",
-    "        \"recall\": all_metrics[\"overall_recall\"],\n",
-    "        \"f1\": all_metrics[\"overall_f1\"],\n",
-    "        \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id2label = {i: label for i, label in enumerate(label_names)}\n",
-    "label2id = {v: k for k, v in id2label.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForTokenClassification\n",
-    "\n",
-    "model = AutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    \"bert-finetuned-ner\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/bert-finetuned-ner/commit/26ab21e5b1568f9afeccdaed2d8715f571d786ed'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/bert-finetuned-ner-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"bert-finetuned-ner-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"bert-finetuned-ner-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess(predictions, labels):\n",
-    "    predictions = predictions.detach().cpu().clone().numpy()\n",
-    "    labels = labels.detach().cpu().clone().numpy()\n",
-    "\n",
-    "    # Loại bỏ các chỉ mục bị ngó lơ (các token đặc biệt) và chuyển chúng thành nhãn\n",
-    "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
-    "    true_predictions = [\n",
-    "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "    return true_labels, true_predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Huấn luyện\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Đánh giá\n",
-    "    model.eval()\n",
-    "    for batch in eval_dataloader:\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        predictions = outputs.logits.argmax(dim=-1)\n",
-    "        labels = batch[\"labels\"]\n",
-    "\n",
-    "        # Cần đệm các dự đoán và nhãn để tập hợp\n",
-    "        predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)\n",
-    "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
-    "\n",
-    "        predictions_gathered = accelerator.gather(predictions)\n",
-    "        labels_gathered = accelerator.gather(labels)\n",
-    "\n",
-    "        true_predictions, true_labels = postprocess(predictions_gathered, labels_gathered)\n",
-    "        metric.add_batch(predictions=true_predictions, references=true_labels)\n",
-    "\n",
-    "    results = metric.compute()\n",
-    "    print(\n",
-    "        f\"epoch {epoch}:\",\n",
-    "        {\n",
-    "            key: results[f\"overall_{key}\"]\n",
-    "            for key in [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n",
-    "        },\n",
-    "    )\n",
-    "\n",
-    "    # Lưu và tải\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accelerator.wait_for_everyone()\n",
-    "unwrapped_model = accelerator.unwrap_model(model)\n",
-    "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9988506, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.9647625, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.9986118, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Thay thế nó với checkpoint của ta\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-ner\"\n",
-    "token_classifier = pipeline(\n",
-    "    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",
-    ")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Phân loại token (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section2_tf.ipynb b/course/vi/chapter7/section2_tf.ipynb
deleted file mode 100644
index fbc2238c..00000000
--- a/course/vi/chapter7/section2_tf.ipynb
+++ /dev/null
@@ -1,707 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Phân loại token (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"conll2003\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 14041\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3250\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3453\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"tokens\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"ner_tags\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], names_file=None, id=None), length=-1, id=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n",
-    "ner_feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label_names = ner_feature.feature.names\n",
-    "label_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'EU    rejects German call to boycott British lamb .'\n",
-       "'B-ORG O       B-MISC O    O  O       B-MISC  O    O'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "words = raw_datasets[\"train\"][0][\"tokens\"]\n",
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "line1 = \"\"\n",
-    "line2 = \"\"\n",
-    "for word, label in zip(words, labels):\n",
-    "    full_label = label_names[label]\n",
-    "    max_length = max(len(word), len(full_label))\n",
-    "    line1 += word + \" \" * (max_length - len(word) + 1)\n",
-    "    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
-    "\n",
-    "print(line1)\n",
-    "print(line2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'la', '##mb', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
-    "inputs.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def align_labels_with_tokens(labels, word_ids):\n",
-    "    new_labels = []\n",
-    "    current_word = None\n",
-    "    for word_id in word_ids:\n",
-    "        if word_id != current_word:\n",
-    "            # Bắt đầu một từ mới!\n",
-    "            current_word = word_id\n",
-    "            label = -100 if word_id is None else labels[word_id]\n",
-    "            new_labels.append(label)\n",
-    "        elif word_id is None:\n",
-    "            # Token đặc biệt\n",
-    "            new_labels.append(-100)\n",
-    "        else:\n",
-    "            # Từ giống với token trước đó\n",
-    "            label = labels[word_id]\n",
-    "            # Nếu nhãn là B-XXX, ta đổi sang I-XXX\n",
-    "            if label % 2 == 1:\n",
-    "                label += 1\n",
-    "            new_labels.append(label)\n",
-    "\n",
-    "    return new_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n",
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "word_ids = inputs.word_ids()\n",
-    "print(labels)\n",
-    "print(align_labels_with_tokens(labels, word_ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_align_labels(examples):\n",
-    "    tokenized_inputs = tokenizer(\n",
-    "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
-    "    )\n",
-    "    all_labels = examples[\"ner_tags\"]\n",
-    "    new_labels = []\n",
-    "    for i, labels in enumerate(all_labels):\n",
-    "        word_ids = tokenized_inputs.word_ids(i)\n",
-    "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
-    "\n",
-    "    tokenized_inputs[\"labels\"] = new_labels\n",
-    "    return tokenized_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize_and_align_labels,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForTokenClassification\n",
-    "\n",
-    "data_collator = DataCollatorForTokenClassification(\n",
-    "    tokenizer=tokenizer, return_tensors=\"tf\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
-       "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
-       "[-100, 1, 2, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(2):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=16,\n",
-    ")\n",
-    "\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id2label = {i: label for i, label in enumerate(label_names)}\n",
-    "label2id = {v: k for k, v in id2label.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForTokenClassification\n",
-    "\n",
-    "model = TFAutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Huấn luyện trong mixed-precision float16\n",
-    "# Bình luận dòng này nếu bạn đang sử dụng GPU nên không được hưởng lợi từ điều này\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n",
-    "\n",
-    "# Số bước huấn luyện là số lượng mẫu trong tập dữ liệu, chia cho kích thước lô sau đó nhân\n",
-    "# với số epoch. Lưu ý rằng tf_train_dataset ở đây là lô tf.data.Dataset,\n",
-    "# không phải Hugging Face Dataset gốc, nên len() của nó vốn là num_samples // batch_size.\n",
-    "num_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"bert-finetuned-ner\", tokenizer=tokenizer)\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_eval_dataset,\n",
-    "    callbacks=[callback],\n",
-    "    epochs=num_epochs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install seqeval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"seqeval\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "labels = [label_names[i] for i in labels]\n",
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MISC': {'precision': 1.0, 'recall': 0.5, 'f1': 0.67, 'number': 2},\n",
-       " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
-       " 'overall_precision': 1.0,\n",
-       " 'overall_recall': 0.67,\n",
-       " 'overall_f1': 0.8,\n",
-       " 'overall_accuracy': 0.89}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = labels.copy()\n",
-    "predictions[2] = \"O\"\n",
-    "metric.compute(predictions=[predictions], references=[labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'LOC': {'precision': 0.91, 'recall': 0.92, 'f1': 0.91, 'number': 1668},\n",
-       " 'MISC': {'precision': 0.70, 'recall': 0.79, 'f1': 0.74, 'number': 702},\n",
-       " 'ORG': {'precision': 0.85, 'recall': 0.90, 'f1': 0.88, 'number': 1661},\n",
-       " 'PER': {'precision': 0.95, 'recall': 0.95, 'f1': 0.95, 'number': 1617},\n",
-       " 'overall_precision': 0.87,\n",
-       " 'overall_recall': 0.91,\n",
-       " 'overall_f1': 0.89,\n",
-       " 'overall_accuracy': 0.97}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "all_predictions = []\n",
-    "all_labels = []\n",
-    "for batch in tf_eval_dataset:\n",
-    "    logits = model.predict(batch)[\"logits\"]\n",
-    "    labels = batch[\"labels\"]\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    for prediction, label in zip(predictions, labels):\n",
-    "        for predicted_idx, label_idx in zip(prediction, label):\n",
-    "            if label_idx == -100:\n",
-    "                continue\n",
-    "            all_predictions.append(label_names[predicted_idx])\n",
-    "            all_labels.append(label_names[label_idx])\n",
-    "metric.compute(predictions=[all_predictions], references=[all_labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9988506, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.9647625, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.9986118, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Thay thế nó với checkpoint của ta\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-ner\"\n",
-    "token_classifier = pipeline(\n",
-    "    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",
-    ")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Phân loại token (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section3_pt.ipynb b/course/vi/chapter7/section3_pt.ipynb
deleted file mode 100644
index 0184c44c..00000000
--- a/course/vi/chapter7/section3_pt.ipynb
+++ /dev/null
@@ -1,957 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tinh chỉnh một mô hình ngôn ngữ bị ẩn đi (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> DistilBERT number of parameters: 67M'\n",
-       "'>>> BERT number of parameters: 110M'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "distilbert_num_parameters = model.num_parameters() / 1_000_000\n",
-    "print(f\"'>>> DistilBERT number of parameters: {round(distilbert_num_parameters)}M'\")\n",
-    "print(f\"'>>> BERT number of parameters: 110M'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a great [MASK].\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> This is a great deal.'\n",
-       "'>>> This is a great success.'\n",
-       "'>>> This is a great adventure.'\n",
-       "'>>> This is a great idea.'\n",
-       "'>>> This is a great feat.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "token_logits = model(**inputs).logits\n",
-    "# Tìm vị trí [MASK] và trích xuất logit\n",
-    "mask_token_index = torch.where(inputs[\"input_ids\"] == tokenizer.mask_token_id)[1]\n",
-    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
-    "# Chọn ứng viên cho [MASK] với logit cao nhất\n",
-    "top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()\n",
-    "\n",
-    "for token in top_5_tokens:\n",
-    "    print(f\"'>>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "imdb_dataset = load_dataset(\"imdb\")\n",
-    "imdb_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "'>>> Review: This is your typical Priyadarshan movie--a bunch of loony characters out on some silly mission. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, \"kodokoo\" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. Don\\'t get me wrong; as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Now, the hilarity of his films is fading as he is using the same formula over and over again.<br /><br />Songs are good. Tanushree Datta looks awesome. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Kunal Khemu is OK, and Sharman Joshi is the best.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam\" Peckinpah directed the film. Somehow, his direction is not enough. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.<br /><br />The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. Watch the great Peckinpah films. Skip this mess.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: I saw this movie at the theaters when I was about 6 or 7 years old. I loved it then, and have recently come to own a VHS version. <br /><br />My 4 and 6 year old children love this movie and have been asking again and again to watch it. <br /><br />I have enjoyed watching it again too. Though I have to admit it is not as good on a little TV.<br /><br />I do not have older children so I do not know what they would think of it. <br /><br />The songs are very cute. My daughter keeps singing them over and over.<br /><br />Hope this helps.'\n",
-       "'>>> Label: 1'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
-    "\n",
-    "for row in sample:\n",
-    "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
-    "    print(f\"'>>> Label: {row['label']}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize_function(examples):\n",
-    "    result = tokenizer(examples[\"text\"])\n",
-    "    if tokenizer.is_fast:\n",
-    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Dùng batched=True để kích hoạt đa luồng nhanh!\n",
-    "tokenized_datasets = imdb_dataset.map(\n",
-    "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "512"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.model_max_length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 128"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review 0 length: 200'\n",
-       "'>>> Review 1 length: 559'\n",
-       "'>>> Review 2 length: 192'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Tạo ra một danh sách các danh sách cho từng đặc trưng\n",
-    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
-    "\n",
-    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
-    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Concatenated reviews length: 951'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatenated_examples = {\n",
-    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
-    "}\n",
-    "total_length = len(concatenated_examples[\"input_ids\"])\n",
-    "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 55'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chunks = {\n",
-    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "    for k, t in concatenated_examples.items()\n",
-    "}\n",
-    "\n",
-    "for chunk in chunks[\"input_ids\"]:\n",
-    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def group_texts(examples):\n",
-    "    # Nối tất cả các văn bản\n",
-    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
-    "    # Tính độ dài của các văn bản được nối\n",
-    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
-    "    # Chúng tôi bỏ đoạn cuối cùng nếu nó nhỏ hơn chunk_size\n",
-    "    total_length = (total_length // chunk_size) * chunk_size\n",
-    "    # Chia phần theo max_len\n",
-    "    result = {\n",
-    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "        for k, t in concatenated_examples.items()\n",
-    "    }\n",
-    "    # Tạo cột nhãn mới\n",
-    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 61289\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 59905\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 122963\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
-    "lm_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\".... at.......... high. a classic line : inspector : i'm here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn't! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "for sample in samples:\n",
-    "    _ = sample.pop(\"word_ids\")\n",
-    "\n",
-    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "import numpy as np\n",
-    "\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "wwm_probability = 0.2\n",
-    "\n",
-    "\n",
-    "def whole_word_masking_data_collator(features):\n",
-    "    for feature in features:\n",
-    "        word_ids = feature.pop(\"word_ids\")\n",
-    "\n",
-    "        # Tạo ra ánh xạ giữa các từ và chỉ mục token tương ứng\n",
-    "        mapping = collections.defaultdict(list)\n",
-    "        current_word_index = -1\n",
-    "        current_word = None\n",
-    "        for idx, word_id in enumerate(word_ids):\n",
-    "            if word_id is not None:\n",
-    "                if word_id != current_word:\n",
-    "                    current_word = word_id\n",
-    "                    current_word_index += 1\n",
-    "                mapping[current_word_index].append(idx)\n",
-    "\n",
-    "        # Che ngẫu nhiền từ\n",
-    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
-    "        input_ids = feature[\"input_ids\"]\n",
-    "        labels = feature[\"labels\"]\n",
-    "        new_labels = [-100] * len(labels)\n",
-    "        for word_id in np.where(mask)[0]:\n",
-    "            word_id = word_id.item()\n",
-    "            for idx in mapping[word_id]:\n",
-    "                new_labels[idx] = labels[idx]\n",
-    "                input_ids[idx] = tokenizer.mask_token_id\n",
-    "        feature[\"labels\"] = new_labels\n",
-    "\n",
-    "    return default_data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> [CLS] bromwell high is a cartoon comedy [MASK] it ran at the same time as some other programs about school life, such as \" teachers \". my 35 years in the teaching profession lead me to believe that bromwell high\\'s satire is much closer to reality than is \" teachers \". the scramble to survive financially, the insightful students who can see right through their pathetic teachers\\'pomp, the pettiness of the whole situation, all remind me of the schools i knew and their students. when i saw the episode in which a student repeatedly tried to burn down the school, i immediately recalled.....'\n",
-       "\n",
-       "'>>> .... [MASK] [MASK] [MASK] [MASK]....... high. a classic line : inspector : i\\'m here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn\\'t! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "batch = whole_word_masking_data_collator(samples)\n",
-    "\n",
-    "for chunk in batch[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 10000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 1000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_size = 10_000\n",
-    "test_size = int(0.1 * train_size)\n",
-    "\n",
-    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
-    "    train_size=train_size, test_size=test_size, seed=42\n",
-    ")\n",
-    "downsampled_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "batch_size = 64\n",
-    "# In ra sự mất mát khi huấn luyện ở mỗi epoch\n",
-    "logging_steps = len(downsampled_dataset[\"train\"]) // batch_size\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=f\"{model_name}-finetuned-imdb\",\n",
-    "    overwrite_output_dir=True,\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    weight_decay=0.01,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    push_to_hub=True,\n",
-    "    fp16=True,\n",
-    "    logging_steps=logging_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=training_args,\n",
-    "    train_dataset=downsampled_dataset[\"train\"],\n",
-    "    eval_dataset=downsampled_dataset[\"test\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 21.75"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 11.32"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def insert_random_mask(batch):\n",
-    "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
-    "    masked_inputs = data_collator(features)\n",
-    "    # Tạo ra một cột \"masked\" mới cho mỗi cột trong bộ dữ liệu\n",
-    "    return {\"masked_\" + k: v.numpy() for k, v in masked_inputs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downsampled_dataset = downsampled_dataset.remove_columns([\"word_ids\"])\n",
-    "eval_dataset = downsampled_dataset[\"test\"].map(\n",
-    "    insert_random_mask,\n",
-    "    batched=True,\n",
-    "    remove_columns=downsampled_dataset[\"test\"].column_names,\n",
-    ")\n",
-    "eval_dataset = eval_dataset.rename_columns(\n",
-    "    {\n",
-    "        \"masked_input_ids\": \"input_ids\",\n",
-    "        \"masked_attention_mask\": \"attention_mask\",\n",
-    "        \"masked_labels\": \"labels\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "batch_size = 64\n",
-    "train_dataloader = DataLoader(\n",
-    "    downsampled_dataset[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    batch_size=batch_size,\n",
-    "    collate_fn=data_collator,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    eval_dataset, batch_size=batch_size, collate_fn=default_data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'lewtun/distilbert-base-uncased-finetuned-imdb-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import get_full_repo_name\n",
-    "\n",
-    "model_name = \"distilbert-base-uncased-finetuned-imdb-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "output_dir = model_name\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Epoch 0: Perplexity: 11.397545307900472\n",
-       ">>> Epoch 1: Perplexity: 10.904909330983092\n",
-       ">>> Epoch 2: Perplexity: 10.729503505340409"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "import math\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Huấn luyện\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Đánh giá\n",
-    "    model.eval()\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        loss = outputs.loss\n",
-    "        losses.append(accelerator.gather(loss.repeat(batch_size)))\n",
-    "\n",
-    "    losses = torch.cat(losses)\n",
-    "    losses = losses[: len(eval_dataset)]\n",
-    "    try:\n",
-    "        perplexity = math.exp(torch.mean(losses))\n",
-    "    except OverflowError:\n",
-    "        perplexity = float(\"inf\")\n",
-    "\n",
-    "    print(f\">>> Epoch {epoch}: Perplexity: {perplexity}\")\n",
-    "\n",
-    "    # Lưu và tải\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "mask_filler = pipeline(\n",
-    "    \"fill-mask\", model=\"huggingface-course/distilbert-base-uncased-finetuned-imdb\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> this is a great movie.'\n",
-       "'>>> this is a great film.'\n",
-       "'>>> this is a great story.'\n",
-       "'>>> this is a great movies.'\n",
-       "'>>> this is a great character.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds = mask_filler(text)\n",
-    "\n",
-    "for pred in preds:\n",
-    "    print(f\">>> {pred['sequence']}\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tinh chỉnh một mô hình ngôn ngữ bị ẩn đi (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section3_tf.ipynb b/course/vi/chapter7/section3_tf.ipynb
deleted file mode 100644
index a7816642..00000000
--- a/course/vi/chapter7/section3_tf.ipynb
+++ /dev/null
@@ -1,759 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tinh chỉnh một mô hình ngôn ngữ bị ẩn đi (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Model: \"tf_distil_bert_for_masked_lm\"\n",
-       "_________________________________________________________________\n",
-       "Layer (type)                 Output Shape              Param #   \n",
-       "=================================================================\n",
-       "distilbert (TFDistilBertMain multiple                  66362880  \n",
-       "_________________________________________________________________\n",
-       "vocab_transform (Dense)      multiple                  590592    \n",
-       "_________________________________________________________________\n",
-       "vocab_layer_norm (LayerNorma multiple                  1536      \n",
-       "_________________________________________________________________\n",
-       "vocab_projector (TFDistilBer multiple                  23866170  \n",
-       "=================================================================\n",
-       "Total params: 66,985,530\n",
-       "Trainable params: 66,985,530\n",
-       "Non-trainable params: 0\n",
-       "_________________________________________________________________"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model(model.dummy_inputs)  # Xây dựng mô hình\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a great [MASK].\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> This is a great deal.'\n",
-       "'>>> This is a great success.'\n",
-       "'>>> This is a great adventure.'\n",
-       "'>>> This is a great idea.'\n",
-       "'>>> This is a great feat.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"np\")\n",
-    "token_logits = model(**inputs).logits\n",
-    "# Tìm vị trí [MASK] và trích xuất logit\n",
-    "mask_token_index = np.argwhere(inputs[\"input_ids\"] == tokenizer.mask_token_id)[0, 1]\n",
-    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
-    "# PChọn ứng viên cho [MASK] với logit cao nhất\n",
-    "# Chúng ta phủ định mảng trước argsort để lấy logits lớn nhất chứ không phải nhỏ nhất\n",
-    "top_5_tokens = np.argsort(-mask_token_logits)[:5].tolist()\n",
-    "\n",
-    "for token in top_5_tokens:\n",
-    "    print(f\">>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "imdb_dataset = load_dataset(\"imdb\")\n",
-    "imdb_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "'>>> Review: This is your typical Priyadarshan movie--a bunch of loony characters out on some silly mission. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, \"kodokoo\" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. Don\\'t get me wrong; as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Now, the hilarity of his films is fading as he is using the same formula over and over again.<br /><br />Songs are good. Tanushree Datta looks awesome. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Kunal Khemu is OK, and Sharman Joshi is the best.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam\" Peckinpah directed the film. Somehow, his direction is not enough. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.<br /><br />The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. Watch the great Peckinpah films. Skip this mess.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: I saw this movie at the theaters when I was about 6 or 7 years old. I loved it then, and have recently come to own a VHS version. <br /><br />My 4 and 6 year old children love this movie and have been asking again and again to watch it. <br /><br />I have enjoyed watching it again too. Though I have to admit it is not as good on a little TV.<br /><br />I do not have older children so I do not know what they would think of it. <br /><br />The songs are very cute. My daughter keeps singing them over and over.<br /><br />Hope this helps.'\n",
-       "'>>> Label: 1'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
-    "\n",
-    "for row in sample:\n",
-    "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
-    "    print(f\"'>>> Label: {row['label']}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize_function(examples):\n",
-    "    result = tokenizer(examples[\"text\"])\n",
-    "    if tokenizer.is_fast:\n",
-    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Dùng batched=True để kích hoạt đa luồng nhanh!\n",
-    "tokenized_datasets = imdb_dataset.map(\n",
-    "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "512"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.model_max_length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 128"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review 0 length: 200'\n",
-       "'>>> Review 1 length: 559'\n",
-       "'>>> Review 2 length: 192'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Tạo ra một danh sách các danh sách cho từng đặc trưng\n",
-    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
-    "\n",
-    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
-    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Concatenated reviews length: 951'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatenated_examples = {\n",
-    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
-    "}\n",
-    "total_length = len(concatenated_examples[\"input_ids\"])\n",
-    "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 55'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chunks = {\n",
-    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "    for k, t in concatenated_examples.items()\n",
-    "}\n",
-    "\n",
-    "for chunk in chunks[\"input_ids\"]:\n",
-    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def group_texts(examples):\n",
-    "    # Nối tất cả các văn bản\n",
-    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
-    "    # Tính độ dài của các văn bản được nối\n",
-    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
-    "    # Chúng tôi bỏ đoạn cuối cùng nếu nó nhỏ hơn chunk_size\n",
-    "    total_length = (total_length // chunk_size) * chunk_size\n",
-    "    # Chia phần theo max_len\n",
-    "    result = {\n",
-    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "        for k, t in concatenated_examples.items()\n",
-    "    }\n",
-    "    # Tạo cột nhãn mới\n",
-    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 61289\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 59905\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 122963\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
-    "lm_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\".... at.......... high. a classic line : inspector : i'm here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn't! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "for sample in samples:\n",
-    "    _ = sample.pop(\"word_ids\")\n",
-    "\n",
-    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "import numpy as np\n",
-    "\n",
-    "from transformers.data.data_collator import tf_default_data_collator\n",
-    "\n",
-    "wwm_probability = 0.2\n",
-    "\n",
-    "\n",
-    "def whole_word_masking_data_collator(features):\n",
-    "    for feature in features:\n",
-    "        word_ids = feature.pop(\"word_ids\")\n",
-    "\n",
-    "        # Tạo ra ánh xạ giữa các từ và chỉ mục token tương ứng\n",
-    "        mapping = collections.defaultdict(list)\n",
-    "        current_word_index = -1\n",
-    "        current_word = None\n",
-    "        for idx, word_id in enumerate(word_ids):\n",
-    "            if word_id is not None:\n",
-    "                if word_id != current_word:\n",
-    "                    current_word = word_id\n",
-    "                    current_word_index += 1\n",
-    "                mapping[current_word_index].append(idx)\n",
-    "\n",
-    "        # Che ngẫu nhiền từ\n",
-    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
-    "        input_ids = feature[\"input_ids\"]\n",
-    "        labels = feature[\"labels\"]\n",
-    "        new_labels = [-100] * len(labels)\n",
-    "        for word_id in np.where(mask)[0]:\n",
-    "            word_id = word_id.item()\n",
-    "            for idx in mapping[word_id]:\n",
-    "                new_labels[idx] = labels[idx]\n",
-    "                input_ids[idx] = tokenizer.mask_token_id\n",
-    "        feature[\"labels\"] = new_labels\n",
-    "\n",
-    "    return tf_default_data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> [CLS] bromwell high is a cartoon comedy [MASK] it ran at the same time as some other programs about school life, such as \" teachers \". my 35 years in the teaching profession lead me to believe that bromwell high\\'s satire is much closer to reality than is \" teachers \". the scramble to survive financially, the insightful students who can see right through their pathetic teachers\\'pomp, the pettiness of the whole situation, all remind me of the schools i knew and their students. when i saw the episode in which a student repeatedly tried to burn down the school, i immediately recalled.....'\n",
-       "\n",
-       "'>>> .... [MASK] [MASK] [MASK] [MASK]....... high. a classic line : inspector : i\\'m here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn\\'t! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "batch = whole_word_masking_data_collator(samples)\n",
-    "\n",
-    "for chunk in batch[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 10000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 1000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_size = 10_000\n",
-    "test_size = int(0.1 * train_size)\n",
-    "\n",
-    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
-    "    train_size=train_size, test_size=test_size, seed=42\n",
-    ")\n",
-    "downsampled_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = downsampled_dataset[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "\n",
-    "tf_eval_dataset = downsampled_dataset[\"test\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=32,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "num_train_steps = len(tf_train_dataset)\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Huấn luyện trong mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=f\"{model_name}-finetuned-imdb\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 21.75"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "eval_loss = model.evaluate(tf_eval_dataset)\n",
-    "print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 11.32"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eval_loss = model.evaluate(tf_eval_dataset)\n",
-    "print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "mask_filler = pipeline(\n",
-    "    \"fill-mask\", model=\"huggingface-course/distilbert-base-uncased-finetuned-imdb\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> this is a great movie.'\n",
-       "'>>> this is a great film.'\n",
-       "'>>> this is a great story.'\n",
-       "'>>> this is a great movies.'\n",
-       "'>>> this is a great character.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds = mask_filler(text)\n",
-    "\n",
-    "for pred in preds:\n",
-    "    print(f\">>> {pred['sequence']}\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tinh chỉnh một mô hình ngôn ngữ bị ẩn đi (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section4_pt.ipynb b/course/vi/chapter7/section4_pt.ipynb
deleted file mode 100644
index 9da8f2d8..00000000
--- a/course/vi/chapter7/section4_pt.ipynb
+++ /dev/null
@@ -1,963 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dịch máy (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 210173\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 189155\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 21018\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
-    "split_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Default to expanded threads',\n",
-       " 'fr': 'Par défaut, développer les fils de discussion'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][1][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut pour les threads élargis'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Unable to import %1 using the OFX importer plugin. This file is not the correct format.',\n",
-       " 'fr': \"Impossible d'importer %1 en utilisant le module d'extension d'importation OFX. Ce fichier n'a pas un format correct.\"}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][172][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le plugin d'importateur OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
-    "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
-    "\n",
-    "inputs = tokenizer(en_sentence)\n",
-    "with tokenizer.as_target_tokenizer():\n",
-    "    targets = tokenizer(fr_sentence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Par', '▁dé', 'f', 'aut', ',', '▁dé', 've', 'lop', 'per', '▁les', '▁fil', 's', '▁de', '▁discussion', '</s>']\n",
-       "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong_targets = tokenizer(fr_sentence)\n",
-    "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
-    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 128\n",
-    "max_target_length = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
-    "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
-    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
-    "\n",
-    "    # Thiết lập tokenizer cho nhãn\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = split_datasets.map(\n",
-    "    preprocess_function,\n",
-    "    batched=True,\n",
-    "    remove_columns=split_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'input_ids', 'labels', 'decoder_input_ids'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
-    "batch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,  -100,\n",
-       "          -100,  -100,  -100,  -100,  -100,  -100],\n",
-       "        [ 1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,   817,\n",
-       "           550,  7032,  5821,  7907, 12649,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[59513,   577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,\n",
-       "         59513, 59513, 59513, 59513, 59513, 59513],\n",
-       "        [59513,  1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,\n",
-       "           817,   550,  7032,  5821,  7907, 12649]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"decoder_input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[577, 5891, 2, 3184, 16, 2542, 5, 1710, 0]\n",
-       "[1211, 3, 49, 9409, 1211, 3, 29140, 817, 3124, 817, 550, 7032, 5821, 7907, 12649, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(1, 3):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install sacrebleu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"sacrebleu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 46.750469682990165,\n",
-       " 'counts': [11, 6, 4, 3],\n",
-       " 'totals': [12, 11, 10, 9],\n",
-       " 'precisions': [91.67, 54.54, 40.0, 33.33],\n",
-       " 'bp': 0.9200444146293233,\n",
-       " 'sys_len': 12,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\n",
-    "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
-    "]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1.683602693167689,\n",
-       " 'counts': [1, 0, 0, 0],\n",
-       " 'totals': [4, 3, 2, 1],\n",
-       " 'precisions': [25.0, 16.67, 12.5, 12.5],\n",
-       " 'bp': 0.10539922456186433,\n",
-       " 'sys_len': 4,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This This This This\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0,\n",
-       " 'counts': [2, 1, 0, 0],\n",
-       " 'totals': [2, 1, 0, 0],\n",
-       " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
-       " 'bp': 0.004086771438464067,\n",
-       " 'sys_len': 2,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This plugin\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_preds):\n",
-    "    preds, labels = eval_preds\n",
-    "    # Trong trường hợp mô hình trả về nhiều hơn logit dự đoán\n",
-    "    if isinstance(preds, tuple):\n",
-    "        preds = preds[0]\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
-    "\n",
-    "    # Thay các gía trị -100 trong nhãn vì ta không giải mã chúng\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Thực một một xố hậu xủ lý đơn giản\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "\n",
-    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
-    "    return {\"bleu\": result[\"score\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainingArguments\n",
-    "\n",
-    "args = Seq2SeqTrainingArguments(\n",
-    "    f\"marian-finetuned-kde4-en-to-fr\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    per_device_train_batch_size=32,\n",
-    "    per_device_eval_batch_size=64,\n",
-    "    weight_decay=0.01,\n",
-    "    save_total_limit=3,\n",
-    "    num_train_epochs=3,\n",
-    "    predict_with_generate=True,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainer\n",
-    "\n",
-    "trainer = Seq2SeqTrainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 1.6964408159255981,\n",
-       " 'eval_bleu': 39.26865061007616,\n",
-       " 'eval_runtime': 965.8884,\n",
-       " 'eval_samples_per_second': 21.76,\n",
-       " 'eval_steps_per_second': 0.341}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 0.8558505773544312,\n",
-       " 'eval_bleu': 52.94161337775576,\n",
-       " 'eval_runtime': 714.2576,\n",
-       " 'eval_samples_per_second': 29.426,\n",
-       " 'eval_steps_per_second': 0.461,\n",
-       " 'epoch': 3.0}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/marian-finetuned-kde4-en-to-fr/commit/3601d621e3baae2bc63d3311452535f8f58f6ef3'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(tags=\"translation\", commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/marian-finetuned-kde4-en-to-fr-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess(predictions, labels):\n",
-    "    predictions = predictions.cpu().numpy()\n",
-    "    labels = labels.cpu().numpy()\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "\n",
-    "    # Thay -100 trong nhãn vì ta không thế giải mã chúng.\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Thực hiện một số hậu xử lý đơn giản\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "    return decoded_preds, decoded_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "epoch 0, BLEU score: 53.47\n",
-       "epoch 1, BLEU score: 54.24\n",
-       "epoch 2, BLEU score: 54.44"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Huấn luyện\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Đánh giá\n",
-    "    model.eval()\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            generated_tokens = accelerator.unwrap_model(model).generate(\n",
-    "                batch[\"input_ids\"],\n",
-    "                attention_mask=batch[\"attention_mask\"],\n",
-    "                max_length=128,\n",
-    "            )\n",
-    "        labels = batch[\"labels\"]\n",
-    "\n",
-    "        # Cần đệm dự đoán và nhãn để dễ gom lại\n",
-    "        generated_tokens = accelerator.pad_across_processes(\n",
-    "            generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n",
-    "        )\n",
-    "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
-    "\n",
-    "        predictions_gathered = accelerator.gather(generated_tokens)\n",
-    "        labels_gathered = accelerator.gather(labels)\n",
-    "\n",
-    "        decoded_preds, decoded_labels = postprocess(predictions_gathered, labels_gathered)\n",
-    "        metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
-    "\n",
-    "    results = metric.compute()\n",
-    "    print(f\"epoch {epoch}, BLEU score: {results['score']:.2f}\")\n",
-    "\n",
-    "    # Lưu và tải\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut, développer les fils de discussion'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Thay nó với checkpoint của bạn\n",
-    "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le module externe d'importation OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Dịch máy (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section4_tf.ipynb b/course/vi/chapter7/section4_tf.ipynb
deleted file mode 100644
index df2c62fe..00000000
--- a/course/vi/chapter7/section4_tf.ipynb
+++ /dev/null
@@ -1,729 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Dịch máy (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 210173\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 189155\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 21018\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
-    "split_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Default to expanded threads',\n",
-       " 'fr': 'Par défaut, développer les fils de discussion'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][1][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut pour les threads élargis'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Unable to import %1 using the OFX importer plugin. This file is not the correct format.',\n",
-       " 'fr': \"Impossible d'importer %1 en utilisant le module d'extension d'importation OFX. Ce fichier n'a pas un format correct.\"}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][172][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le plugin d'importateur OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
-    "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
-    "\n",
-    "inputs = tokenizer(en_sentence)\n",
-    "with tokenizer.as_target_tokenizer():\n",
-    "    targets = tokenizer(fr_sentence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Par', '▁dé', 'f', 'aut', ',', '▁dé', 've', 'lop', 'per', '▁les', '▁fil', 's', '▁de', '▁discussion', '</s>']\n",
-       "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong_targets = tokenizer(fr_sentence)\n",
-    "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
-    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 128\n",
-    "max_target_length = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
-    "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
-    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
-    "\n",
-    "    # Thiết lập tokenizer cho nhãn\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = split_datasets.map(\n",
-    "    preprocess_function,\n",
-    "    batched=True,\n",
-    "    remove_columns=split_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'input_ids', 'labels', 'decoder_input_ids'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
-    "batch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,  -100,\n",
-       "          -100,  -100,  -100,  -100,  -100,  -100],\n",
-       "        [ 1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,   817,\n",
-       "           550,  7032,  5821,  7907, 12649,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[59513,   577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,\n",
-       "         59513, 59513, 59513, 59513, 59513, 59513],\n",
-       "        [59513,  1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,\n",
-       "           817,   550,  7032,  5821,  7907, 12649]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"decoder_input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[577, 5891, 2, 3184, 16, 2542, 5, 1710, 0]\n",
-       "[1211, 3, 49, 9409, 1211, 3, 29140, 817, 3124, 817, 550, 7032, 5821, 7907, 12649, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(1, 3):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install sacrebleu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"sacrebleu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 46.750469682990165,\n",
-       " 'counts': [11, 6, 4, 3],\n",
-       " 'totals': [12, 11, 10, 9],\n",
-       " 'precisions': [91.67, 54.54, 40.0, 33.33],\n",
-       " 'bp': 0.9200444146293233,\n",
-       " 'sys_len': 12,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\n",
-    "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
-    "]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1.683602693167689,\n",
-       " 'counts': [1, 0, 0, 0],\n",
-       " 'totals': [4, 3, 2, 1],\n",
-       " 'precisions': [25.0, 16.67, 12.5, 12.5],\n",
-       " 'bp': 0.10539922456186433,\n",
-       " 'sys_len': 4,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This This This This\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0,\n",
-       " 'counts': [2, 1, 0, 0],\n",
-       " 'totals': [2, 1, 0, 0],\n",
-       " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
-       " 'bp': 0.004086771438464067,\n",
-       " 'sys_len': 2,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This plugin\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics():\n",
-    "    all_preds = []\n",
-    "    all_labels = []\n",
-    "    sampled_dataset = tokenized_datasets[\"validation\"].shuffle().select(range(200))\n",
-    "    tf_generate_dataset = sampled_dataset.to_tf_dataset(\n",
-    "        columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "        collate_fn=data_collator,\n",
-    "        shuffle=False,\n",
-    "        batch_size=4,\n",
-    "    )\n",
-    "    for batch in tf_generate_dataset:\n",
-    "        predictions = model.generate(\n",
-    "            input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
-    "        )\n",
-    "        decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "        labels = batch[\"labels\"].numpy()\n",
-    "        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "        decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "        decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "        all_preds.extend(decoded_preds)\n",
-    "        all_labels.extend(decoded_labels)\n",
-    "\n",
-    "    result = metric.compute(predictions=all_preds, references=all_labels)\n",
-    "    return {\"bleu\": result[\"score\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(compute_metrics())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Số bước huấn luyện là số lượng mẫu trong tập dữ liệu, chia cho kích thước lô sau đó nhân\n",
-    "# với tổng số epoch. Lưu ý rằng tf_train_dataset ở đây là tf.data.Dataset theo lô,\n",
-    "# không phải là Hugging Face Dataset ban đầu, vì vậy len() của nó vốn là num_samples // batch_size.\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Huấn luyện trong mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=\"marian-finetuned-kde4-en-to-fr\", tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_eval_dataset,\n",
-    "    callbacks=[callback],\n",
-    "    epochs=num_epochs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(compute_metrics())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut, développer les fils de discussion'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Thay nó với checkpoint của bạn\n",
-    "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le module externe d'importation OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Dịch máy (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section5_pt.ipynb b/course/vi/chapter7/section5_pt.ipynb
deleted file mode 100644
index 8cee1edb..00000000
--- a/course/vi/chapter7/section5_pt.ipynb
+++ /dev/null
@@ -1,1035 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tóm tắt (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 200000\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "spanish_dataset = load_dataset(\"amazon_reviews_multi\", \"es\")\n",
-    "english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n",
-    "english_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Worked in front position, not rear'\n",
-       "'>> Review: 3 stars because these are not rear brakes as stated in the item description. At least the mount adapter only worked on the front fork of the bike that I got it for.'\n",
-       "\n",
-       "'>> Title: meh'\n",
-       "'>> Review: Does it’s job and it’s gorgeous but mine is falling apart, I had to basically put it together again with hot glue'\n",
-       "\n",
-       "'>> Title: Can\\'t beat these for the money'\n",
-       "'>> Review: Bought this for handling miscellaneous aircraft parts and hanger \"stuff\" that I needed to organize; it really fit the bill. The unit arrived quickly, was well packaged and arrived intact (always a good sign). There are five wall mounts-- three on the top and two on the bottom. I wanted to mount it on the wall, so all I had to do was to remove the top two layers of plastic drawers, as well as the bottom corner drawers, place it when I wanted and mark it; I then used some of the new plastic screw in wall anchors (the 50 pound variety) and it easily mounted to the wall. Some have remarked that they wanted dividers for the drawers, and that they made those. Good idea. My application was that I needed something that I can see the contents at about eye level, so I wanted the fuller-sized drawers. I also like that these are the new plastic that doesn\\'t get brittle and split like my older plastic drawers did. I like the all-plastic construction. It\\'s heavy duty enough to hold metal parts, but being made of plastic it\\'s not as heavy as a metal frame, so you can easily mount it to the wall and still load it up with heavy stuff, or light stuff. No problem there. For the money, you can\\'t beat it. Best one of these I\\'ve bought to date-- and I\\'ve been using some version of these for over forty years.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def show_samples(dataset, num_samples=3, seed=42):\n",
-    "    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n",
-    "    for example in sample:\n",
-    "        print(f\"\\n'>> Title: {example['review_title']}'\")\n",
-    "        print(f\"'>> Review: {example['review_body']}'\")\n",
-    "\n",
-    "\n",
-    "show_samples(english_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "home                      17679\n",
-       "apparel                   15951\n",
-       "wireless                  15717\n",
-       "other                     13418\n",
-       "beauty                    12091\n",
-       "drugstore                 11730\n",
-       "kitchen                   10382\n",
-       "toy                        8745\n",
-       "sports                     8277\n",
-       "automotive                 7506\n",
-       "lawn_and_garden            7327\n",
-       "home_improvement           7136\n",
-       "pet_products               7082\n",
-       "digital_ebook_purchase     6749\n",
-       "pc                         6401\n",
-       "electronics                6186\n",
-       "office_product             5521\n",
-       "shoes                      5197\n",
-       "grocery                    4730\n",
-       "book                       3756\n",
-       "Name: product_category, dtype: int64"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "english_dataset.set_format(\"pandas\")\n",
-    "english_df = english_dataset[\"train\"][:]\n",
-    "# Hiển thị số lượng cho 20 sản phẩm hàng đầu\n",
-    "english_df[\"product_category\"].value_counts()[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_books(example):\n",
-    "    return (\n",
-    "        example[\"product_category\"] == \"book\"\n",
-    "        or example[\"product_category\"] == \"digital_ebook_purchase\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "english_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: I\\'m dissapointed.'\n",
-       "'>> Review: I guess I had higher expectations for this book from the reviews. I really thought I\\'d at least like it. The plot idea was great. I loved Ash but, it just didnt go anywhere. Most of the book was about their radio show and talking to callers. I wanted the author to dig deeper so we could really get to know the characters. All we know about Grace is that she is attractive looking, Latino and is kind of a brat. I\\'m dissapointed.'\n",
-       "\n",
-       "'>> Title: Good art, good price, poor design'\n",
-       "'>> Review: I had gotten the DC Vintage calendar the past two years, but it was on backorder forever this year and I saw they had shrunk the dimensions for no good reason. This one has good art choices but the design has the fold going through the picture, so it\\'s less aesthetically pleasing, especially if you want to keep a picture to hang. For the price, a good calendar'\n",
-       "\n",
-       "'>> Title: Helpful'\n",
-       "'>> Review: Nearly all the tips useful and. I consider myself an intermediate to advanced user of OneNote. I would highly recommend.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spanish_books = spanish_dataset.filter(filter_books)\n",
-    "english_books = english_dataset.filter(filter_books)\n",
-    "show_samples(english_books)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Easy to follow!!!!'\n",
-       "'>> Review: I loved The dash diet weight loss Solution. Never hungry. I would recommend this diet. Also the menus are well rounded. Try it. Has lots of the information need thanks.'\n",
-       "\n",
-       "'>> Title: PARCIALMENTE DAÑADO'\n",
-       "'>> Review: Me llegó el día que tocaba, junto a otros libros que pedí, pero la caja llegó en mal estado lo cual dañó las esquinas de los libros porque venían sin protección (forro).'\n",
-       "\n",
-       "'>> Title: no lo he podido descargar'\n",
-       "'>> Review: igual que el anterior'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import concatenate_datasets, DatasetDict\n",
-    "\n",
-    "books_dataset = DatasetDict()\n",
-    "\n",
-    "for split in english_books.keys():\n",
-    "    books_dataset[split] = concatenate_datasets(\n",
-    "        [english_books[split], spanish_books[split]]\n",
-    "    )\n",
-    "    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n",
-    "\n",
-    "# Chọn ra một vài mẫu\n",
-    "show_samples(books_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"google/mt5-small\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [336, 259, 28387, 11807, 287, 62893, 295, 12507, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"I loved reading the Hunger Games!\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁I', '▁', 'loved', '▁reading', '▁the', '▁Hung', 'er', '▁Games', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs.input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 512\n",
-    "max_target_length = 30\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    model_inputs = tokenizer(\n",
-    "        examples[\"review_body\"], max_length=max_input_length, truncation=True\n",
-    "    )\n",
-    "    # Thiết lập tokenizer cho nhãn\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(\n",
-    "            examples[\"review_title\"], max_length=max_target_length, truncation=True\n",
-    "        )\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generated_summary = \"I absolutely loved reading the Hunger Games\"\n",
-    "reference_summary = \"I loved reading the Hunger Games\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install rouge_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "rouge_score = evaluate.load(\"rouge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rouge2': AggregateScore(low=Score(precision=0.67, recall=0.8, fmeasure=0.73), mid=Score(precision=0.67, recall=0.8, fmeasure=0.73), high=Score(precision=0.67, recall=0.8, fmeasure=0.73)),\n",
-       " 'rougeL': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rougeLsum': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92))}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = rouge_score.compute(\n",
-    "    predictions=[generated_summary], references=[reference_summary]\n",
-    ")\n",
-    "scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Score(precision=0.86, recall=1.0, fmeasure=0.92)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores[\"rouge1\"].mid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install nltk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk\n",
-    "\n",
-    "nltk.download(\"punkt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'I grew up reading Koontz, and years ago, I stopped,convinced i had \"outgrown\" him.'\n",
-       "'Still,when a friend was looking for something suspenseful too read, I suggested Koontz.'\n",
-       "'She found Strangers.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "\n",
-    "\n",
-    "def three_sentence_summary(text):\n",
-    "    return \"\\n\".join(sent_tokenize(text)[:3])\n",
-    "\n",
-    "\n",
-    "print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_baseline(dataset, metric):\n",
-    "    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n",
-    "    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': 16.74, 'rouge2': 8.83, 'rougeL': 15.6, 'rougeLsum': 15.96}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n",
-    "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
-    "rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n",
-    "rouge_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainingArguments\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_train_epochs = 8\n",
-    "# Hiện thị mất mát huấn luyện mỗi epoch\n",
-    "logging_steps = len(tokenized_datasets[\"train\"]) // batch_size\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "args = Seq2SeqTrainingArguments(\n",
-    "    output_dir=f\"{model_name}-finetuned-amazon-en-es\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    learning_rate=5.6e-5,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    weight_decay=0.01,\n",
-    "    save_total_limit=3,\n",
-    "    num_train_epochs=num_train_epochs,\n",
-    "    predict_with_generate=True,\n",
-    "    logging_steps=logging_steps,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    # Giải mã các tóm tắt được tạo ra thành văn bản\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "    # Thay -100 vào nhãn vì ta không thể giải mã chúng\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    # Giải mã các tóm tắt mẫu thành văn bản\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "    # ROUGE kì vọng dòng mới sau mỗi câu\n",
-    "    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
-    "    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n",
-    "    # Tính điểm ROUGE\n",
-    "    result = rouge_score.compute(\n",
-    "        predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n",
-    "    )\n",
-    "    # Trích xuất điểm trung vị\n",
-    "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "    return {k: round(v, 4) for k, v in result.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
-    "    books_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[  1494,    259,   8622,    390,    259,    262,   2316,   3435,    955,\n",
-       "            772,    281,    772,   1617,    263,    305,  14701,    260,   1385,\n",
-       "           3031,    259,  24146,    332,   1037,    259,  43906,    305,    336,\n",
-       "            260,      1,      0,      0,      0,      0,      0,      0],\n",
-       "        [   259,  27531,  13483,    259,   7505,    260, 112240,  15192,    305,\n",
-       "          53198,    276,    259,  74060,    263,    260,    459,  25640,    776,\n",
-       "           2119,    336,    259,   2220,    259,  18896,    288,   4906,    288,\n",
-       "           1037,   3931,    260,   7083, 101476,   1143,    260,      1]]), 'labels': tensor([[ 7483,   259,  2364, 15695,     1,  -100],\n",
-       "        [  259, 27531, 13483,   259,  7505,     1]]), 'decoder_input_ids': tensor([[    0,  7483,   259,  2364, 15695,     1],\n",
-       "        [    0,   259, 27531, 13483,   259,  7505]])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n",
-    "data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainer\n",
-    "\n",
-    "trainer = Seq2SeqTrainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 3.028524398803711,\n",
-       " 'eval_rouge1': 16.9728,\n",
-       " 'eval_rouge2': 8.2969,\n",
-       " 'eval_rougeL': 16.8366,\n",
-       " 'eval_rougeLsum': 16.851,\n",
-       " 'eval_gen_len': 10.1597,\n",
-       " 'eval_runtime': 6.1054,\n",
-       " 'eval_samples_per_second': 38.982,\n",
-       " 'eval_steps_per_second': 4.914}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets.set_format(\"torch\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "batch_size = 8\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=batch_size,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=batch_size\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 10\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess_text(preds, labels):\n",
-    "    preds = [pred.strip() for pred in preds]\n",
-    "    labels = [label.strip() for label in labels]\n",
-    "\n",
-    "    # ROUGE kì vọng dòng mới sau mỗi câu\n",
-    "    preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n",
-    "    labels = [\"\\n\".join(nltk.sent_tokenize(label)) for label in labels]\n",
-    "\n",
-    "    return preds, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'lewtun/mt5-finetuned-amazon-en-es-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import get_full_repo_name\n",
-    "\n",
-    "model_name = \"test-bert-finetuned-squad-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "output_dir = \"results-mt5-finetuned-squad-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Epoch 0: {'rouge1': 5.6351, 'rouge2': 1.1625, 'rougeL': 5.4866, 'rougeLsum': 5.5005}\n",
-       "Epoch 1: {'rouge1': 9.8646, 'rouge2': 3.4106, 'rougeL': 9.9439, 'rougeLsum': 9.9306}\n",
-       "Epoch 2: {'rouge1': 11.0872, 'rouge2': 3.3273, 'rougeL': 11.0508, 'rougeLsum': 10.9468}\n",
-       "Epoch 3: {'rouge1': 11.8587, 'rouge2': 4.8167, 'rougeL': 11.7986, 'rougeLsum': 11.7518}\n",
-       "Epoch 4: {'rouge1': 12.9842, 'rouge2': 5.5887, 'rougeL': 12.7546, 'rougeLsum': 12.7029}\n",
-       "Epoch 5: {'rouge1': 13.4628, 'rouge2': 6.4598, 'rougeL': 13.312, 'rougeLsum': 13.2913}\n",
-       "Epoch 6: {'rouge1': 12.9131, 'rouge2': 5.8914, 'rougeL': 12.6896, 'rougeLsum': 12.5701}\n",
-       "Epoch 7: {'rouge1': 13.3079, 'rouge2': 6.2994, 'rougeL': 13.1536, 'rougeLsum': 13.1194}\n",
-       "Epoch 8: {'rouge1': 13.96, 'rouge2': 6.5998, 'rougeL': 13.9123, 'rougeLsum': 13.7744}\n",
-       "Epoch 9: {'rouge1': 14.1192, 'rouge2': 7.0059, 'rougeL': 14.1172, 'rougeLsum': 13.9509}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Huấn luyện\n",
-    "    model.train()\n",
-    "    for step, batch in enumerate(train_dataloader):\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Đánh giá\n",
-    "    model.eval()\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            generated_tokens = accelerator.unwrap_model(model).generate(\n",
-    "                batch[\"input_ids\"],\n",
-    "                attention_mask=batch[\"attention_mask\"],\n",
-    "            )\n",
-    "\n",
-    "            generated_tokens = accelerator.pad_across_processes(\n",
-    "                generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n",
-    "            )\n",
-    "            labels = batch[\"labels\"]\n",
-    "\n",
-    "            # Nếu ta không đệm đến độ giải tối đa, ta cần đệm cả nhãn nữa\n",
-    "            labels = accelerator.pad_across_processes(\n",
-    "                batch[\"labels\"], dim=1, pad_index=tokenizer.pad_token_id\n",
-    "            )\n",
-    "\n",
-    "            generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()\n",
-    "            labels = accelerator.gather(labels).cpu().numpy()\n",
-    "\n",
-    "            # Thay -100 ở nhãn vì ta không thể  giải mã chúng\n",
-    "            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "            if isinstance(generated_tokens, tuple):\n",
-    "                generated_tokens = generated_tokens[0]\n",
-    "            decoded_preds = tokenizer.batch_decode(\n",
-    "                generated_tokens, skip_special_tokens=True\n",
-    "            )\n",
-    "            decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "            decoded_preds, decoded_labels = postprocess_text(\n",
-    "                decoded_preds, decoded_labels\n",
-    "            )\n",
-    "\n",
-    "            rouge_score.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
-    "\n",
-    "    # Tính toán các chỉ số\n",
-    "    result = rouge_score.compute()\n",
-    "    # Trích xuất điểm trung vị ROUGE\n",
-    "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "    result = {k: round(v, 4) for k, v in result.items()}\n",
-    "    print(f\"Epoch {epoch}:\", result)\n",
-    "\n",
-    "    # Lưu và tải\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-es\"\n",
-    "summarizer = pipeline(\"summarization\", model=hub_model_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_summary(idx):\n",
-    "    review = books_dataset[\"test\"][idx][\"review_body\"]\n",
-    "    title = books_dataset[\"test\"][idx][\"review_title\"]\n",
-    "    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n",
-    "    print(f\"'>>> Review: {review}'\")\n",
-    "    print(f\"\\n'>>> Title: {title}'\")\n",
-    "    print(f\"\\n'>>> Summary: {summary}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Nothing special at all about this product... the book is too small and stiff and hard to write in. The huge sticker on the back doesn’t come off and looks super tacky. I would not purchase this again. I could have just bought a journal from the dollar store and it would be basically the same thing. It’s also really expensive for what it is.'\n",
-       "\n",
-       "'>>> Title: Not impressed at all... buy something else'\n",
-       "\n",
-       "'>>> Summary: Nothing special at all about this product'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Es una trilogia que se hace muy facil de leer. Me ha gustado, no me esperaba el final para nada'\n",
-       "\n",
-       "'>>> Title: Buena literatura para adolescentes'\n",
-       "\n",
-       "'>>> Summary: Muy facil de leer'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(0)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tóm tắt (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section5_tf.ipynb b/course/vi/chapter7/section5_tf.ipynb
deleted file mode 100644
index b1e40107..00000000
--- a/course/vi/chapter7/section5_tf.ipynb
+++ /dev/null
@@ -1,785 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tóm tắt (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 200000\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "spanish_dataset = load_dataset(\"amazon_reviews_multi\", \"es\")\n",
-    "english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n",
-    "english_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Worked in front position, not rear'\n",
-       "'>> Review: 3 stars because these are not rear brakes as stated in the item description. At least the mount adapter only worked on the front fork of the bike that I got it for.'\n",
-       "\n",
-       "'>> Title: meh'\n",
-       "'>> Review: Does it’s job and it’s gorgeous but mine is falling apart, I had to basically put it together again with hot glue'\n",
-       "\n",
-       "'>> Title: Can\\'t beat these for the money'\n",
-       "'>> Review: Bought this for handling miscellaneous aircraft parts and hanger \"stuff\" that I needed to organize; it really fit the bill. The unit arrived quickly, was well packaged and arrived intact (always a good sign). There are five wall mounts-- three on the top and two on the bottom. I wanted to mount it on the wall, so all I had to do was to remove the top two layers of plastic drawers, as well as the bottom corner drawers, place it when I wanted and mark it; I then used some of the new plastic screw in wall anchors (the 50 pound variety) and it easily mounted to the wall. Some have remarked that they wanted dividers for the drawers, and that they made those. Good idea. My application was that I needed something that I can see the contents at about eye level, so I wanted the fuller-sized drawers. I also like that these are the new plastic that doesn\\'t get brittle and split like my older plastic drawers did. I like the all-plastic construction. It\\'s heavy duty enough to hold metal parts, but being made of plastic it\\'s not as heavy as a metal frame, so you can easily mount it to the wall and still load it up with heavy stuff, or light stuff. No problem there. For the money, you can\\'t beat it. Best one of these I\\'ve bought to date-- and I\\'ve been using some version of these for over forty years.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def show_samples(dataset, num_samples=3, seed=42):\n",
-    "    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n",
-    "    for example in sample:\n",
-    "        print(f\"\\n'>> Title: {example['review_title']}'\")\n",
-    "        print(f\"'>> Review: {example['review_body']}'\")\n",
-    "\n",
-    "\n",
-    "show_samples(english_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "home                      17679\n",
-       "apparel                   15951\n",
-       "wireless                  15717\n",
-       "other                     13418\n",
-       "beauty                    12091\n",
-       "drugstore                 11730\n",
-       "kitchen                   10382\n",
-       "toy                        8745\n",
-       "sports                     8277\n",
-       "automotive                 7506\n",
-       "lawn_and_garden            7327\n",
-       "home_improvement           7136\n",
-       "pet_products               7082\n",
-       "digital_ebook_purchase     6749\n",
-       "pc                         6401\n",
-       "electronics                6186\n",
-       "office_product             5521\n",
-       "shoes                      5197\n",
-       "grocery                    4730\n",
-       "book                       3756\n",
-       "Name: product_category, dtype: int64"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "english_dataset.set_format(\"pandas\")\n",
-    "english_df = english_dataset[\"train\"][:]\n",
-    "# Hiển thị số lượng cho 20 sản phẩm hàng đầu\n",
-    "english_df[\"product_category\"].value_counts()[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_books(example):\n",
-    "    return (\n",
-    "        example[\"product_category\"] == \"book\"\n",
-    "        or example[\"product_category\"] == \"digital_ebook_purchase\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "english_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: I\\'m dissapointed.'\n",
-       "'>> Review: I guess I had higher expectations for this book from the reviews. I really thought I\\'d at least like it. The plot idea was great. I loved Ash but, it just didnt go anywhere. Most of the book was about their radio show and talking to callers. I wanted the author to dig deeper so we could really get to know the characters. All we know about Grace is that she is attractive looking, Latino and is kind of a brat. I\\'m dissapointed.'\n",
-       "\n",
-       "'>> Title: Good art, good price, poor design'\n",
-       "'>> Review: I had gotten the DC Vintage calendar the past two years, but it was on backorder forever this year and I saw they had shrunk the dimensions for no good reason. This one has good art choices but the design has the fold going through the picture, so it\\'s less aesthetically pleasing, especially if you want to keep a picture to hang. For the price, a good calendar'\n",
-       "\n",
-       "'>> Title: Helpful'\n",
-       "'>> Review: Nearly all the tips useful and. I consider myself an intermediate to advanced user of OneNote. I would highly recommend.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spanish_books = spanish_dataset.filter(filter_books)\n",
-    "english_books = english_dataset.filter(filter_books)\n",
-    "show_samples(english_books)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Easy to follow!!!!'\n",
-       "'>> Review: I loved The dash diet weight loss Solution. Never hungry. I would recommend this diet. Also the menus are well rounded. Try it. Has lots of the information need thanks.'\n",
-       "\n",
-       "'>> Title: PARCIALMENTE DAÑADO'\n",
-       "'>> Review: Me llegó el día que tocaba, junto a otros libros que pedí, pero la caja llegó en mal estado lo cual dañó las esquinas de los libros porque venían sin protección (forro).'\n",
-       "\n",
-       "'>> Title: no lo he podido descargar'\n",
-       "'>> Review: igual que el anterior'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import concatenate_datasets, DatasetDict\n",
-    "\n",
-    "books_dataset = DatasetDict()\n",
-    "\n",
-    "for split in english_books.keys():\n",
-    "    books_dataset[split] = concatenate_datasets(\n",
-    "        [english_books[split], spanish_books[split]]\n",
-    "    )\n",
-    "    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n",
-    "\n",
-    "# Chọn ra một vài mẫu\n",
-    "show_samples(books_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"google/mt5-small\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [336, 259, 28387, 11807, 287, 62893, 295, 12507, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"I loved reading the Hunger Games!\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁I', '▁', 'loved', '▁reading', '▁the', '▁Hung', 'er', '▁Games', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs.input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 512\n",
-    "max_target_length = 30\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    model_inputs = tokenizer(\n",
-    "        examples[\"review_body\"], max_length=max_input_length, truncation=True\n",
-    "    )\n",
-    "    # Thiết lập tokenizer cho nhãn\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(\n",
-    "            examples[\"review_title\"], max_length=max_target_length, truncation=True\n",
-    "        )\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generated_summary = \"I absolutely loved reading the Hunger Games\"\n",
-    "reference_summary = \"I loved reading the Hunger Games\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install rouge_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "rouge_score = evaluate.load(\"rouge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rouge2': AggregateScore(low=Score(precision=0.67, recall=0.8, fmeasure=0.73), mid=Score(precision=0.67, recall=0.8, fmeasure=0.73), high=Score(precision=0.67, recall=0.8, fmeasure=0.73)),\n",
-       " 'rougeL': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rougeLsum': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92))}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = rouge_score.compute(\n",
-    "    predictions=[generated_summary], references=[reference_summary]\n",
-    ")\n",
-    "scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Score(precision=0.86, recall=1.0, fmeasure=0.92)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores[\"rouge1\"].mid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install nltk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk\n",
-    "\n",
-    "nltk.download(\"punkt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'I grew up reading Koontz, and years ago, I stopped,convinced i had \"outgrown\" him.'\n",
-       "'Still,when a friend was looking for something suspenseful too read, I suggested Koontz.'\n",
-       "'She found Strangers.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "\n",
-    "\n",
-    "def three_sentence_summary(text):\n",
-    "    return \"\\n\".join(sent_tokenize(text)[:3])\n",
-    "\n",
-    "\n",
-    "print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_baseline(dataset, metric):\n",
-    "    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n",
-    "    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': 16.74, 'rouge2': 8.83, 'rougeL': 15.6, 'rougeLsum': 15.96}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n",
-    "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
-    "rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n",
-    "rouge_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
-    "    books_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[  1494,    259,   8622,    390,    259,    262,   2316,   3435,    955,\n",
-       "            772,    281,    772,   1617,    263,    305,  14701,    260,   1385,\n",
-       "           3031,    259,  24146,    332,   1037,    259,  43906,    305,    336,\n",
-       "            260,      1,      0,      0,      0,      0,      0,      0],\n",
-       "        [   259,  27531,  13483,    259,   7505,    260, 112240,  15192,    305,\n",
-       "          53198,    276,    259,  74060,    263,    260,    459,  25640,    776,\n",
-       "           2119,    336,    259,   2220,    259,  18896,    288,   4906,    288,\n",
-       "           1037,   3931,    260,   7083, 101476,   1143,    260,      1]]), 'labels': tensor([[ 7483,   259,  2364, 15695,     1,  -100],\n",
-       "        [  259, 27531, 13483,   259,  7505,     1]]), 'decoder_input_ids': tensor([[    0,  7483,   259,  2364, 15695,     1],\n",
-       "        [    0,   259, 27531, 13483,   259,  7505]])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n",
-    "data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Số bước huấn luyện là số lượng mẫu trong tập dữ liệu, chia cho kích thước lô sau đó nhân\n",
-    "# với tổng số epoch. Lưu ý rằng tf_train_dataset ở đây là tf.data.Dataset theo lô,\n",
-    "# không phải là Hugging Face Dataset ban đầu, vì vậy len() của nó vốn là num_samples // batch_size.\n",
-    "\n",
-    "num_train_epochs = 8\n",
-    "num_train_steps = len(tf_train_dataset) * num_train_epochs\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5.6e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Huấn luyện trong mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=f\"{model_name}-finetuned-amazon-en-es\", tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback], epochs=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm\n",
-    "import numpy as np\n",
-    "\n",
-    "all_preds = []\n",
-    "all_labels = []\n",
-    "for batch in tqdm(tf_eval_dataset):\n",
-    "    predictions = model.generate(**batch)\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "    labels = batch[\"labels\"].numpy()\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
-    "    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n",
-    "    all_preds.extend(decoded_preds)\n",
-    "    all_labels.extend(decoded_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = rouge_score.compute(\n",
-    "    predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n",
-    ")\n",
-    "result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "{k: round(v, 4) for k, v in result.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-es\"\n",
-    "summarizer = pipeline(\"summarization\", model=hub_model_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_summary(idx):\n",
-    "    review = books_dataset[\"test\"][idx][\"review_body\"]\n",
-    "    title = books_dataset[\"test\"][idx][\"review_title\"]\n",
-    "    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n",
-    "    print(f\"'>>> Review: {review}'\")\n",
-    "    print(f\"\\n'>>> Title: {title}'\")\n",
-    "    print(f\"\\n'>>> Summary: {summary}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Nothing special at all about this product... the book is too small and stiff and hard to write in. The huge sticker on the back doesn’t come off and looks super tacky. I would not purchase this again. I could have just bought a journal from the dollar store and it would be basically the same thing. It’s also really expensive for what it is.'\n",
-       "\n",
-       "'>>> Title: Not impressed at all... buy something else'\n",
-       "\n",
-       "'>>> Summary: Nothing special at all about this product'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Es una trilogia que se hace muy facil de leer. Me ha gustado, no me esperaba el final para nada'\n",
-       "\n",
-       "'>>> Title: Buena literatura para adolescentes'\n",
-       "\n",
-       "'>>> Summary: Muy facil de leer'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(0)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tóm tắt (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section6_pt.ipynb b/course/vi/chapter7/section6_pt.ipynb
deleted file mode 100644
index b4cc0383..00000000
--- a/course/vi/chapter7/section6_pt.ipynb
+++ /dev/null
@@ -1,895 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Huấn luyện một mô hình ngôn ngữ nhân quả từ đầu (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def any_keyword_in_string(string, keywords):\n",
-    "    for keyword in keywords:\n",
-    "        if keyword in string:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "example_1 = \"import numpy as np\"\n",
-    "example_2 = \"import pandas as pd\"\n",
-    "\n",
-    "print(\n",
-    "    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm import tqdm\n",
-    "from datasets import Dataset\n",
-    "\n",
-    "\n",
-    "def filter_streaming_dataset(dataset, filters):\n",
-    "    filtered_dict = defaultdict(list)\n",
-    "    total = 0\n",
-    "    for sample in tqdm(iter(dataset)):\n",
-    "        total += 1\n",
-    "        if any_keyword_in_string(sample[\"content\"], filters):\n",
-    "            for k, v in sample.items():\n",
-    "                filtered_dict[k].append(v)\n",
-    "    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n",
-    "    return Dataset.from_dict(filtered_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.26% of data after filtering."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Ô này sẽ mất rất nhiều thời gian để thực thi, vì vậy bạn nên bỏ qua và chuyển đến\n",
-    "# cái tiếp theo!\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "split = \"train\"  # \"valid\"\n",
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "\n",
-    "data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n",
-    "filtered_data = filter_streaming_dataset(data, filters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 606720\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 3322\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, DatasetDict\n",
-    "\n",
-    "ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n",
-    "ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"validation\")\n",
-    "\n",
-    "raw_datasets = DatasetDict(\n",
-    "    {\n",
-    "        \"train\": ds_train,  # .shuffle().select(range(50000)),\n",
-    "        \"valid\": ds_valid,  # .shuffle().select(range(500))\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'REPO_NAME: kmike/scikit-learn'\n",
-       "'PATH: sklearn/utils/__init__.py'\n",
-       "'COPIES: 3'\n",
-       "'SIZE: 10094'\n",
-       "'''CONTENT: \"\"\"\n",
-       "The :mod:`sklearn.utils` module includes various utilites.\n",
-       "\"\"\"\n",
-       "\n",
-       "from collections import Sequence\n",
-       "\n",
-       "import numpy as np\n",
-       "from scipy.sparse import issparse\n",
-       "import warnings\n",
-       "\n",
-       "from .murmurhash import murm\n",
-       "LICENSE: bsd-3-clause'''"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for key in raw_datasets[\"train\"][0]:\n",
-    "    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs length: 34\n",
-       "Input chunk lengths: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 117, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 41]\n",
-       "Chunk mapping: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "context_length = 128\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n",
-    "\n",
-    "outputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][:2][\"content\"],\n",
-    "    truncation=True,\n",
-    "    max_length=context_length,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_length=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n",
-    "print(f\"Input chunk lengths: {(outputs['length'])}\")\n",
-    "print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 16702061\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 93164\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(element):\n",
-    "    outputs = tokenizer(\n",
-    "        element[\"content\"],\n",
-    "        truncation=True,\n",
-    "        max_length=context_length,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_length=True,\n",
-    "    )\n",
-    "    input_batch = []\n",
-    "    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
-    "        if length == context_length:\n",
-    "            input_batch.append(input_ids)\n",
-    "    return {\"input_ids\": input_batch}\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\n",
-    "    \"gpt2\",\n",
-    "    vocab_size=len(tokenizer),\n",
-    "    n_ctx=context_length,\n",
-    "    bos_token_id=tokenizer.bos_token_id,\n",
-    "    eos_token_id=tokenizer.eos_token_id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "GPT-2 size: 124.2M parameters"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = GPT2LMHeadModel(config)\n",
-    "model_size = sum(t.numel() for t in model.parameters())\n",
-    "print(f\"GPT-2 size: {model_size/1000**2:.1f}M parameters\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "input_ids shape: torch.Size([5, 128])\n",
-       "attention_mask shape: torch.Size([5, 128])\n",
-       "labels shape: torch.Size([5, 128])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out = data_collator([tokenized_datasets[\"train\"][i] for i in range(5)])\n",
-    "for key in out:\n",
-    "    print(f\"{key} shape: {out[key].shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer, TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    output_dir=\"codeparrot-ds\",\n",
-    "    per_device_train_batch_size=32,\n",
-    "    per_device_eval_batch_size=32,\n",
-    "    evaluation_strategy=\"steps\",\n",
-    "    eval_steps=5_000,\n",
-    "    logging_steps=5_000,\n",
-    "    gradient_accumulation_steps=8,\n",
-    "    num_train_epochs=1,\n",
-    "    weight_decay=0.1,\n",
-    "    warmup_steps=1_000,\n",
-    "    lr_scheduler_type=\"cosine\",\n",
-    "    learning_rate=5e-4,\n",
-    "    save_steps=5_000,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    tokenizer=tokenizer,\n",
-    "    args=args,\n",
-    "    data_collator=data_collator,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"valid\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import pipeline\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=\"huggingface-course/codeparrot-ds\", device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create scatter plot with x, y\n",
-       "plt.scatter(x, y)\n",
-       "\n",
-       "# create scatter"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create scatter plot with x, y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create dataframe from x and y\n",
-       "df = pd.DataFrame({'x': x, 'y': y})\n",
-       "df.insert(0,'x', x)\n",
-       "for"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create dataframe from x and y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# dataframe with profession, income and name\n",
-       "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-       "\n",
-       "# calculate the mean income per profession\n",
-       "profession = df.groupby(['profession']).mean()\n",
-       "\n",
-       "# compute the"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# dataframe with profession, income and name\n",
-    "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-    "\n",
-    "# calculate the mean income per profession\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# import random forest regressor from scikit-learn\n",
-       "from sklearn.ensemble import RandomForestRegressor\n",
-       "\n",
-       "# fit random forest model with 300 estimators on X, y:\n",
-       "rf = RandomForestRegressor(n_estimators=300, random_state=random_state, max_depth=3)\n",
-       "rf.fit(X, y)\n",
-       "rf"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\n",
-    "# import random forest regressor from scikit-learn\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "# fit random forest model with 300 estimators on X, y:\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Keyword has not single token: testtest'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "keytoken_ids = []\n",
-    "for keyword in [\n",
-    "    \"plt\",\n",
-    "    \"pd\",\n",
-    "    \"sk\",\n",
-    "    \"fit\",\n",
-    "    \"predict\",\n",
-    "    \" plt\",\n",
-    "    \" pd\",\n",
-    "    \" sk\",\n",
-    "    \" fit\",\n",
-    "    \" predict\",\n",
-    "    \"testtest\",\n",
-    "]:\n",
-    "    ids = tokenizer([keyword]).input_ids[0]\n",
-    "    if len(ids) == 1:\n",
-    "        keytoken_ids.append(ids[0])\n",
-    "    else:\n",
-    "        print(f\"Keyword has not single token: {keyword}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.nn import CrossEntropyLoss\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "def keytoken_weighted_loss(inputs, logits, keytoken_ids, alpha=1.0):\n",
-    "    # Dịch chuyển để token < n dự đoán n\n",
-    "    shift_labels = inputs[..., 1:].contiguous()\n",
-    "    shift_logits = logits[..., :-1, :].contiguous()\n",
-    "    # Tính độ mất mát từng token\n",
-    "    loss_fct = CrossEntropyLoss(reduce=False)\n",
-    "    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n",
-    "    # Thay đổi kích thước và mất mát trung bình trên mỗi mẫu\n",
-    "    loss_per_sample = loss.view(shift_logits.size(0), shift_logits.size(1)).mean(axis=1)\n",
-    "    # Tính toán và chia tỷ trọng\n",
-    "    weights = torch.stack([(inputs == kt).float() for kt in keytoken_ids]).sum(\n",
-    "        axis=[0, 2]\n",
-    "    )\n",
-    "    weights = alpha * (1.0 + weights)\n",
-    "    # Tính giá trị trung bình có trọng số\n",
-    "    weighted_loss = (loss_per_sample * weights).mean()\n",
-    "    return weighted_loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data.dataloader import DataLoader\n",
-    "\n",
-    "tokenized_dataset.set_format(\"torch\")\n",
-    "train_dataloader = DataLoader(tokenized_dataset[\"train\"], batch_size=32, shuffle=True)\n",
-    "eval_dataloader = DataLoader(tokenized_dataset[\"valid\"], batch_size=32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weight_decay = 0.1\n",
-    "\n",
-    "\n",
-    "def get_grouped_params(model, no_decay=[\"bias\", \"LayerNorm.weight\"]):\n",
-    "    params_with_wd, params_without_wd = [], []\n",
-    "    for n, p in model.named_parameters():\n",
-    "        if any(nd in n for nd in no_decay):\n",
-    "            params_without_wd.append(p)\n",
-    "        else:\n",
-    "            params_with_wd.append(p)\n",
-    "    return [\n",
-    "        {\"params\": params_with_wd, \"weight_decay\": weight_decay},\n",
-    "        {\"params\": params_without_wd, \"weight_decay\": 0.0},\n",
-    "    ]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate():\n",
-    "    model.eval()\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(batch[\"input_ids\"], labels=batch[\"input_ids\"])\n",
-    "\n",
-    "        losses.append(accelerator.gather(outputs.loss))\n",
-    "    loss = torch.mean(torch.cat(losses))\n",
-    "    try:\n",
-    "        perplexity = torch.exp(loss)\n",
-    "    except OverflowError:\n",
-    "        perplexity = float(\"inf\")\n",
-    "    return loss.item(), perplexity.item()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = GPT2LMHeadModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(get_grouped_params(model), lr=5e-4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator(fp16=True)\n",
-    "\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 1\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    name=\"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/codeparrot-ds-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"codeparrot-ds-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"codeparrot-ds-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.934126853942871, 56057.14453125)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "gradient_accumulation_steps = 8\n",
-    "eval_steps = 5_000\n",
-    "\n",
-    "model.train()\n",
-    "completed_steps = 0\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    for step, batch in tqdm(\n",
-    "        enumerate(train_dataloader, start=1), total=num_training_steps\n",
-    "    ):\n",
-    "        logits = model(batch[\"input_ids\"]).logits\n",
-    "        loss = keytoken_weighted_loss(batch[\"input_ids\"], logits, keytoken_ids)\n",
-    "        if step % 100 == 0:\n",
-    "            accelerator.print(\n",
-    "                {\n",
-    "                    \"lr\": get_lr(),\n",
-    "                    \"samples\": step * samples_per_step,\n",
-    "                    \"steps\": completed_steps,\n",
-    "                    \"loss/train\": loss.item() * gradient_accumulation_steps,\n",
-    "                }\n",
-    "            )\n",
-    "        loss = loss / gradient_accumulation_steps\n",
-    "        accelerator.backward(loss)\n",
-    "        if step % gradient_accumulation_steps == 0:\n",
-    "            accelerator.clip_grad_norm_(model.parameters(), 1.0)\n",
-    "            optimizer.step()\n",
-    "            lr_scheduler.step()\n",
-    "            optimizer.zero_grad()\n",
-    "            completed_steps += 1\n",
-    "        if (step % (eval_steps * gradient_accumulation_steps)) == 0:\n",
-    "            eval_loss, perplexity = evaluate()\n",
-    "            accelerator.print({\"loss/eval\": eval_loss, \"perplexity\": perplexity})\n",
-    "            model.train()\n",
-    "            accelerator.wait_for_everyone()\n",
-    "            unwrapped_model = accelerator.unwrap_model(model)\n",
-    "            unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "            if accelerator.is_main_process:\n",
-    "                tokenizer.save_pretrained(output_dir)\n",
-    "                repo.push_to_hub(\n",
-    "                    commit_message=f\"Training in progress step {step}\", blocking=False\n",
-    "                )"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Huấn luyện một mô hình ngôn ngữ nhân quả từ đầu (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section6_tf.ipynb b/course/vi/chapter7/section6_tf.ipynb
deleted file mode 100644
index b09bd10d..00000000
--- a/course/vi/chapter7/section6_tf.ipynb
+++ /dev/null
@@ -1,618 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Huấn luyện một mô hình ngôn ngữ nhân quả từ đầu (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def any_keyword_in_string(string, keywords):\n",
-    "    for keyword in keywords:\n",
-    "        if keyword in string:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "example_1 = \"import numpy as np\"\n",
-    "example_2 = \"import pandas as pd\"\n",
-    "\n",
-    "print(\n",
-    "    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "from tqdm import tqdm\n",
-    "from datasets import Dataset\n",
-    "\n",
-    "\n",
-    "def filter_streaming_dataset(dataset, filters):\n",
-    "    filtered_dict = defaultdict(list)\n",
-    "    total = 0\n",
-    "    for sample in tqdm(iter(dataset)):\n",
-    "        total += 1\n",
-    "        if any_keyword_in_string(sample[\"content\"], filters):\n",
-    "            for k, v in sample.items():\n",
-    "                filtered_dict[k].append(v)\n",
-    "    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n",
-    "    return Dataset.from_dict(filtered_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.26% of data after filtering."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Ô này sẽ mất rất nhiều thời gian để thực thi, vì vậy bạn nên bỏ qua và chuyển đến\n",
-    "# cái tiếp theo!\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "split = \"train\"  # \"valid\"\n",
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "\n",
-    "data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n",
-    "filtered_data = filter_streaming_dataset(data, filters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 606720\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 3322\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, DatasetDict\n",
-    "\n",
-    "ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n",
-    "ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"validation\")\n",
-    "\n",
-    "raw_datasets = DatasetDict(\n",
-    "    {\n",
-    "        \"train\": ds_train,  # .shuffle().select(range(50000)),\n",
-    "        \"valid\": ds_valid,  # .shuffle().select(range(500))\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'REPO_NAME: kmike/scikit-learn'\n",
-       "'PATH: sklearn/utils/__init__.py'\n",
-       "'COPIES: 3'\n",
-       "'SIZE: 10094'\n",
-       "'''CONTENT: \"\"\"\n",
-       "The :mod:`sklearn.utils` module includes various utilites.\n",
-       "\"\"\"\n",
-       "\n",
-       "from collections import Sequence\n",
-       "\n",
-       "import numpy as np\n",
-       "from scipy.sparse import issparse\n",
-       "import warnings\n",
-       "\n",
-       "from .murmurhash import murm\n",
-       "LICENSE: bsd-3-clause'''"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for key in raw_datasets[\"train\"][0]:\n",
-    "    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs length: 34\n",
-       "Input chunk lengths: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 117, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 41]\n",
-       "Chunk mapping: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "context_length = 128\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n",
-    "\n",
-    "outputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][:2][\"content\"],\n",
-    "    truncation=True,\n",
-    "    max_length=context_length,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_length=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n",
-    "print(f\"Input chunk lengths: {(outputs['length'])}\")\n",
-    "print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 16702061\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 93164\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(element):\n",
-    "    outputs = tokenizer(\n",
-    "        element[\"content\"],\n",
-    "        truncation=True,\n",
-    "        max_length=context_length,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_length=True,\n",
-    "    )\n",
-    "    input_batch = []\n",
-    "    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
-    "        if length == context_length:\n",
-    "            input_batch.append(input_ids)\n",
-    "    return {\"input_ids\": input_batch}\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFGPT2LMHeadModel, AutoConfig\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\n",
-    "    \"gpt2\",\n",
-    "    vocab_size=len(tokenizer),\n",
-    "    n_ctx=context_length,\n",
-    "    bos_token_id=tokenizer.bos_token_id,\n",
-    "    eos_token_id=tokenizer.eos_token_id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "_________________________________________________________________\n",
-       "Layer (type)                 Output Shape              Param #\n",
-       "=================================================================\n",
-       "transformer (TFGPT2MainLayer multiple                  124242432\n",
-       "=================================================================\n",
-       "Total params: 124,242,432\n",
-       "Trainable params: 124,242,432\n",
-       "Non-trainable params: 0\n",
-       "_________________________________________________________________"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFGPT2LMHeadModel(config)\n",
-    "model(model.dummy_inputs)  # Xây mô hình\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "input_ids shape: (5, 128)\n",
-       "attention_mask shape: (5, 128)\n",
-       "labels shape: (5, 128)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out = data_collator([tokenized_datasets[\"train\"][i] for i in range(5)])\n",
-    "for key in out:\n",
-    "    print(f\"{key} shape: {out[key].shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_dataset[\"valid\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=32,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "num_train_steps = len(tf_train_dataset)\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5e-5,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Huấn luyện trong mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"codeparrot-ds\", tokenizer=tokenizer)\n",
-    "\n",
-    "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "course_model = TFGPT2LMHeadModel.from_pretrained(\"huggingface-course/codeparrot-ds\")\n",
-    "course_tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/codeparrot-ds\")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=course_model, tokenizer=course_tokenizer, device=0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create scatter plot with x, y\n",
-       "plt.scatter(x, y)\n",
-       "\n",
-       "# create scatter"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create scatter plot with x, y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create dataframe from x and y\n",
-       "df = pd.DataFrame({'x': x, 'y': y})\n",
-       "df.insert(0,'x', x)\n",
-       "for"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create dataframe from x and y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# dataframe with profession, income and name\n",
-       "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-       "\n",
-       "# calculate the mean income per profession\n",
-       "profession = df.groupby(['profession']).mean()\n",
-       "\n",
-       "# compute the"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# dataframe with profession, income and name\n",
-    "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-    "\n",
-    "# calculate the mean income per profession\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# import random forest regressor from scikit-learn\n",
-       "from sklearn.ensemble import RandomForestRegressor\n",
-       "\n",
-       "# fit random forest model with 300 estimators on X, y:\n",
-       "rf = RandomForestRegressor(n_estimators=300, random_state=random_state, max_depth=3)\n",
-       "rf.fit(X, y)\n",
-       "rf"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\n",
-    "# import random forest regressor from scikit-learn\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "# fit random forest model with 300 estimators on X, y:\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Huấn luyện một mô hình ngôn ngữ nhân quả từ đầu (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section7_pt.ipynb b/course/vi/chapter7/section7_pt.ipynb
deleted file mode 100644
index 83338ed5..00000000
--- a/course/vi/chapter7/section7_pt.ipynb
+++ /dev/null
@@ -1,1218 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hỏi đáp (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 87599\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 10570\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Context: 'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'\n",
-       "Question: 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'\n",
-       "Answer: {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n",
-    "print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n",
-    "print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "    num_rows: 0\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}\n",
-       "{'text': ['Santa Clara, California', \"Levi's Stadium\", \"Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.\"], 'answer_start': [403, 355, 355]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][0][\"answers\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \"Super Bowl L\"), so that the logo could prominently feature the Arabic numerals 50.'\n",
-       "'Where did Super Bowl 50 take place?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][2][\"context\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, '\n",
-       "'the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin '\n",
-       "'Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms '\n",
-       "'upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred '\n",
-       "'Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a '\n",
-       "'replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette '\n",
-       "'Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues '\n",
-       "'and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context = raw_datasets[\"train\"][0][\"context\"]\n",
-    "question = raw_datasets[\"train\"][0][\"question\"]\n",
-    "\n",
-    "inputs = tokenizer(question, context)\n",
-    "tokenizer.decode(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basi [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP]. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "inputs.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"overflow_to_sample_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The 4 examples gave 19 features.'\n",
-       "'Here is where each comes from: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3].'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][2:6][\"question\"],\n",
-    "    raw_datasets[\"train\"][2:6][\"context\"],\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n",
-    "print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([83, 51, 19, 0, 0, 64, 27, 0, 34, 0, 0, 0, 67, 34, 0, 0, 0, 0, 0],\n",
-       " [85, 53, 21, 0, 0, 70, 33, 0, 40, 0, 0, 0, 68, 35, 0, 0, 0, 0, 0])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "answers = raw_datasets[\"train\"][2:6][\"answers\"]\n",
-    "start_positions = []\n",
-    "end_positions = []\n",
-    "\n",
-    "for i, offset in enumerate(inputs[\"offset_mapping\"]):\n",
-    "    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n",
-    "    answer = answers[sample_idx]\n",
-    "    start_char = answer[\"answer_start\"][0]\n",
-    "    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "    sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "    # Tìm điểm bắt đầu và kết thúc của ngữ cảnh\n",
-    "    while sequence_ids[idx] != 1:\n",
-    "        idx += 1\n",
-    "    context_start = idx\n",
-    "    while sequence_ids[idx] == 1:\n",
-    "        idx += 1\n",
-    "    context_end = idx - 1\n",
-    "\n",
-    "    # Nếu câu trả lời không hoàn toàn nằm trong ngữ cảnh, nhãn là (0, 0)\n",
-    "    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "        start_positions.append(0)\n",
-    "        end_positions.append(0)\n",
-    "    else:\n",
-    "        # Nếu không nó sẽ là vị trí bắt đầu và kết thúc\n",
-    "        idx = context_start\n",
-    "        while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "            idx += 1\n",
-    "        start_positions.append(idx - 1)\n",
-    "\n",
-    "        idx = context_end\n",
-    "        while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "            idx -= 1\n",
-    "        end_positions.append(idx + 1)\n",
-    "\n",
-    "start_positions, end_positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: the Main Building, labels give: the Main Building'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "start = start_positions[idx]\n",
-    "end = end_positions[idx]\n",
-    "labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n",
-    "\n",
-    "print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: a Marian place of prayer and reflection, decoded example: [CLS] What is the Grotto at Notre Dame? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grot [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 4\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n",
-    "print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_length = 384\n",
-    "stride = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_training_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    answers = examples[\"answers\"]\n",
-    "    start_positions = []\n",
-    "    end_positions = []\n",
-    "\n",
-    "    for i, offset in enumerate(offset_mapping):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        answer = answers[sample_idx]\n",
-    "        start_char = answer[\"answer_start\"][0]\n",
-    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "        # Tìm điểm bắt đầu và kết thúc của ngữ cảnh\n",
-    "        idx = 0\n",
-    "        while sequence_ids[idx] != 1:\n",
-    "            idx += 1\n",
-    "        context_start = idx\n",
-    "        while sequence_ids[idx] == 1:\n",
-    "            idx += 1\n",
-    "        context_end = idx - 1\n",
-    "\n",
-    "        # Nếu câu trả lời không hoàn toàn nằm trong ngữ cảnh, nhãn là (0, 0)\n",
-    "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "            start_positions.append(0)\n",
-    "            end_positions.append(0)\n",
-    "        else:\n",
-    "            # Nếu không nó sẽ là vị trí token bắt đầu và kết thúc\n",
-    "            idx = context_start\n",
-    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "                idx += 1\n",
-    "            start_positions.append(idx - 1)\n",
-    "\n",
-    "            idx = context_end\n",
-    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "                idx -= 1\n",
-    "            end_positions.append(idx + 1)\n",
-    "\n",
-    "    inputs[\"start_positions\"] = start_positions\n",
-    "    inputs[\"end_positions\"] = end_positions\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(87599, 88729)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_dataset = raw_datasets[\"train\"].map(\n",
-    "    preprocess_training_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"train\"]), len(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess_validation_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    example_ids = []\n",
-    "\n",
-    "    for i in range(len(inputs[\"input_ids\"])):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        example_ids.append(examples[\"id\"][sample_idx])\n",
-    "\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "        offset = inputs[\"offset_mapping\"][i]\n",
-    "        inputs[\"offset_mapping\"][i] = [\n",
-    "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
-    "        ]\n",
-    "\n",
-    "    inputs[\"example_id\"] = example_ids\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10570, 10822)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation_dataset = raw_datasets[\"validation\"].map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"validation\"]), len(validation_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "small_eval_set = raw_datasets[\"validation\"].select(range(100))\n",
-    "trained_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n",
-    "eval_set = small_eval_set.map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoModelForQuestionAnswering\n",
-    "\n",
-    "eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "eval_set_for_model.set_format(\"torch\")\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: eval_set_for_model[k].to(device) for k in eval_set_for_model.column_names}\n",
-    "trained_model = AutoModelForQuestionAnswering.from_pretrained(trained_checkpoint).to(\n",
-    "    device\n",
-    ")\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trained_model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_logits = outputs.start_logits.cpu().numpy()\n",
-    "end_logits = outputs.end_logits.cpu().numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "\n",
-    "example_to_features = collections.defaultdict(list)\n",
-    "for idx, feature in enumerate(eval_set):\n",
-    "    example_to_features[feature[\"example_id\"]].append(idx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "n_best = 20\n",
-    "max_answer_length = 30\n",
-    "predicted_answers = []\n",
-    "\n",
-    "for example in small_eval_set:\n",
-    "    example_id = example[\"id\"]\n",
-    "    context = example[\"context\"]\n",
-    "    answers = []\n",
-    "\n",
-    "    for feature_index in example_to_features[example_id]:\n",
-    "        start_logit = start_logits[feature_index]\n",
-    "        end_logit = end_logits[feature_index]\n",
-    "        offsets = eval_set[\"offset_mapping\"][feature_index]\n",
-    "\n",
-    "        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        for start_index in start_indexes:\n",
-    "            for end_index in end_indexes:\n",
-    "                # Bỏ qua các câu trả lời không đầu đủ trong ngữ cảnh\n",
-    "                if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                    continue\n",
-    "                # Bỏ qua những câu trả lời có độ dài < 0 hoặc > max_answer_length.\n",
-    "                if (\n",
-    "                    end_index < start_index\n",
-    "                    or end_index - start_index + 1 > max_answer_length\n",
-    "                ):\n",
-    "                    continue\n",
-    "\n",
-    "                answers.append(\n",
-    "                    {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                )\n",
-    "\n",
-    "    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theoretical_answers = [\n",
-    "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\n",
-       "{'id': '56be4db0acb8001400a502ec', 'answers': {'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(predicted_answers[0])\n",
-    "print(theoretical_answers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "\n",
-    "def compute_metrics(start_logits, end_logits, features, examples):\n",
-    "    example_to_features = collections.defaultdict(list)\n",
-    "    for idx, feature in enumerate(features):\n",
-    "        example_to_features[feature[\"example_id\"]].append(idx)\n",
-    "\n",
-    "    predicted_answers = []\n",
-    "    for example in tqdm(examples):\n",
-    "        example_id = example[\"id\"]\n",
-    "        context = example[\"context\"]\n",
-    "        answers = []\n",
-    "\n",
-    "        # Lặp qua tất cả các đặc trưng liên quan tới mẫu đó\n",
-    "        for feature_index in example_to_features[example_id]:\n",
-    "            start_logit = start_logits[feature_index]\n",
-    "            end_logit = end_logits[feature_index]\n",
-    "            offsets = features[feature_index][\"offset_mapping\"]\n",
-    "\n",
-    "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            for start_index in start_indexes:\n",
-    "                for end_index in end_indexes:\n",
-    "                    # Bỏ qua câu trả lời không xuất hiện hoàn toàn trong ngữ cảnh\n",
-    "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                        continue\n",
-    "                    # Bỏ qua những câu trả lời với độ dài < 0 hoặc > max_answer_length\n",
-    "                    if (\n",
-    "                        end_index < start_index\n",
-    "                        or end_index - start_index + 1 > max_answer_length\n",
-    "                    ):\n",
-    "                        continue\n",
-    "\n",
-    "                    answer = {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                    answers.append(answer)\n",
-    "\n",
-    "        # Chọn câu trả lời có điểm cao nhất\n",
-    "        if len(answers) > 0:\n",
-    "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "            predicted_answers.append(\n",
-    "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
-    "            )\n",
-    "        else:\n",
-    "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
-    "\n",
-    "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
-    "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    \"bert-finetuned-squad\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=train_dataset,\n",
-    "    eval_dataset=validation_dataset,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 81.18259224219489, 'f1': 88.67381321905516}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions, _, _ = trainer.predict(validation_dataset)\n",
-    "start_logits, end_logits = predictions\n",
-    "compute_metrics(start_logits, end_logits, validation_dataset, raw_datasets[\"validation\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/bert-finetuned-squad/commit/9dcee1fbc25946a6ed4bb32efb1bd71d5fa90b68'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "train_dataset.set_format(\"torch\")\n",
-    "validation_set = validation_dataset.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "validation_set.set_format(\"torch\")\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    train_dataset,\n",
-    "    shuffle=True,\n",
-    "    collate_fn=default_data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    validation_set, collate_fn=default_data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator(fp16=True)\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/bert-finetuned-squad-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"bert-finetuned-squad-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"bert-finetuned-squad-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Huấn luyện\n",
-    "    model.train()\n",
-    "    for step, batch in enumerate(train_dataloader):\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Đánh giá\n",
-    "    model.eval()\n",
-    "    start_logits = []\n",
-    "    end_logits = []\n",
-    "    accelerator.print(\"Evaluation!\")\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        start_logits.append(accelerator.gather(outputs.start_logits).cpu().numpy())\n",
-    "        end_logits.append(accelerator.gather(outputs.end_logits).cpu().numpy())\n",
-    "\n",
-    "    start_logits = np.concatenate(start_logits)\n",
-    "    end_logits = np.concatenate(end_logits)\n",
-    "    start_logits = start_logits[: len(validation_dataset)]\n",
-    "    end_logits = end_logits[: len(validation_dataset)]\n",
-    "\n",
-    "    metrics = compute_metrics(\n",
-    "        start_logits, end_logits, validation_dataset, raw_datasets[\"validation\"]\n",
-    "    )\n",
-    "    print(f\"epoch {epoch}:\", metrics)\n",
-    "\n",
-    "    # Lưu và tải\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accelerator.wait_for_everyone()\n",
-    "unwrapped_model = accelerator.unwrap_model(model)\n",
-    "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.9979003071784973,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Thay thế nó với checkpoint của bạn\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-squad\"\n",
-    "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
-    "\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Hỏi đáp (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter7/section7_tf.ipynb b/course/vi/chapter7/section7_tf.ipynb
deleted file mode 100644
index d4e00391..00000000
--- a/course/vi/chapter7/section7_tf.ipynb
+++ /dev/null
@@ -1,1056 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hỏi đáp (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 87599\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 10570\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Context: 'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'\n",
-       "Question: 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'\n",
-       "Answer: {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n",
-    "print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n",
-    "print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "    num_rows: 0\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}\n",
-       "{'text': ['Santa Clara, California', \"Levi's Stadium\", \"Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.\"], 'answer_start': [403, 355, 355]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][0][\"answers\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \"Super Bowl L\"), so that the logo could prominently feature the Arabic numerals 50.'\n",
-       "'Where did Super Bowl 50 take place?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][2][\"context\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, '\n",
-       "'the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin '\n",
-       "'Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms '\n",
-       "'upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred '\n",
-       "'Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a '\n",
-       "'replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette '\n",
-       "'Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues '\n",
-       "'and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context = raw_datasets[\"train\"][0][\"context\"]\n",
-    "question = raw_datasets[\"train\"][0][\"question\"]\n",
-    "\n",
-    "inputs = tokenizer(question, context)\n",
-    "tokenizer.decode(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basi [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP]. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "inputs.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"overflow_to_sample_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The 4 examples gave 19 features.'\n",
-       "'Here is where each comes from: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3].'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][2:6][\"question\"],\n",
-    "    raw_datasets[\"train\"][2:6][\"context\"],\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n",
-    "print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([83, 51, 19, 0, 0, 64, 27, 0, 34, 0, 0, 0, 67, 34, 0, 0, 0, 0, 0],\n",
-       " [85, 53, 21, 0, 0, 70, 33, 0, 40, 0, 0, 0, 68, 35, 0, 0, 0, 0, 0])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "answers = raw_datasets[\"train\"][2:6][\"answers\"]\n",
-    "start_positions = []\n",
-    "end_positions = []\n",
-    "\n",
-    "for i, offset in enumerate(inputs[\"offset_mapping\"]):\n",
-    "    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n",
-    "    answer = answers[sample_idx]\n",
-    "    start_char = answer[\"answer_start\"][0]\n",
-    "    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "    sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "    # Tìm điểm bắt đầu và kết thúc của ngữ cảnh\n",
-    "    while sequence_ids[idx] != 1:\n",
-    "        idx += 1\n",
-    "    context_start = idx\n",
-    "    while sequence_ids[idx] == 1:\n",
-    "        idx += 1\n",
-    "    context_end = idx - 1\n",
-    "\n",
-    "    # Nếu câu trả lời không hoàn toàn nằm trong ngữ cảnh, nhãn là (0, 0)\n",
-    "    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "        start_positions.append(0)\n",
-    "        end_positions.append(0)\n",
-    "    else:\n",
-    "        # Nếu không nó sẽ là vị trí bắt đầu và kết thúc\n",
-    "        idx = context_start\n",
-    "        while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "            idx += 1\n",
-    "        start_positions.append(idx - 1)\n",
-    "\n",
-    "        idx = context_end\n",
-    "        while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "            idx -= 1\n",
-    "        end_positions.append(idx + 1)\n",
-    "\n",
-    "start_positions, end_positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: the Main Building, labels give: the Main Building'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "start = start_positions[idx]\n",
-    "end = end_positions[idx]\n",
-    "labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n",
-    "\n",
-    "print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: a Marian place of prayer and reflection, decoded example: [CLS] What is the Grotto at Notre Dame? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grot [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 4\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n",
-    "print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_length = 384\n",
-    "stride = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_training_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    answers = examples[\"answers\"]\n",
-    "    start_positions = []\n",
-    "    end_positions = []\n",
-    "\n",
-    "    for i, offset in enumerate(offset_mapping):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        answer = answers[sample_idx]\n",
-    "        start_char = answer[\"answer_start\"][0]\n",
-    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "        # Tìm điểm bắt đầu và kết thúc của ngữ cảnh\n",
-    "        idx = 0\n",
-    "        while sequence_ids[idx] != 1:\n",
-    "            idx += 1\n",
-    "        context_start = idx\n",
-    "        while sequence_ids[idx] == 1:\n",
-    "            idx += 1\n",
-    "        context_end = idx - 1\n",
-    "\n",
-    "        # Nếu câu trả lời không hoàn toàn nằm trong ngữ cảnh, nhãn là (0, 0)\n",
-    "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "            start_positions.append(0)\n",
-    "            end_positions.append(0)\n",
-    "        else:\n",
-    "            # Nếu không nó sẽ là vị trí token bắt đầu và kết thúc\n",
-    "            idx = context_start\n",
-    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "                idx += 1\n",
-    "            start_positions.append(idx - 1)\n",
-    "\n",
-    "            idx = context_end\n",
-    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "                idx -= 1\n",
-    "            end_positions.append(idx + 1)\n",
-    "\n",
-    "    inputs[\"start_positions\"] = start_positions\n",
-    "    inputs[\"end_positions\"] = end_positions\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(87599, 88729)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_dataset = raw_datasets[\"train\"].map(\n",
-    "    preprocess_training_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"train\"]), len(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess_validation_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    example_ids = []\n",
-    "\n",
-    "    for i in range(len(inputs[\"input_ids\"])):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        example_ids.append(examples[\"id\"][sample_idx])\n",
-    "\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "        offset = inputs[\"offset_mapping\"][i]\n",
-    "        inputs[\"offset_mapping\"][i] = [\n",
-    "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
-    "        ]\n",
-    "\n",
-    "    inputs[\"example_id\"] = example_ids\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10570, 10822)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation_dataset = raw_datasets[\"validation\"].map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"validation\"]), len(validation_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "small_eval_set = raw_datasets[\"validation\"].select(range(100))\n",
-    "trained_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n",
-    "eval_set = small_eval_set.map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import TFAutoModelForQuestionAnswering\n",
-    "\n",
-    "eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "eval_set_for_model.set_format(\"numpy\")\n",
-    "\n",
-    "batch = {k: eval_set_for_model[k] for k in eval_set_for_model.column_names}\n",
-    "trained_model = TFAutoModelForQuestionAnswering.from_pretrained(trained_checkpoint)\n",
-    "\n",
-    "outputs = trained_model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_logits = outputs.start_logits.numpy()\n",
-    "end_logits = outputs.end_logits.numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "\n",
-    "example_to_features = collections.defaultdict(list)\n",
-    "for idx, feature in enumerate(eval_set):\n",
-    "    example_to_features[feature[\"example_id\"]].append(idx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "n_best = 20\n",
-    "max_answer_length = 30\n",
-    "predicted_answers = []\n",
-    "\n",
-    "for example in small_eval_set:\n",
-    "    example_id = example[\"id\"]\n",
-    "    context = example[\"context\"]\n",
-    "    answers = []\n",
-    "\n",
-    "    for feature_index in example_to_features[example_id]:\n",
-    "        start_logit = start_logits[feature_index]\n",
-    "        end_logit = end_logits[feature_index]\n",
-    "        offsets = eval_set[\"offset_mapping\"][feature_index]\n",
-    "\n",
-    "        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        for start_index in start_indexes:\n",
-    "            for end_index in end_indexes:\n",
-    "                # Bỏ qua các câu trả lời không đầu đủ trong ngữ cảnh\n",
-    "                if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                    continue\n",
-    "                # Bỏ qua những câu trả lời có độ dài < 0 hoặc > max_answer_length.\n",
-    "                if (\n",
-    "                    end_index < start_index\n",
-    "                    or end_index - start_index + 1 > max_answer_length\n",
-    "                ):\n",
-    "                    continue\n",
-    "\n",
-    "                answers.append(\n",
-    "                    {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                )\n",
-    "\n",
-    "    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theoretical_answers = [\n",
-    "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\n",
-       "{'id': '56be4db0acb8001400a502ec', 'answers': {'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(predicted_answers[0])\n",
-    "print(theoretical_answers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "\n",
-    "def compute_metrics(start_logits, end_logits, features, examples):\n",
-    "    example_to_features = collections.defaultdict(list)\n",
-    "    for idx, feature in enumerate(features):\n",
-    "        example_to_features[feature[\"example_id\"]].append(idx)\n",
-    "\n",
-    "    predicted_answers = []\n",
-    "    for example in tqdm(examples):\n",
-    "        example_id = example[\"id\"]\n",
-    "        context = example[\"context\"]\n",
-    "        answers = []\n",
-    "\n",
-    "        # Lặp qua tất cả các đặc trưng liên quan tới mẫu đó\n",
-    "        for feature_index in example_to_features[example_id]:\n",
-    "            start_logit = start_logits[feature_index]\n",
-    "            end_logit = end_logits[feature_index]\n",
-    "            offsets = features[feature_index][\"offset_mapping\"]\n",
-    "\n",
-    "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            for start_index in start_indexes:\n",
-    "                for end_index in end_indexes:\n",
-    "                    # Bỏ qua câu trả lời không xuất hiện hoàn toàn trong ngữ cảnh\n",
-    "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                        continue\n",
-    "                    # Bỏ qua những câu trả lời với độ dài < 0 hoặc > max_answer_length\n",
-    "                    if (\n",
-    "                        end_index < start_index\n",
-    "                        or end_index - start_index + 1 > max_answer_length\n",
-    "                    ):\n",
-    "                        continue\n",
-    "\n",
-    "                    answer = {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                    answers.append(answer)\n",
-    "\n",
-    "        # Chọn câu trả lời có điểm cao nhất\n",
-    "        if len(answers) > 0:\n",
-    "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "            predicted_answers.append(\n",
-    "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
-    "            )\n",
-    "        else:\n",
-    "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
-    "\n",
-    "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
-    "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DefaultDataCollator\n",
-    "\n",
-    "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = train_dataset.to_tf_dataset(\n",
-    "    columns=[\n",
-    "        \"input_ids\",\n",
-    "        \"start_positions\",\n",
-    "        \"end_positions\",\n",
-    "        \"attention_mask\",\n",
-    "        \"token_type_ids\",\n",
-    "    ],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=16,\n",
-    ")\n",
-    "tf_eval_dataset = validation_dataset.to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Số bước huấn luyện là số lượng mẫu trong tập dữ liệu, chia cho kích thước lô sau đó nhân\n",
-    "# với tổng số epoch. Lưu ý rằng tf_train_dataset ở đây là tf.data.Dataset theo lô,\n",
-    "# không phải là Hugging Face Dataset ban đầu, vì vậy len() của nó vốn là num_samples // batch_size.\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_train_epochs\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Huấn luyện trong mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"bert-finetuned-squad\", tokenizer=tokenizer)\n",
-    "\n",
-    "# Chúng ta sẽ thực hiện kiểm định sau đó, vì vậy không có quá trình huấnlluyện giữa quá trình kiểm định\n",
-    "model.fit(tf_train_dataset, callbacks=[callback], epochs=num_train_epochs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 81.18259224219489, 'f1': 88.67381321905516}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = model.predict(tf_eval_dataset)\n",
-    "compute_metrics(\n",
-    "    predictions[\"start_logits\"],\n",
-    "    predictions[\"end_logits\"],\n",
-    "    validation_dataset,\n",
-    "    raw_datasets[\"validation\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.9979003071784973,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Thay thế nó với checkpoint của bạn\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-squad\"\n",
-    "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
-    "\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Hỏi đáp (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter8/section2.ipynb b/course/vi/chapter8/section2.ipynb
deleted file mode 100644
index b649a4d5..00000000
--- a/course/vi/chapter8/section2.ipynb
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Phải làm gì khi bạn gặp lỗi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Sao chép kho và trích xuất đường dẫn cục bộ\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Tạo ra một kho rỗng trên Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Sao chép kho rỗng\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Sao chép các tệp\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Đẩy lên Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.gitattributes', 'README.md', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer_config.json', 'training_args.bin', 'vocab.txt']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.38669535517692566,\n",
-       " 'start': 34,\n",
-       " 'end': 95,\n",
-       " 'answer': 'the task of extracting an answer from a text given a question'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "---------------------------------------------------------------------------\n",
-       "AttributeError                            Traceback (most recent call last)\n",
-       "/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in <module>\n",
-       "      1 inputs = tokenizer(question, text, add_special_tokens=True)\n",
-       "      2 input_ids = inputs[\"input_ids\"]\n",
-       "----> 3 outputs = model(**inputs)\n",
-       "      4 answer_start_scores = outputs.start_logits\n",
-       "      5 answer_end_scores = outputs.end_logits\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\n",
-       "    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
-       "    724\n",
-       "--> 725         distilbert_output = self.distilbert(\n",
-       "    726             input_ids=input_ids,\n",
-       "    727             attention_mask=attention_mask,\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\n",
-       "    471             raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n",
-       "    472         elif input_ids is not None:\n",
-       "--> 473             input_shape = input_ids.size()\n",
-       "    474         elif inputs_embeds is not None:\n",
-       "    475             input_shape = inputs_embeds.size()[:-1]\n",
-       "\n",
-       "AttributeError: 'list' object has no attribute 'size'\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Lấy phần có khả năng là bắt đầu của câu trả lời nhất với argmax của điểm trả về\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Lấy phần có khả năng là kết thúc của câu trả lời nhất với argmax của điểm trả về\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 2029, 7705, 2015, 2064]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Phải làm gì khi bạn gặp lỗi",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter8/section3.ipynb b/course/vi/chapter8/section3.ipynb
deleted file mode 100644
index f4b72a59..00000000
--- a/course/vi/chapter8/section3.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Yêu cầu trợ giúp trên diễn đàn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModel.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"\"\"\n",
-    "Generation One is a retroactive term for the Transformers characters that\n",
-    "appeared between 1984 and 1993. The Transformers began with the 1980s Japanese\n",
-    "toy lines Micro Change and Diaclone. They presented robots able to transform\n",
-    "into everyday vehicles, electronic items or weapons. Hasbro bought the Micro\n",
-    "Change and Diaclone toys, and partnered with Takara. Marvel Comics was hired by\n",
-    "Hasbro to create the backstory; editor-in-chief Jim Shooter wrote an overall\n",
-    "story, and gave the task of creating the characthers to writer Dennis O'Neil.\n",
-    "Unhappy with O'Neil's work (although O'Neil created the name \"Optimus Prime\"),\n",
-    "Shooter chose Bob Budiansky to create the characters.\n",
-    "\n",
-    "The Transformers mecha were largely designed by Shōji Kawamori, the creator of\n",
-    "the Japanese mecha anime franchise Macross (which was adapted into the Robotech\n",
-    "franchise in North America). Kawamori came up with the idea of transforming\n",
-    "mechs while working on the Diaclone and Macross franchises in the early 1980s\n",
-    "(such as the VF-1 Valkyrie in Macross and Robotech), with his Diaclone mechs\n",
-    "later providing the basis for Transformers.\n",
-    "\n",
-    "The primary concept of Generation One is that the heroic Optimus Prime, the\n",
-    "villainous Megatron, and their finest soldiers crash land on pre-historic Earth\n",
-    "in the Ark and the Nemesis before awakening in 1985, Cybertron hurtling through\n",
-    "the Neutral zone as an effect of the war. The Marvel comic was originally part\n",
-    "of the main Marvel Universe, with appearances from Spider-Man and Nick Fury,\n",
-    "plus some cameos, as well as a visit to the Savage Land.\n",
-    "\n",
-    "The Transformers TV series began around the same time. Produced by Sunbow\n",
-    "Productions and Marvel Productions, later Hasbro Productions, from the start it\n",
-    "contradicted Budiansky's backstories. The TV series shows the Autobots looking\n",
-    "for new energy sources, and crash landing as the Decepticons attack. Marvel\n",
-    "interpreted the Autobots as destroying a rogue asteroid approaching Cybertron.\n",
-    "Shockwave is loyal to Megatron in the TV series, keeping Cybertron in a\n",
-    "stalemate during his absence, but in the comic book he attempts to take command\n",
-    "of the Decepticons. The TV series would also differ wildly from the origins\n",
-    "Budiansky had created for the Dinobots, the Decepticon turned Autobot Jetfire\n",
-    "(known as Skyfire on TV), the Constructicons (who combine to form\n",
-    "Devastator),[19][20] and Omega Supreme. The Marvel comic establishes early on\n",
-    "that Prime wields the Creation Matrix, which gives life to machines. In the\n",
-    "second season, the two-part episode The Key to Vector Sigma introduced the\n",
-    "ancient Vector Sigma computer, which served the same original purpose as the\n",
-    "Creation Matrix (giving life to Transformers), and its guardian Alpha Trion.\n",
-    "\"\"\"\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "logits = model(**inputs).logits"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Yêu cầu trợ giúp trên diễn đàn",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter8/section4.ipynb b/course/vi/chapter8/section4.ipynb
deleted file mode 100644
index 87665f2b..00000000
--- a/course/vi/chapter8/section4.ipynb
+++ /dev/null
@@ -1,870 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gỡ lỗi quy trình huấn luyện"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: You have to specify either input_ids or inputs_embeds'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=raw_datasets[\"train\"],\n",
-    "    eval_dataset=raw_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'hypothesis': 'Product and geography are what make cream skimming work. ',\n",
-       " 'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'premise': 'Conceptually cream skimming has two basic dimensions - product and geography.'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: expected sequence of length 43 at dim 1 (got 37)'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] conceptually cream skimming has two basic dimensions - product and geography. [SEP] product and geography are what make cream skimming work. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(trainer.train_dataset[0][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'hypothesis', 'idx', 'input_ids', 'label', 'premise'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0].keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "transformers.models.distilbert.modeling_distilbert.DistilBertForSequenceClassification"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(trainer.model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0][\"attention_mask\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(trainer.train_dataset[0][\"attention_mask\"]) == len(\n",
-    "    trainer.train_dataset[0][\"input_ids\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0][\"label\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['entailment', 'neutral', 'contradiction']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset.features[\"label\"].names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/git/transformers/src/transformers/data/data_collator.py in torch_default_data_collator(features)\n",
-       "    105                 batch[k] = torch.stack([f[k] for f in features])\n",
-       "    106             else:\n",
-       "--> 107                 batch[k] = torch.tensor([f[k] for f in features])\n",
-       "    108 \n",
-       "    109     return batch\n",
-       "\n",
-       "ValueError: expected sequence of length 45 at dim 1 (got 76)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function transformers.data.data_collator.default_data_collator(features: List[InputDataClass], return_tensors='pt') -> Dict[str, Any]>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "data_collator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "batch = data_collator([trainer.train_dataset[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "actual_train_set = trainer._remove_unused_columns(trainer.train_dataset)\n",
-    "batch = data_collator([actual_train_set[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)\n",
-       "   2386         )\n",
-       "   2387     if dim == 2:\n",
-       "-> 2388         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "   2389     elif dim == 4:\n",
-       "   2390         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "\n",
-       "IndexError: Target 2 is out of bounds."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "outputs = trainer.model.to(device)(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = outputs.loss\n",
-    "loss.backward()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.create_optimizer()\n",
-    "trainer.optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Quá trình này sẽ mất nhiều thời gian và xảy ra lỗi, vì vậy bạn không nên chạy ô này\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_eval_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = outputs.logits.cpu().numpy()\n",
-    "labels = batch[\"labels\"].cpu().numpy()\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((8, 3), (8,))"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions.shape, labels.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.625}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "trainer.create_optimizer()\n",
-    "\n",
-    "for _ in range(20):\n",
-    "    outputs = trainer.model(**batch)\n",
-    "    loss = outputs.loss\n",
-    "    loss.backward()\n",
-    "    trainer.optimizer.step()\n",
-    "    trainer.optimizer.zero_grad()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 1.0}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)\n",
-    "preds = outputs.logits\n",
-    "labels = batch[\"labels\"]\n",
-    "\n",
-    "compute_metrics((preds.cpu().numpy(), labels.cpu().numpy()))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Gỡ lỗi quy trình huấn luyện",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter8/section4_tf.ipynb b/course/vi/chapter8/section4_tf.ipynb
deleted file mode 100644
index 342a53c7..00000000
--- a/course/vi/chapter8/section4_tf.ipynb
+++ /dev/null
@@ -1,443 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gỡ lỗi quy trình huấn luyện"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ValueError: No gradients provided for any variable: ['tf_distil_bert_for_sequence_classification/distilbert/embeddings/word_embeddings/weight:0', '...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "import evaluate\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    TFAutoModelForSequenceClassification,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "\n",
-    "train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "validation_dataset = tokenized_datasets[\"validation_matched\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
-    "\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        ...,\n",
-       "        [1, 1, 1, ..., 1, 1, 1],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0]])>,\n",
-       " 'label': <tf.Tensor: shape=(16,), dtype=int64, numpy=array([0, 2, 1, 2, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2, 1])>,\n",
-       " 'input_ids': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[ 101, 2174, 1010, ...,    0,    0,    0],\n",
-       "        [ 101, 3174, 2420, ...,    0,    0,    0],\n",
-       "        [ 101, 2044, 2048, ...,    0,    0,    0],\n",
-       "        ...,\n",
-       "        [ 101, 3398, 3398, ..., 2051, 2894,  102],\n",
-       "        [ 101, 1996, 4124, ...,    0,    0,    0],\n",
-       "        [ 101, 1999, 2070, ...,    0,    0,    0]])>}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  246/24543 [..............................] - ETA: 15:52 - loss: nan"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.compile(optimizer=\"adam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
-       "       nan, nan, nan], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([0.6844486 ,        nan,        nan, 0.67127866, 0.7068601 ,\n",
-       "              nan, 0.69309855,        nan, 0.65531296,        nan,\n",
-       "              nan,        nan, 0.675402  ,        nan,        nan,\n",
-       "       0.69831556], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[-0.04761693, -0.06509043],\n",
-       "       [-0.0481936 , -0.04556257],\n",
-       "       [-0.0040929 , -0.05848458],\n",
-       "       [-0.02417453, -0.0684005 ],\n",
-       "       [-0.02517801, -0.05241832],\n",
-       "       [-0.04514256, -0.0757378 ],\n",
-       "       [-0.02656011, -0.02646275],\n",
-       "       [ 0.00766164, -0.04350497],\n",
-       "       [ 0.02060014, -0.05655622],\n",
-       "       [-0.02615328, -0.0447021 ],\n",
-       "       [-0.05119278, -0.06928903],\n",
-       "       [-0.02859691, -0.04879177],\n",
-       "       [-0.02210129, -0.05791225],\n",
-       "       [-0.02363213, -0.05962167],\n",
-       "       [-0.05352269, -0.0481673 ],\n",
-       "       [-0.08141848, -0.07110836]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  2,  5,  7,  9, 10, 11, 13, 14])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "loss = model(batch).loss.numpy()\n",
-    "indices = np.flatnonzero(np.isnan(loss))\n",
-    "indices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  101,  2007,  2032,  2001,  1037, 16480,  3917,  2594,  4135,\n",
-       "        23212,  3070,  2214, 10170,  1010,  2012,  4356,  1997,  3183,\n",
-       "         6838, 12953,  2039,  2000,  1996,  6147,  1997,  2010,  2606,\n",
-       "         1012,   102,  6838,  2001,  3294,  6625,  3773,  1996,  2214,\n",
-       "         2158,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  6814,  2016,  2234,  2461,  2153,  1998, 13322,\n",
-       "         2009,  1012,   102,  2045,  1005,  1055,  2053,  3382,  2008,\n",
-       "         2016,  1005,  2222,  3046,  8103,  2075,  2009,  2153,  1012,\n",
-       "          102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  2007,  1996,  3712,  4634,  1010,  2057,  8108,\n",
-       "         2025,  3404,  2028,  1012,  1996,  2616, 18449,  2125,  1999,\n",
-       "         1037,  9666,  1997,  4100,  8663, 11020,  6313,  2791,  1998,\n",
-       "         2431,  1011,  4301,  1012,   102,  2028,  1005,  1055,  5177,\n",
-       "         2110,  1998,  3977,  2000,  2832,  2106,  2025,  2689,  2104,\n",
-       "         2122,  6214,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1045,  2001,  1999,  1037, 13090,  5948,  2007,  2048,\n",
-       "         2308,  2006,  2026,  5001,  2043,  2026,  2171,  2001,  2170,\n",
-       "         1012,   102,  1045,  2001,  3564,  1999,  2277,  1012,   102,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2195,  4279,  2191,  2039,  1996,  2181,  2124,  2004,\n",
-       "         1996,  2225,  7363,  1012,   102,  2045,  2003,  2069,  2028,\n",
-       "         2451,  1999,  1996,  2225,  7363,  1012,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2061,  2008,  1045,  2123,  1005,  1056,  2113,  2065,\n",
-       "         2009,  2428, 10654,  7347,  2030,  2009,  7126,  2256,  2495,\n",
-       "         2291,   102,  2009,  2003,  5094,  2256,  2495,  2291,  2035,\n",
-       "         2105,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2051,  1010,  2029,  3216,  2019,  2503,  3444,  1010,\n",
-       "         6732,  1996,  2265,  2038, 19840,  2098,  2125,  9906,  1998,\n",
-       "         2003,  2770,  2041,  1997,  4784,  1012,   102,  2051,  6732,\n",
-       "         1996,  2265,  2003,  9525,  1998,  4569,  1012,   102,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1996, 10556,  2140, 11515,  2058,  1010,  2010,  2162,\n",
-       "         2252,  5689,  2013,  2010,  7223,  1012,   102,  2043,  1996,\n",
-       "        10556,  2140, 11515,  2058,  1010,  2010,  2252,  3062,  2000,\n",
-       "         1996,  2598,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101, 13543,  1999,  2049,  6143,  2933,  2443,   102,  2025,\n",
-       "        13543,  1999,  6143,  2933,  2003,  2443,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "input_ids[indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model.compile(optimizer=Adam(5e-5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "319/24543 [..............................] - ETA: 16:07 - loss: 0.9718"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "tokenizer.decode(input_ids[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = batch[\"labels\"].numpy()\n",
-    "label = labels[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break\n",
-    "\n",
-    "# Đảm bảo rằng bạn đã chạy model.compile() và đặt trình tối ưu hóa của mình,\n",
-    "# và mất mát/chỉ số của bạn nếu bạn đang sử dụng chúng\n",
-    "\n",
-    "model.fit(batch, epochs=20)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Gỡ lỗi quy trình huấn luyện",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter8/section5.ipynb b/course/vi/chapter8/section5.ipynb
deleted file mode 100644
index dc6e403a..00000000
--- a/course/vi/chapter8/section5.ipynb
+++ /dev/null
@@ -1,42 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Làm thế nào để viết một vấn đề hay"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Làm thế nào để viết một vấn đề hay",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter8/section7.ipynb b/course/vi/chapter8/section7.ipynb
deleted file mode 100644
index f8e5b0db..00000000
--- a/course/vi/chapter8/section7.ipynb
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Đố vui cuối chương"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)\n",
-    "# ---------------------------------------------------------------------------\n",
-    "# ImportError                               Traceback (most recent call last)\n",
-    "# /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_30848/333858878.py in <module>\n",
-    "# ----> 1 from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Đố vui cuối chương",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter9/section2.ipynb b/course/vi/chapter9/section2.ipynb
deleted file mode 100644
index 0d2fb268..00000000
--- a/course/vi/chapter9/section2.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xây dựng bản demo đầu tiên của bạn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def greet(name):\n",
-    "    return \"Hello \" + name\n",
-    "\n",
-    "\n",
-    "demo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\")\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def greet(name):\n",
-    "    return \"Hello \" + name\n",
-    "\n",
-    "\n",
-    "# Chúng tôi khởi tạo lớp Textbox\n",
-    "textbox = gr.Textbox(label=\"Type your name here:\", placeholder=\"John Doe\", lines=2)\n",
-    "\n",
-    "gr.Interface(fn=greet, inputs=textbox, outputs=\"text\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model = pipeline(\"text-generation\")\n",
-    "\n",
-    "\n",
-    "def predict(prompt):\n",
-    "    completion = model(prompt)[0][\"generated_text\"]\n",
-    "    return completion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "gr.Interface(fn=predict, inputs=\"text\", outputs=\"text\").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Xây dựng bản demo đầu tiên của bạn",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter9/section3.ipynb b/course/vi/chapter9/section3.ipynb
deleted file mode 100644
index 3763dc7a..00000000
--- a/course/vi/chapter9/section3.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hiểu lớp Interface"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def reverse_audio(audio):\n",
-    "    sr, data = audio\n",
-    "    reversed_audio = (sr, np.flipud(data))\n",
-    "    return reversed_audio\n",
-    "\n",
-    "\n",
-    "mic = gr.Audio(source=\"microphone\", type=\"numpy\", label=\"Speak here...\")\n",
-    "gr.Interface(reverse_audio, mic, \"audio\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "notes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n",
-    "\n",
-    "\n",
-    "def generate_tone(note, octave, duration):\n",
-    "    sr = 48000\n",
-    "    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n",
-    "    frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n",
-    "    duration = int(duration)\n",
-    "    audio = np.linspace(0, duration, duration * sr)\n",
-    "    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n",
-    "    return (sr, audio)\n",
-    "\n",
-    "\n",
-    "gr.Interface(\n",
-    "    generate_tone,\n",
-    "    [\n",
-    "        gr.Dropdown(notes, type=\"index\"),\n",
-    "        gr.Slider(minimum=4, maximum=6, step=1),\n",
-    "        gr.Textbox(type=\"number\", value=1, label=\"Duration in seconds\"),\n",
-    "    ],\n",
-    "    \"audio\",\n",
-    ").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "import gradio as gr\n",
-    "\n",
-    "model = pipeline(\"automatic-speech-recognition\")\n",
-    "\n",
-    "\n",
-    "def transcribe_audio(mic=None, file=None):\n",
-    "    if mic is not None:\n",
-    "        audio = mic\n",
-    "    elif file is not None:\n",
-    "        audio = file\n",
-    "    else:\n",
-    "        return \"You must either provide a mic recording or a file\"\n",
-    "    transcription = model(audio)[\"text\"]\n",
-    "    return transcription\n",
-    "\n",
-    "\n",
-    "gr.Interface(\n",
-    "    fn=transcribe_audio,\n",
-    "    inputs=[\n",
-    "        gr.Audio(source=\"microphone\", type=\"filepath\", optional=True),\n",
-    "        gr.Audio(source=\"upload\", type=\"filepath\", optional=True),\n",
-    "    ],\n",
-    "    outputs=\"text\",\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Hiểu lớp Interface",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter9/section4.ipynb b/course/vi/chapter9/section4.ipynb
deleted file mode 100644
index 46f825c4..00000000
--- a/course/vi/chapter9/section4.ipynb
+++ /dev/null
@@ -1,131 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chia sẻ các bản demo với người khác"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "title = \"Ask Rick a Question\"\n",
-    "description = \"\"\"\n",
-    "The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!\n",
-    "<img src=\"https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png\" width=200px>\n",
-    "\"\"\"\n",
-    "\n",
-    "article = \"Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of.\"\n",
-    "\n",
-    "gr.Interface(\n",
-    "    fn=predict,\n",
-    "    inputs=\"textbox\",\n",
-    "    outputs=\"text\",\n",
-    "    title=title,\n",
-    "    description=description,\n",
-    "    article=article,\n",
-    "    examples=[[\"What are you doing?\"], [\"Where should we time travel to?\"]],\n",
-    ").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gr.Interface(classify_image, \"image\", \"label\").launch(share=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "import torch\n",
-    "import gradio as gr\n",
-    "from torch import nn\n",
-    "\n",
-    "LABELS = Path(\"class_names.txt\").read_text().splitlines()\n",
-    "\n",
-    "model = nn.Sequential(\n",
-    "    nn.Conv2d(1, 32, 3, padding=\"same\"),\n",
-    "    nn.ReLU(),\n",
-    "    nn.MaxPool2d(2),\n",
-    "    nn.Conv2d(32, 64, 3, padding=\"same\"),\n",
-    "    nn.ReLU(),\n",
-    "    nn.MaxPool2d(2),\n",
-    "    nn.Conv2d(64, 128, 3, padding=\"same\"),\n",
-    "    nn.ReLU(),\n",
-    "    nn.MaxPool2d(2),\n",
-    "    nn.Flatten(),\n",
-    "    nn.Linear(1152, 256),\n",
-    "    nn.ReLU(),\n",
-    "    nn.Linear(256, len(LABELS)),\n",
-    ")\n",
-    "state_dict = torch.load(\"pytorch_model.bin\", map_location=\"cpu\")\n",
-    "model.load_state_dict(state_dict, strict=False)\n",
-    "model.eval()\n",
-    "\n",
-    "\n",
-    "def predict(im):\n",
-    "    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0\n",
-    "    with torch.no_grad():\n",
-    "        out = model(x)\n",
-    "    probabilities = torch.nn.functional.softmax(out[0], dim=0)\n",
-    "    values, indices = torch.topk(probabilities, 5)\n",
-    "    return {LABELS[i]: v.item() for i, v in zip(indices, values)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "interface = gr.Interface(\n",
-    "    predict,\n",
-    "    inputs=\"sketchpad\",\n",
-    "    outputs=\"label\",\n",
-    "    theme=\"huggingface\",\n",
-    "    title=\"Sketch Recognition\",\n",
-    "    description=\"Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!\",\n",
-    "    article=\"<p style='text-align: center'>Sketch Recognition | Demo Model</p>\",\n",
-    "    live=True,\n",
-    ")\n",
-    "interface.launch(share=True)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Chia sẻ các bản demo với người khác",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter9/section5.ipynb b/course/vi/chapter9/section5.ipynb
deleted file mode 100644
index f156c23c..00000000
--- a/course/vi/chapter9/section5.ipynb
+++ /dev/null
@@ -1,83 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tích hợp với Hugging Face Hub"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "title = \"GPT-J-6B\"\n",
-    "description = \"Gradio Demo for GPT-J 6B, a transformer model trained using Ben Wang's Mesh Transformer JAX. 'GPT-J' refers to the class of model, while '6B' represents the number of trainable parameters. To use it, simply add your text, or click one of the examples to load them. Read more at the links below.\"\n",
-    "article = \"<p style='text-align: center'><a href='https://github.com/kingoflolz/mesh-transformer-jax' target='_blank'>GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model</a></p>\"\n",
-    "examples = [\n",
-    "    [\"The tower is 324 metres (1,063 ft) tall,\"],\n",
-    "    [\"The Moon's orbit around Earth has\"],\n",
-    "    [\"The smooth Borealis basin in the Northern Hemisphere covers 40%\"],\n",
-    "]\n",
-    "gr.Interface.load(\n",
-    "    \"huggingface/EleutherAI/gpt-j-6B\",\n",
-    "    inputs=gr.Textbox(lines=5, label=\"Input Text\"),\n",
-    "    title=title,\n",
-    "    description=description,\n",
-    "    article=article,\n",
-    "    examples=examples,\n",
-    "    enable_queue=True,\n",
-    ").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gr.Interface.load(\"spaces/abidlabs/remove-bg\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gr.Interface.load(\n",
-    "    \"spaces/abidlabs/remove-bg\", inputs=\"webcam\", title=\"Remove your webcam background!\"\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tích hợp với Hugging Face Hub",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter9/section6.ipynb b/course/vi/chapter9/section6.ipynb
deleted file mode 100644
index a3f3f424..00000000
--- a/course/vi/chapter9/section6.ipynb
+++ /dev/null
@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Các tính năng nâng cao của Interface"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def chat(message, history):\n",
-    "    history = history or []\n",
-    "    if message.startswith(\"How many\"):\n",
-    "        response = random.randint(1, 10)\n",
-    "    elif message.startswith(\"How\"):\n",
-    "        response = random.choice([\"Great\", \"Good\", \"Okay\", \"Bad\"])\n",
-    "    elif message.startswith(\"Where\"):\n",
-    "        response = random.choice([\"Here\", \"There\", \"Somewhere\"])\n",
-    "    else:\n",
-    "        response = \"I don't know\"\n",
-    "    history.append((message, response))\n",
-    "    return history, history\n",
-    "\n",
-    "\n",
-    "iface = gr.Interface(\n",
-    "    chat,\n",
-    "    [\"text\", \"state\"],\n",
-    "    [\"chatbot\", \"state\"],\n",
-    "    allow_screenshot=False,\n",
-    "    allow_flagging=\"never\",\n",
-    ")\n",
-    "iface.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "inception_net = tf.keras.applications.MobileNetV2()  # tải mô hình\n",
-    "\n",
-    "# Tải nhãn con người đọc được cho ImageNet.\n",
-    "response = requests.get(\"https://git.io/JJkYN\")\n",
-    "labels = response.text.split(\"\\n\")\n",
-    "\n",
-    "\n",
-    "def classify_image(inp):\n",
-    "    inp = inp.reshape((-1, 224, 224, 3))\n",
-    "    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n",
-    "    prediction = inception_net.predict(inp).flatten()\n",
-    "    return {labels[i]: float(prediction[i]) for i in range(1000)}\n",
-    "\n",
-    "\n",
-    "image = gr.Image(shape=(224, 224))\n",
-    "label = gr.Label(num_top_classes=3)\n",
-    "\n",
-    "title = \"Gradio Image Classifiction + Interpretation Example\"\n",
-    "gr.Interface(\n",
-    "    fn=classify_image, inputs=image, outputs=label, interpretation=\"default\", title=title\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Các tính năng nâng cao của Interface",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/vi/chapter9/section7.ipynb b/course/vi/chapter9/section7.ipynb
deleted file mode 100644
index 9d975fac..00000000
--- a/course/vi/chapter9/section7.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Giới thiệu về Gradio Blocks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def flip_text(x):\n",
-    "    return x[::-1]\n",
-    "\n",
-    "\n",
-    "demo = gr.Blocks()\n",
-    "\n",
-    "with demo:\n",
-    "    gr.Markdown(\n",
-    "        \"\"\"\n",
-    "    # Flip Text!\n",
-    "    Start typing below to see the output.\n",
-    "    \"\"\"\n",
-    "    )\n",
-    "    input = gr.Textbox(placeholder=\"Flip this text\")\n",
-    "    output = gr.Textbox()\n",
-    "\n",
-    "    input.change(fn=flip_text, inputs=input, outputs=output)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "demo = gr.Blocks()\n",
-    "\n",
-    "\n",
-    "def flip_text(x):\n",
-    "    return x[::-1]\n",
-    "\n",
-    "\n",
-    "def flip_image(x):\n",
-    "    return np.fliplr(x)\n",
-    "\n",
-    "\n",
-    "with demo:\n",
-    "    gr.Markdown(\"Flip text or image files using this demo.\")\n",
-    "    with gr.Tabs():\n",
-    "        with gr.TabItem(\"Flip Text\"):\n",
-    "            with gr.Row():\n",
-    "                text_input = gr.Textbox()\n",
-    "                text_output = gr.Textbox()\n",
-    "            text_button = gr.Button(\"Flip\")\n",
-    "        with gr.TabItem(\"Flip Image\"):\n",
-    "            with gr.Row():\n",
-    "                image_input = gr.Image()\n",
-    "                image_output = gr.Image()\n",
-    "            image_button = gr.Button(\"Flip\")\n",
-    "\n",
-    "    text_button.click(flip_text, inputs=text_input, outputs=text_output)\n",
-    "    image_button.click(flip_image, inputs=image_input, outputs=image_output)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "api = gr.Interface.load(\"huggingface/EleutherAI/gpt-j-6B\")\n",
-    "\n",
-    "\n",
-    "def complete_with_gpt(text):\n",
-    "    # Sử dụng 50 kí tự cuối của văn bản làm ngữ cảnh\n",
-    "    return text[:-50] + api(text[-50:])\n",
-    "\n",
-    "\n",
-    "with gr.Blocks() as demo:\n",
-    "    textbox = gr.Textbox(placeholder=\"Type here and press enter...\", lines=4)\n",
-    "    btn = gr.Button(\"Generate\")\n",
-    "\n",
-    "    btn.click(complete_with_gpt, textbox, textbox)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "asr = pipeline(\"automatic-speech-recognition\", \"facebook/wav2vec2-base-960h\")\n",
-    "classifier = pipeline(\"text-classification\")\n",
-    "\n",
-    "\n",
-    "def speech_to_text(speech):\n",
-    "    text = asr(speech)[\"text\"]\n",
-    "    return text\n",
-    "\n",
-    "\n",
-    "def text_to_sentiment(text):\n",
-    "    return classifier(text)[0][\"label\"]\n",
-    "\n",
-    "\n",
-    "demo = gr.Blocks()\n",
-    "\n",
-    "with demo:\n",
-    "    audio_file = gr.Audio(type=\"filepath\")\n",
-    "    text = gr.Textbox()\n",
-    "    label = gr.Label()\n",
-    "\n",
-    "    b1 = gr.Button(\"Recognize Speech\")\n",
-    "    b2 = gr.Button(\"Classify Sentiment\")\n",
-    "\n",
-    "    b1.click(speech_to_text, inputs=audio_file, outputs=text)\n",
-    "    b2.click(text_to_sentiment, inputs=text, outputs=label)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def change_textbox(choice):\n",
-    "    if choice == \"short\":\n",
-    "        return gr.Textbox.update(lines=2, visible=True)\n",
-    "    elif choice == \"long\":\n",
-    "        return gr.Textbox.update(lines=8, visible=True)\n",
-    "    else:\n",
-    "        return gr.Textbox.update(visible=False)\n",
-    "\n",
-    "\n",
-    "with gr.Blocks() as block:\n",
-    "    radio = gr.Radio(\n",
-    "        [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n",
-    "    )\n",
-    "    text = gr.Textbox(lines=2, interactive=True)\n",
-    "\n",
-    "    radio.change(fn=change_textbox, inputs=radio, outputs=text)\n",
-    "    block.launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Giới thiệu về Gradio Blocks",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter1/section10.ipynb b/course/zh-CN/chapter1/section10.ipynb
deleted file mode 100644
index 22c332d1..00000000
--- a/course/zh-CN/chapter1/section10.ipynb
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 章末小测验"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "filler = pipeline(\"fill-mask\", model=\"bert-base-cased\")\n",
-    "result = filler(\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "result = classifier(\"This is a course about the Transformers library\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "章末小测验",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter1/section3.ipynb b/course/zh-CN/chapter1/section3.ipynb
deleted file mode 100644
index fb8d84d1..00000000
--- a/course/zh-CN/chapter1/section3.ipynb
+++ /dev/null
@@ -1,324 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Transformers能做什么？"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "classifier(\n",
-    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence': 'This is a course about the Transformers library',\n",
-       " 'labels': ['education', 'business', 'politics'],\n",
-       " 'scores': [0.8445963859558105, 0.111976258456707, 0.043427448719739914]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"zero-shot-classification\")\n",
-    "classifier(\n",
-    "    \"This is a course about the Transformers library\",\n",
-    "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to understand and use '\n",
-       "                    'data flow and data interchange when handling user data. We '\n",
-       "                    'will be working with one or more of the most commonly used '\n",
-       "                    'data flows — data flows of various types, as seen by the '\n",
-       "                    'HTTP'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\")\n",
-    "generator(\"In this course, we will teach you how to\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'generated_text': 'In this course, we will teach you how to manipulate the world and '\n",
-       "                    'move your mental and physical capabilities to your advantage.'},\n",
-       " {'generated_text': 'In this course, we will teach you how to become an expert and '\n",
-       "                    'practice realtime, and with a hands on experience on both real '\n",
-       "                    'time and real'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
-    "generator(\n",
-    "    \"In this course, we will teach you how to\",\n",
-    "    max_length=30,\n",
-    "    num_return_sequences=2,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'sequence': 'This course will teach you all about mathematical models.',\n",
-       "  'score': 0.19619831442832947,\n",
-       "  'token': 30412,\n",
-       "  'token_str': ' mathematical'},\n",
-       " {'sequence': 'This course will teach you all about computational models.',\n",
-       "  'score': 0.04052725434303284,\n",
-       "  'token': 38163,\n",
-       "  'token_str': ' computational'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\")\n",
-    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.99816, 'word': 'Sylvain', 'start': 11, 'end': 18}, \n",
-       " {'entity_group': 'ORG', 'score': 0.97960, 'word': 'Hugging Face', 'start': 33, 'end': 45}, \n",
-       " {'entity_group': 'LOC', 'score': 0.99321, 'word': 'Brooklyn', 'start': 49, 'end': 57}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "ner = pipeline(\"ner\", grouped_entities=True)\n",
-    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.6385916471481323, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}\n",
-       "klyn\",\n",
-       ")\n"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "question_answerer(\n",
-    "    question=\"Where do I work?\",\n",
-    "    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'summary_text': ' America has changed dramatically during recent years . The '\n",
-       "                  'number of engineering graduates in the U.S. has declined in '\n",
-       "                  'traditional engineering disciplines such as mechanical, civil '\n",
-       "                  ', electrical, chemical, and aeronautical engineering . Rapidly '\n",
-       "                  'developing economies such as China and India, as well as other '\n",
-       "                  'industrial countries in Europe and Asia, continue to encourage '\n",
-       "                  'and advance engineering .'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "summarizer = pipeline(\"summarization\")\n",
-    "summarizer(\n",
-    "    \"\"\"\n",
-    "    America has changed dramatically during recent years. Not only has the number of \n",
-    "    graduates in traditional engineering disciplines such as mechanical, civil, \n",
-    "    electrical, chemical, and aeronautical engineering declined, but in most of \n",
-    "    the premier American universities engineering curricula now concentrate on \n",
-    "    and encourage largely the study of engineering science. As a result, there \n",
-    "    are declining offerings in engineering subjects dealing with infrastructure, \n",
-    "    the environment, and related issues, and greater concentration on high \n",
-    "    technology subjects, largely supporting increasingly complex scientific \n",
-    "    developments. While the latter is important, it should not be at the expense \n",
-    "    of more traditional engineering.\n",
-    "\n",
-    "    Rapidly developing economies such as China and India, as well as other \n",
-    "    industrial countries in Europe and Asia, continue to encourage and advance \n",
-    "    the teaching of engineering. Both China and India, respectively, graduate \n",
-    "    six and eight times as many traditional engineers as does the United States. \n",
-    "    Other industrial countries at minimum maintain their output, while America \n",
-    "    suffers an increasingly serious decline in the number of engineering graduates \n",
-    "    and a lack of well-educated engineers.\n",
-    "\"\"\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'This course is produced by Hugging Face.'}]\n"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
-    "translator(\"Ce cours est produit par Hugging Face.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Transformers能做什么？",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter1/section8.ipynb b/course/zh-CN/chapter1/section8.ipynb
deleted file mode 100644
index daeb7080..00000000
--- a/course/zh-CN/chapter1/section8.ipynb
+++ /dev/null
@@ -1,63 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 偏见和局限性"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['lawyer', 'carpenter', 'doctor', 'waiter', 'mechanic']\n",
-       "['nurse', 'waitress', 'teacher', 'maid', 'prostitute']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
-    "result = unmasker(\"This man works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])\n",
-    "\n",
-    "result = unmasker(\"This woman works as a [MASK].\")\n",
-    "print([r[\"token_str\"] for r in result])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "偏见和局限性",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section2_pt.ipynb b/course/zh-CN/chapter2/section2_pt.ipynb
deleted file mode 100644
index 40555c98..00000000
--- a/course/zh-CN/chapter2/section2_pt.ipynb
+++ /dev/null
@@ -1,241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 管道的内部 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': tensor([\n",
-       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
-       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "    ]), \n",
-       "    'attention_mask': tensor([\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "    ])\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 16, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-1.5607,  1.6123],\n",
-       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[4.0195e-02, 9.5980e-01],\n",
-       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "管道的内部 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section2_tf.ipynb b/course/zh-CN/chapter2/section2_tf.ipynb
deleted file mode 100644
index 5c71e74e..00000000
--- a/course/zh-CN/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 管道的内部 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "管道的内部 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section3_pt.ipynb b/course/zh-CN/chapter2/section3_pt.ipynb
deleted file mode 100644
index dedd5403..00000000
--- a/course/zh-CN/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 模型 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "模型 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section3_tf.ipynb b/course/zh-CN/chapter2/section3_tf.ipynb
deleted file mode 100644
index 07af78ad..00000000
--- a/course/zh-CN/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 模型 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "模型 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section4_pt.ipynb b/course/zh-CN/chapter2/section4_pt.ipynb
deleted file mode 100644
index 8e31832c..00000000
--- a/course/zh-CN/chapter2/section4_pt.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 标记器（Tokenizer） (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "标记器（Tokenizer） (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section4_tf.ipynb b/course/zh-CN/chapter2/section4_tf.ipynb
deleted file mode 100644
index 43d3d97e..00000000
--- a/course/zh-CN/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 标记器（Tokenizer） (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "标记器（Tokenizer） (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section5_pt.ipynb b/course/zh-CN/chapter2/section5_pt.ipynb
deleted file mode 100644
index f7eea846..00000000
--- a/course/zh-CN/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 处理多个序列 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "处理多个序列 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section5_tf.ipynb b/course/zh-CN/chapter2/section5_tf.ipynb
deleted file mode 100644
index 86fa8b48..00000000
--- a/course/zh-CN/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 处理多个序列 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "处理多个序列 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section6_pt.ipynb b/course/zh-CN/chapter2/section6_pt.ipynb
deleted file mode 100644
index 290f9c35..00000000
--- a/course/zh-CN/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 把它们放在一起 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Will pad the sequences up to the maximum sequence length\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Will pad the sequences up to the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Will pad the sequences up to the specified max length\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the specified max length\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "把它们放在一起 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section6_tf.ipynb b/course/zh-CN/chapter2/section6_tf.ipynb
deleted file mode 100644
index 22dfa38a..00000000
--- a/course/zh-CN/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 把它们放在一起 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Will pad the sequences up to the maximum sequence length\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Will pad the sequences up to the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Will pad the sequences up to the specified max length\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the specified max length\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "把它们放在一起 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section8_pt.ipynb b/course/zh-CN/chapter2/section8_pt.ipynb
deleted file mode 100644
index 4a6065e2..00000000
--- a/course/zh-CN/chapter2/section8_pt.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 章末小测验 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = AutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "章末小测验 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter2/section8_tf.ipynb b/course/zh-CN/chapter2/section8_tf.ipynb
deleted file mode 100644
index cd983ccd..00000000
--- a/course/zh-CN/chapter2/section8_tf.ipynb
+++ /dev/null
@@ -1,62 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 章末小测验 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "result = tokenizer.tokenize(\"Hello!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "model = TFAutoModel.from_pretrained(\"gpt2\")\n",
-    "\n",
-    "encoded = tokenizer(\"Hey!\", return_tensors=\"pt\")\n",
-    "result = model(**encoded)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "章末小测验 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter3/section2_pt.ipynb b/course/zh-CN/chapter3/section2_pt.ipynb
deleted file mode 100644
index 54e738dc..00000000
--- a/course/zh-CN/chapter3/section2_pt.ipynb
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 预处理数据 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# This is new\n",
-    "batch[\"labels\"] = torch.tensor([1, 1])\n",
-    "\n",
-    "optimizer = AdamW(model.parameters())\n",
-    "loss = model(**batch).loss\n",
-    "loss.backward()\n",
-    "optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
-       " 'token_type_ids': torch.Size([8, 67]),\n",
-       " 'labels': torch.Size([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "预处理数据 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter3/section2_tf.ipynb b/course/zh-CN/chapter3/section2_tf.ipynb
deleted file mode 100644
index b647e032..00000000
--- a/course/zh-CN/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 预处理数据 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "预处理数据 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter3/section3.ipynb b/course/zh-CN/chapter3/section3.ipynb
deleted file mode 100644
index 1750ae3e..00000000
--- a/course/zh-CN/chapter3/section3.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 Trainer API 或者 Keras 微调一个模型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "使用 Trainer API 或者 Keras 微调一个模型",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter3/section3_tf.ipynb b/course/zh-CN/chapter3/section3_tf.ipynb
deleted file mode 100644
index 8fba21de..00000000
--- a/course/zh-CN/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 Trainer API 或者 Keras 微调一个模型"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# 训练步数是数据集中的样本数除以batch size再乘以 epoch。\n",
-    "# 注意这里的tf_train_dataset是一个转化为batch后的 tf.data.Dataset，\n",
-    "# 不是原来的 Hugging Face Dataset，所以它的 len() 已经是 num_samples // batch_size。\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "使用 Trainer API 或者 Keras 微调一个模型",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter3/section4.ipynb b/course/zh-CN/chapter3/section4.ipynb
deleted file mode 100644
index 04821762..00000000
--- a/course/zh-CN/chapter3/section4.ipynb
+++ /dev/null
@@ -1,368 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 一个完成的训练过程"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
-    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "tokenized_datasets[\"train\"].column_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': torch.Size([8, 65]),\n",
-       " 'input_ids': torch.Size([8, 65]),\n",
-       " 'labels': torch.Size([8]),\n",
-       " 'token_type_ids': torch.Size([8, 65])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataloader:\n",
-    "    break\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0.5441, grad_fn=<NllLossBackward>) torch.Size([8, 2])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**batch)\n",
-    "print(outputs.loss, outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1377"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "print(num_training_steps)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cuda')"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8431372549019608, 'f1': 0.8907849829351535}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "model.eval()\n",
-    "for batch in eval_dataloader:\n",
-    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "    with torch.no_grad():\n",
-    "        outputs = model(**batch)\n",
-    "\n",
-    "    logits = outputs.logits\n",
-    "    predictions = torch.argmax(logits, dim=-1)\n",
-    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
-    "\n",
-    "metric.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "model.to(device)\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dataloader)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dataloader:\n",
-    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        loss.backward()\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
-    "\n",
-    "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
-    "    train_dataloader, eval_dataloader, model, optimizer\n",
-    ")\n",
-    "\n",
-    "num_epochs = 3\n",
-    "num_training_steps = num_epochs * len(train_dl)\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "model.train()\n",
-    "for epoch in range(num_epochs):\n",
-    "    for batch in train_dl:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import notebook_launcher\n",
-    "\n",
-    "notebook_launcher(training_function)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "一个完成的训练过程",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter4/section2_pt.ipynb b/course/zh-CN/chapter4/section2_pt.ipynb
deleted file mode 100644
index c9385c1b..00000000
--- a/course/zh-CN/chapter4/section2_pt.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用预训练的模型 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, CamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = CamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = AutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "使用预训练的模型 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter4/section2_tf.ipynb b/course/zh-CN/chapter4/section2_tf.ipynb
deleted file mode 100644
index 665d0f56..00000000
--- a/course/zh-CN/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用预训练的模型 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "使用预训练的模型 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter4/section3_pt.ipynb b/course/zh-CN/chapter4/section3_pt.ipynb
deleted file mode 100644
index adb30896..00000000
--- a/course/zh-CN/chapter4/section3_pt.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 分享预训练的模型 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", push_to_hub=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository creation and management\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # And some methods to retrieve/change information about the content\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = AutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "分享预训练的模型 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter4/section3_tf.ipynb b/course/zh-CN/chapter4/section3_tf.ipynb
deleted file mode 100644
index ac72c83e..00000000
--- a/course/zh-CN/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 分享预训练的模型 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository creation and management\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # And some methods to retrieve/change information about the content\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "分享预训练的模型 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter5/section2.ipynb b/course/zh-CN/chapter5/section2.ipynb
deleted file mode 100644
index 481baec9..00000000
--- a/course/zh-CN/chapter5/section2.ipynb
+++ /dev/null
@@ -1,181 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 如果我的数据集不在 Hub 上怎么办？"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz\n",
-    "!wget https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!gzip -dkv SQuAD_it-*.json.gz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=\"SQuAD_it-train.json\", field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    \"title\": \"Terremoto del Sichuan del 2008\",\n",
-       "    \"paragraphs\": [\n",
-       "        {\n",
-       "            \"context\": \"Il terremoto del Sichuan del 2008 o il terremoto...\",\n",
-       "            \"qas\": [\n",
-       "                {\n",
-       "                    \"answers\": [{\"answer_start\": 29, \"text\": \"2008\"}],\n",
-       "                    \"id\": \"56cdca7862d2951400fa6826\",\n",
-       "                    \"question\": \"In quale anno si è verificato il terremoto nel Sichuan?\",\n",
-       "                },\n",
-       "                ...\n",
-       "            ],\n",
-       "        },\n",
-       "        ...\n",
-       "    ],\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squad_it_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 442\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['title', 'paragraphs'],\n",
-       "        num_rows: 48\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json\", \"test\": \"SQuAD_it-test.json\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")\n",
-    "squad_it_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\"train\": \"SQuAD_it-train.json.gz\", \"test\": \"SQuAD_it-test.json.gz\"}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = \"https://github.com/crux82/squad-it/raw/master/\"\n",
-    "data_files = {\n",
-    "    \"train\": url + \"SQuAD_it-train.json.gz\",\n",
-    "    \"test\": url + \"SQuAD_it-test.json.gz\",\n",
-    "}\n",
-    "squad_it_dataset = load_dataset(\"json\", data_files=data_files, field=\"data\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "如果我的数据集不在 Hub 上怎么办？",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter5/section3.ipynb b/course/zh-CN/chapter5/section3.ipynb
deleted file mode 100644
index b7c06e33..00000000
--- a/course/zh-CN/chapter5/section3.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 是时候来学一下切片了"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"\n",
-    "!unzip drugsCom_raw.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n",
-    "# \\t is the tab character in Python\n",
-    "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Unnamed: 0': [87571, 178045, 80482],\n",
-       " 'drugName': ['Naproxen', 'Duloxetine', 'Mobic'],\n",
-       " 'condition': ['Gout, Acute', 'ibromyalgia', 'Inflammatory Conditions'],\n",
-       " 'review': ['\"like the previous person mention, I&#039;m a strong believer of aleve, it works faster for my gout than the prescription meds I take. No more going to the doctor for refills.....Aleve works!\"',\n",
-       "  '\"I have taken Cymbalta for about a year and a half for fibromyalgia pain. It is great\\r\\nas a pain reducer and an anti-depressant, however, the side effects outweighed \\r\\nany benefit I got from it. I had trouble with restlessness, being tired constantly,\\r\\ndizziness, dry mouth, numbness and tingling in my feet, and horrible sweating. I am\\r\\nbeing weaned off of it now. Went from 60 mg to 30mg and now to 15 mg. I will be\\r\\noff completely in about a week. The fibro pain is coming back, but I would rather deal with it than the side effects.\"',\n",
-       "  '\"I have been taking Mobic for over a year with no side effects other than an elevated blood pressure.  I had severe knee and ankle pain which completely went away after taking Mobic.  I attempted to stop the medication however pain returned after a few days.\"'],\n",
-       " 'rating': [9.0, 3.0, 10.0],\n",
-       " 'date': ['September 2, 2015', 'November 7, 2011', 'June 5, 2013'],\n",
-       " 'usefulCount': [36, 13, 128]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_sample = drug_dataset[\"train\"].shuffle(seed=42).select(range(1000))\n",
-    "# Peek at the first few examples\n",
-    "drug_sample[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split in drug_dataset.keys():\n",
-    "    assert len(drug_dataset[split]) == len(drug_dataset[split].unique(\"Unnamed: 0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 161297\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount'],\n",
-       "        num_rows: 53766\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.rename_column(\n",
-    "    original_column_name=\"Unnamed: 0\", new_column_name=\"patient_id\"\n",
-    ")\n",
-    "drug_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AttributeError: 'NoneType' object has no attribute 'lower'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def lowercase_condition(example):\n",
-    "    return {\"condition\": example[\"condition\"].lower()}\n",
-    "\n",
-    "\n",
-    "drug_dataset.map(lowercase_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_nones(x):\n",
-    "    return x[\"condition\"] is not None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda x: x * x)(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(lambda base, height: 0.5 * base * height)(4, 8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"condition\"] is not None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['left ventricular dysfunction', 'adhd', 'birth control']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(lowercase_condition)\n",
-    "# Check that lowercasing worked\n",
-    "drug_dataset[\"train\"][\"condition\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_review_length(example):\n",
-    "    return {\"review_length\": len(example[\"review\"].split())}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': 206461,\n",
-       " 'drugName': 'Valsartan',\n",
-       " 'condition': 'left ventricular dysfunction',\n",
-       " 'review': '\"It has no side effect, I take it in combination of Bystolic 5 Mg and Fish Oil\"',\n",
-       " 'rating': 9.0,\n",
-       " 'date': 'May 20, 2012',\n",
-       " 'usefulCount': 27,\n",
-       " 'review_length': 17}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.map(compute_review_length)\n",
-    "# Inspect the first training example\n",
-    "drug_dataset[\"train\"][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'patient_id': [103488, 23627, 20558],\n",
-       " 'drugName': ['Loestrin 21 1 / 20', 'Chlorzoxazone', 'Nucynta'],\n",
-       " 'condition': ['birth control', 'muscle spasm', 'pain'],\n",
-       " 'review': ['\"Excellent.\"', '\"useless\"', '\"ok\"'],\n",
-       " 'rating': [10.0, 1.0, 6.0],\n",
-       " 'date': ['November 4, 2008', 'March 24, 2017', 'August 20, 2016'],\n",
-       " 'usefulCount': [5, 2, 10],\n",
-       " 'review_length': [1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset[\"train\"].sort(\"review_length\")[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'train': 138514, 'test': 46108}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset = drug_dataset.filter(lambda x: x[\"review_length\"] > 30)\n",
-    "print(drug_dataset.num_rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"I'm a transformer called BERT\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import html\n",
-    "\n",
-    "text = \"I&#039;m a transformer called BERT\"\n",
-    "html.unescape(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset = drug_dataset.map(lambda x: {\"review\": html.unescape(x[\"review\"])})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_drug_dataset = drug_dataset.map(\n",
-    "    lambda x: {\"review\": [html.unescape(o) for o in x[\"review\"]]}, batched=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "\n",
-    "def tokenize_function(examples):\n",
-    "    return tokenizer(examples[\"review\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%time tokenized_dataset = drug_dataset.map(tokenize_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slow_tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\", use_fast=False)\n",
-    "\n",
-    "\n",
-    "def slow_tokenize_function(examples):\n",
-    "    return slow_tokenizer(examples[\"review\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_dataset = drug_dataset.map(slow_tokenize_function, batched=True, num_proc=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    return tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[128, 49]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = tokenize_and_split(drug_dataset[\"train\"][0])\n",
-    "[len(inp) for inp in result[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ArrowInvalid: Column 1 named condition expected length 1463 but got length 1000"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(\n",
-    "    tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(206772, 138514)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(tokenized_dataset[\"train\"]), len(drug_dataset[\"train\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_split(examples):\n",
-    "    result = tokenizer(\n",
-    "        examples[\"review\"],\n",
-    "        truncation=True,\n",
-    "        max_length=128,\n",
-    "        return_overflowing_tokens=True,\n",
-    "    )\n",
-    "    # Extract mapping between new and old indices\n",
-    "    sample_map = result.pop(\"overflow_to_sample_mapping\")\n",
-    "    for key, values in examples.items():\n",
-    "        result[key] = [values[i] for i in sample_map]\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 206772\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'condition', 'date', 'drugName', 'input_ids', 'patient_id', 'rating', 'review', 'review_length', 'token_type_ids', 'usefulCount'],\n",
-       "        num_rows: 68876\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)\n",
-    "tokenized_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.set_format(\"pandas\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset[\"train\"][:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_df = drug_dataset[\"train\"][:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "frequencies = (\n",
-    "    train_df[\"condition\"]\n",
-    "    .value_counts()\n",
-    "    .to_frame()\n",
-    "    .reset_index()\n",
-    "    .rename(columns={\"index\": \"condition\", \"condition\": \"frequency\"})\n",
-    ")\n",
-    "frequencies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['condition', 'frequency'],\n",
-       "    num_rows: 819\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "freq_dataset = Dataset.from_pandas(frequencies)\n",
-    "freq_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length', 'review_clean'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n",
-    "# Rename the default \"test\" split to \"validation\"\n",
-    "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n",
-    "# Add the \"test\" set to our `DatasetDict`\n",
-    "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n",
-    "drug_dataset_clean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drug_dataset_clean.save_to_disk(\"drug-reviews\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 110811\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 27703\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n",
-       "        num_rows: 46108\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_from_disk\n",
-    "\n",
-    "drug_dataset_reloaded = load_from_disk(\"drug-reviews\")\n",
-    "drug_dataset_reloaded"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for split, dataset in drug_dataset_clean.items():\n",
-    "    dataset.to_json(f\"drug-reviews-{split}.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\"patient_id\":141780,\"drugName\":\"Escitalopram\",\"condition\":\"depression\",\"review\":\"\\\"I seemed to experience the regular side effects of LEXAPRO, insomnia, low sex drive, sleepiness during the day. I am taking it at night because my doctor said if it made me tired to take it at night. I assumed it would and started out taking it at night. Strange dreams, some pleasant. I was diagnosed with fibromyalgia. Seems to be helping with the pain. Have had anxiety and depression in my family, and have tried quite a few other medications that haven't worked. Only have been on it for two weeks but feel more positive in my mind, want to accomplish more in my life. Hopefully the side effects will dwindle away, worth it to stick with it from hearing others responses. Great medication.\\\"\",\"rating\":9.0,\"date\":\"May 29, 2011\",\"usefulCount\":10,\"review_length\":125}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "!head -n 1 drug-reviews-train.jsonl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_files = {\n",
-    "    \"train\": \"drug-reviews-train.jsonl\",\n",
-    "    \"validation\": \"drug-reviews-validation.jsonl\",\n",
-    "    \"test\": \"drug-reviews-test.jsonl\",\n",
-    "}\n",
-    "drug_dataset_reloaded = load_dataset(\"json\", data_files=data_files)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "是时候来学一下切片了",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter5/section4.ipynb b/course/zh-CN/chapter5/section4.ipynb
deleted file mode 100644
index 7a1cc694..00000000
--- a/course/zh-CN/chapter5/section4.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 大数据? 🤗 Datasets 来救援!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install zstandard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['meta', 'text'],\n",
-       "    num_rows: 15518009\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# This takes a few minutes to run, so go grab a tea or coffee while you wait :)\n",
-    "data_files = \"https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst\"\n",
-    "pubmed_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "pubmed_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pubmed_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install psutil"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RAM used: 5678.33 MB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import psutil\n",
-    "\n",
-    "# Process.memory_info is expressed in bytes, so convert to megabytes\n",
-    "print(f\"RAM used: {psutil.Process().memory_info().rss / (1024 * 1024):.2f} MB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of files in dataset : 20979437051\n",
-       "Dataset size (cache file) : 19.54 GB"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(f\"Number of files in dataset : {pubmed_dataset.dataset_size}\")\n",
-    "size_gb = pubmed_dataset.dataset_size / (1024**3)\n",
-    "print(f\"Dataset size (cache file) : {size_gb:.2f} GB\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Iterated over 15518009 examples (about 19.5 GB) in 64.2s, i.e. 0.304 GB/s'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import timeit\n",
-    "\n",
-    "code_snippet = \"\"\"batch_size = 1000\n",
-    "\n",
-    "for idx in range(0, len(pubmed_dataset), batch_size):\n",
-    "    _ = pubmed_dataset[idx:idx + batch_size]\n",
-    "\"\"\"\n",
-    "\n",
-    "time = timeit.timeit(stmt=code_snippet, number=1, globals=globals())\n",
-    "print(\n",
-    "    f\"Iterated over {len(pubmed_dataset)} examples (about {size_gb:.1f} GB) in \"\n",
-    "    f\"{time:.1f}s, i.e. {size_gb/time:.3f} GB/s\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pubmed_dataset_streamed = load_dataset(\n",
-    "    \"json\", data_files=data_files, split=\"train\", streaming=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       " 'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection.\\nTo determine the prevalence of hypoxaemia in children aged under 5 years suffering acute lower respiratory infections (ALRI), the risk factors for hypoxaemia in children under 5 years of age with ALRI, and the association of hypoxaemia with an increased risk of dying in children of the same age ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iter(pubmed_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 4958, 5178, 4328, 6779, ...], 'attention_mask': [1, 1, 1, 1, 1, ...]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
-    "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"text\"]))\n",
-    "next(iter(tokenized_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pmid': 11410799, 'language': 'eng'},\n",
-       " 'text': 'Randomized study of dose or schedule modification of granulocyte colony-stimulating factor in platinum-based chemotherapy for elderly patients with lung cancer ...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=10_000, seed=42)\n",
-    "next(iter(shuffled_dataset))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'pmid': 11409575, 'language': 'eng'},\n",
-       "  'text': 'Clinical signs of hypoxaemia in children with acute lower respiratory infection: indicators of oxygen therapy ...'},\n",
-       " {'meta': {'pmid': 11409576, 'language': 'eng'},\n",
-       "  'text': \"Hypoxaemia in children with severe pneumonia in Papua New Guinea ...\"},\n",
-       " {'meta': {'pmid': 11409577, 'language': 'eng'},\n",
-       "  'text': 'Oxygen concentrators and cylinders ...'},\n",
-       " {'meta': {'pmid': 11409578, 'language': 'eng'},\n",
-       "  'text': 'Oxygen supply in rural africa: a personal experience ...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_head = pubmed_dataset_streamed.take(5)\n",
-    "list(dataset_head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Skip the first 1,000 examples and include the rest in the training set\n",
-    "train_dataset = shuffled_dataset.skip(1000)\n",
-    "# Take the first 1,000 examples for the validation set\n",
-    "validation_dataset = shuffled_dataset.take(1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'case_ID': '110921.json',\n",
-       "  'case_jurisdiction': 'scotus.tar.gz',\n",
-       "  'date_created': '2010-04-28T17:12:49Z'},\n",
-       " 'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "law_dataset_streamed = load_dataset(\n",
-    "    \"json\",\n",
-    "    data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\n",
-    "    split=\"train\",\n",
-    "    streaming=True,\n",
-    ")\n",
-    "next(iter(law_dataset_streamed))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'meta': {'pmid': 11409574, 'language': 'eng'},\n",
-       "  'text': 'Epidemiology of hypoxaemia in children with acute lower respiratory infection ...'},\n",
-       " {'meta': {'case_ID': '110921.json',\n",
-       "   'case_jurisdiction': 'scotus.tar.gz',\n",
-       "   'date_created': '2010-04-28T17:12:49Z'},\n",
-       "  'text': '\\n461 U.S. 238 (1983)\\nOLIM ET AL.\\nv.\\nWAKINEKONA\\nNo. 81-1581.\\nSupreme Court of United States.\\nArgued January 19, 1983.\\nDecided April 26, 1983.\\nCERTIORARI TO THE UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT\\n*239 Michael A. Lilly, First Deputy Attorney General of Hawaii, argued the cause for petitioners. With him on the brief was James H. Dannenberg, Deputy Attorney General...'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from itertools import islice\n",
-    "from datasets import interleave_datasets\n",
-    "\n",
-    "combined_dataset = interleave_datasets([pubmed_dataset_streamed, law_dataset_streamed])\n",
-    "list(islice(combined_dataset, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'meta': {'pile_set_name': 'Pile-CC'},\n",
-       " 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web...'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "base_url = \"https://the-eye.eu/public/AI/pile/\"\n",
-    "data_files = {\n",
-    "    \"train\": [base_url + \"train/\" + f\"{idx:02d}.jsonl.zst\" for idx in range(30)],\n",
-    "    \"validation\": base_url + \"val.jsonl.zst\",\n",
-    "    \"test\": base_url + \"test.jsonl.zst\",\n",
-    "}\n",
-    "pile_dataset = load_dataset(\"json\", data_files=data_files, streaming=True)\n",
-    "next(iter(pile_dataset[\"train\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "大数据? 🤗 Datasets 来救援!",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter5/section5.ipynb b/course/zh-CN/chapter5/section5.ipynb
deleted file mode 100644
index e3ceab02..00000000
--- a/course/zh-CN/chapter5/section5.ipynb
+++ /dev/null
@@ -1,524 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 创建自己的数据集"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install requests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n",
-    "response = requests.get(url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "200"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.status_code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'repository_url': 'https://api.github.com/repos/huggingface/datasets',\n",
-       "  'labels_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/labels{/name}',\n",
-       "  'comments_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/comments',\n",
-       "  'events_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792/events',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "  'id': 968650274,\n",
-       "  'node_id': 'MDExOlB1bGxSZXF1ZXN0NzEwNzUyMjc0',\n",
-       "  'number': 2792,\n",
-       "  'title': 'Update GooAQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'labels': [],\n",
-       "  'state': 'open',\n",
-       "  'locked': False,\n",
-       "  'assignee': None,\n",
-       "  'assignees': [],\n",
-       "  'milestone': None,\n",
-       "  'comments': 1,\n",
-       "  'created_at': '2021-08-12T11:40:18Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'closed_at': None,\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'active_lock_reason': None,\n",
-       "  'pull_request': {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/2792',\n",
-       "   'html_url': 'https://github.com/huggingface/datasets/pull/2792',\n",
-       "   'diff_url': 'https://github.com/huggingface/datasets/pull/2792.diff',\n",
-       "   'patch_url': 'https://github.com/huggingface/datasets/pull/2792.patch'},\n",
-       "  'body': '[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.',\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "GITHUB_TOKEN = xxx  # Copy your GitHub token here\n",
-    "headers = {\"Authorization\": f\"token {GITHUB_TOKEN}\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import math\n",
-    "from pathlib import Path\n",
-    "import pandas as pd\n",
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "\n",
-    "def fetch_issues(\n",
-    "    owner=\"huggingface\",\n",
-    "    repo=\"datasets\",\n",
-    "    num_issues=10_000,\n",
-    "    rate_limit=5_000,\n",
-    "    issues_path=Path(\".\"),\n",
-    "):\n",
-    "    if not issues_path.is_dir():\n",
-    "        issues_path.mkdir(exist_ok=True)\n",
-    "\n",
-    "    batch = []\n",
-    "    all_issues = []\n",
-    "    per_page = 100  # Number of issues to return per page\n",
-    "    num_pages = math.ceil(num_issues / per_page)\n",
-    "    base_url = \"https://api.github.com/repos\"\n",
-    "\n",
-    "    for page in tqdm(range(num_pages)):\n",
-    "        # Query with state=all to get both open and closed issues\n",
-    "        query = f\"issues?page={page}&per_page={per_page}&state=all\"\n",
-    "        issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n",
-    "        batch.extend(issues.json())\n",
-    "\n",
-    "        if len(batch) > rate_limit and len(all_issues) < num_issues:\n",
-    "            all_issues.extend(batch)\n",
-    "            batch = []  # Flush batch for next time period\n",
-    "            print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n",
-    "            time.sleep(60 * 60 + 1)\n",
-    "\n",
-    "    all_issues.extend(batch)\n",
-    "    df = pd.DataFrame.from_records(all_issues)\n",
-    "    df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n",
-    "    print(\n",
-    "        f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Depending on your internet connection, this can take several minutes to run...\n",
-    "fetch_issues()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'timeline_url', 'performed_via_github_app'],\n",
-       "    num_rows: 3019\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = load_dataset(\"json\", data_files=\"datasets-issues.jsonl\", split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">> URL: https://github.com/huggingface/datasets/pull/850\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/850', 'html_url': 'https://github.com/huggingface/datasets/pull/850', 'diff_url': 'https://github.com/huggingface/datasets/pull/850.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/850.patch'}\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/issues/2773\n",
-       ">> Pull request: None\n",
-       "\n",
-       ">> URL: https://github.com/huggingface/datasets/pull/783\n",
-       ">> Pull request: {'url': 'https://api.github.com/repos/huggingface/datasets/pulls/783', 'html_url': 'https://github.com/huggingface/datasets/pull/783', 'diff_url': 'https://github.com/huggingface/datasets/pull/783.diff', 'patch_url': 'https://github.com/huggingface/datasets/pull/783.patch'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = issues_dataset.shuffle(seed=666).select(range(3))\n",
-    "\n",
-    "# Print out the URL and pull request entries\n",
-    "for url, pr in zip(sample[\"html_url\"], sample[\"pull_request\"]):\n",
-    "    print(f\">> URL: {url}\")\n",
-    "    print(f\">> Pull request: {pr}\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"is_pull_request\": False if x[\"pull_request\"] is None else True}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'url': 'https://api.github.com/repos/huggingface/datasets/issues/comments/897594128',\n",
-       "  'html_url': 'https://github.com/huggingface/datasets/pull/2792#issuecomment-897594128',\n",
-       "  'issue_url': 'https://api.github.com/repos/huggingface/datasets/issues/2792',\n",
-       "  'id': 897594128,\n",
-       "  'node_id': 'IC_kwDODunzps41gDMQ',\n",
-       "  'user': {'login': 'bhavitvyamalik',\n",
-       "   'id': 19718818,\n",
-       "   'node_id': 'MDQ6VXNlcjE5NzE4ODE4',\n",
-       "   'avatar_url': 'https://avatars.githubusercontent.com/u/19718818?v=4',\n",
-       "   'gravatar_id': '',\n",
-       "   'url': 'https://api.github.com/users/bhavitvyamalik',\n",
-       "   'html_url': 'https://github.com/bhavitvyamalik',\n",
-       "   'followers_url': 'https://api.github.com/users/bhavitvyamalik/followers',\n",
-       "   'following_url': 'https://api.github.com/users/bhavitvyamalik/following{/other_user}',\n",
-       "   'gists_url': 'https://api.github.com/users/bhavitvyamalik/gists{/gist_id}',\n",
-       "   'starred_url': 'https://api.github.com/users/bhavitvyamalik/starred{/owner}{/repo}',\n",
-       "   'subscriptions_url': 'https://api.github.com/users/bhavitvyamalik/subscriptions',\n",
-       "   'organizations_url': 'https://api.github.com/users/bhavitvyamalik/orgs',\n",
-       "   'repos_url': 'https://api.github.com/users/bhavitvyamalik/repos',\n",
-       "   'events_url': 'https://api.github.com/users/bhavitvyamalik/events{/privacy}',\n",
-       "   'received_events_url': 'https://api.github.com/users/bhavitvyamalik/received_events',\n",
-       "   'type': 'User',\n",
-       "   'site_admin': False},\n",
-       "  'created_at': '2021-08-12T12:21:52Z',\n",
-       "  'updated_at': '2021-08-12T12:31:17Z',\n",
-       "  'author_association': 'CONTRIBUTOR',\n",
-       "  'body': \"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n",
-       "  'performed_via_github_app': None}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issue_number = 2792\n",
-    "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "response = requests.get(url, headers=headers)\n",
-    "response.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n    def test_load_dataset(self, dataset_name):\\r\\n        configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n>       self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n    self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE   AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\"]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def get_comments(issue_number):\n",
-    "    url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n",
-    "    response = requests.get(url, headers=headers)\n",
-    "    return [r[\"body\"] for r in response.json()]\n",
-    "\n",
-    "\n",
-    "# Test our function works as expected\n",
-    "get_comments(2792)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Depending on your internet connection, this can take a few minutes...\n",
-    "issues_with_comments_dataset = issues_dataset.map(\n",
-    "    lambda x: {\"comments\": get_comments(x[\"number\"])}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_with_comments_dataset.to_json(\"issues-datasets-with-comments.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Number of datasets on Hub: 1487\n",
-       "Dataset Name: acronym_identification, Tags: ['annotations_creators:expert-generated', 'language_creators:found', 'languages:en', 'licenses:mit', 'multilinguality:monolingual', 'size_categories:10K<n<100K', 'source_datasets:original', 'task_categories:structure-prediction', 'task_ids:structure-prediction-other-acronym-identification']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_datasets\n",
-    "\n",
-    "all_datasets = list_datasets()\n",
-    "print(f\"Number of datasets on Hub: {len(all_datasets)}\")\n",
-    "print(all_datasets[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/datasets/lewtun/github-issues'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "repo_url = create_repo(name=\"github-issues\", repo_type=\"dataset\")\n",
-    "repo_url"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)\n",
-    "!cp datasets-issues-with-comments.jsonl github-issues/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.lfs_track(\"*.jsonl\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "remote_dataset = load_dataset(\"lewtun/github-issues\", split=\"train\")\n",
-    "remote_dataset"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "创建自己的数据集",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter5/section6_pt.ipynb b/course/zh-CN/chapter5/section6_pt.ipynb
deleted file mode 100644
index a0ea305b..00000000
--- a/course/zh-CN/chapter5/section6_pt.ipynb
+++ /dev/null
@@ -1,518 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 FAISS 进行语义搜索 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = AutoModel.from_pretrained(model_ckpt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"pt\"\n",
-    "    )\n",
-    "    encoded_input = {k: v.to(device) for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).detach().cpu().numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "使用 FAISS 进行语义搜索 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter5/section6_tf.ipynb b/course/zh-CN/chapter5/section6_tf.ipynb
deleted file mode 100644
index 254be80e..00000000
--- a/course/zh-CN/chapter5/section6_tf.ipynb
+++ /dev/null
@@ -1,506 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 使用 FAISS 进行语义搜索 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install faiss-gpu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import hf_hub_url\n",
-    "\n",
-    "data_files = hf_hub_url(\n",
-    "    repo_id=\"lewtun/github-issues\",\n",
-    "    filename=\"datasets-issues-with-comments.jsonl\",\n",
-    "    repo_type=\"dataset\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 2855\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'pull_request', 'body', 'performed_via_github_app', 'is_pull_request'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "issues_dataset = issues_dataset.filter(\n",
-    "    lambda x: (x[\"is_pull_request\"] == False and len(x[\"comments\"]) > 0)\n",
-    ")\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 771\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "columns = issues_dataset.column_names\n",
-    "columns_to_keep = [\"title\", \"body\", \"html_url\", \"comments\"]\n",
-    "columns_to_remove = set(columns_to_keep).symmetric_difference(columns)\n",
-    "issues_dataset = issues_dataset.remove_columns(columns_to_remove)\n",
-    "issues_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "issues_dataset.set_format(\"pandas\")\n",
-    "df = issues_dataset[:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['the bug code locate in ：\\r\\n    if data_args.task_name is not None:\\r\\n        # Downloading and loading a dataset from the hub.\\r\\n        datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)',\n",
-       " 'Hi @jinec,\\r\\n\\r\\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\\r\\n\\r\\nNormally, it should work if you wait a little and then retry.\\r\\n\\r\\nCould you please confirm if the problem persists?',\n",
-       " 'cannot connect，even by Web browser，please check that  there is some  problems。',\n",
-       " 'I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"comments\"][0].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_df = df.explode(\"comments\", ignore_index=True)\n",
-    "comments_df.head(4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body'],\n",
-       "    num_rows: 2842\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import Dataset\n",
-    "\n",
-    "comments_dataset = Dataset.from_pandas(comments_df)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comments_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"comment_length\": len(x[\"comments\"].split())}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['html_url', 'title', 'comments', 'body', 'comment_length'],\n",
-       "    num_rows: 2098\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments_dataset = comments_dataset.filter(lambda x: x[\"comment_length\"] > 15)\n",
-    "comments_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concatenate_text(examples):\n",
-    "    return {\n",
-    "        \"text\": examples[\"title\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"body\"]\n",
-    "        + \" \\n \"\n",
-    "        + examples[\"comments\"]\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "comments_dataset = comments_dataset.map(concatenate_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModel\n",
-    "\n",
-    "model_ckpt = \"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
-    "model = TFAutoModel.from_pretrained(model_ckpt, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def cls_pooling(model_output):\n",
-    "    return model_output.last_hidden_state[:, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_embeddings(text_list):\n",
-    "    encoded_input = tokenizer(\n",
-    "        text_list, padding=True, truncation=True, return_tensors=\"tf\"\n",
-    "    )\n",
-    "    encoded_input = {k: v for k, v in encoded_input.items()}\n",
-    "    model_output = model(**encoded_input)\n",
-    "    return cls_pooling(model_output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([1, 768])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding = get_embeddings(comments_dataset[\"text\"][0])\n",
-    "embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset = comments_dataset.map(\n",
-    "    lambda x: {\"embeddings\": get_embeddings(x[\"text\"]).numpy()[0]}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings_dataset.add_faiss_index(column=\"embeddings\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"How can I load a dataset offline?\"\n",
-    "question_embedding = get_embeddings([question]).numpy()\n",
-    "question_embedding.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores, samples = embeddings_dataset.get_nearest_examples(\n",
-    "    \"embeddings\", question_embedding, k=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "samples_df = pd.DataFrame.from_dict(samples)\n",
-    "samples_df[\"scores\"] = scores\n",
-    "samples_df.sort_values(\"scores\", ascending=False, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "COMMENT: Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\n",
-       "\n",
-       "@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "SCORE: 25.505046844482422\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\n",
-       "You can now use them offline\n",
-       "\\`\\`\\`python\n",
-       "datasets = load_dataset(\"text\", data_files=data_files)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "We'll do a new release soon\n",
-       "SCORE: 24.555509567260742\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\n",
-       "\n",
-       "Let me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :)\n",
-       "\n",
-       "I already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\n",
-       "\n",
-       "Indeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\n",
-       "For example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\n",
-       "\\`\\`\\`python\n",
-       "load_dataset(\"./my_dataset\")\n",
-       "\\`\\`\\`\n",
-       "and the dataset script will generate your dataset once and for all.\n",
-       "\n",
-       "----------\n",
-       "\n",
-       "About I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\n",
-       "cf #1724\n",
-       "SCORE: 24.14896583557129\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: > here is my way to load a dataset offline, but it **requires** an online machine\n",
-       ">\n",
-       "> 1. (online machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_dataset(...)\n",
-       ">\n",
-       "> data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> 2. copy the dir from online to the offline machine\n",
-       ">\n",
-       "> 3. (offline machine)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       "> import datasets\n",
-       ">\n",
-       "> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       ">\n",
-       "> ```\n",
-       ">\n",
-       ">\n",
-       ">\n",
-       "> HTH.\n",
-       "\n",
-       "\n",
-       "SCORE: 22.893993377685547\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\n",
-       "COMMENT: here is my way to load a dataset offline, but it **requires** an online machine\n",
-       "1. (online machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_dataset(...)\n",
-       "data.save_to_disk(/YOUR/DATASET/DIR)\n",
-       "\\`\\`\\`\n",
-       "2. copy the dir from online to the offline machine\n",
-       "3. (offline machine)\n",
-       "\\`\\`\\`\n",
-       "import datasets\n",
-       "data = datasets.load_from_disk(/SAVED/DATA/DIR)\n",
-       "\\`\\`\\`\n",
-       "\n",
-       "HTH.\n",
-       "SCORE: 22.406635284423828\n",
-       "TITLE: Discussion using datasets in offline mode\n",
-       "URL: https://github.com/huggingface/datasets/issues/824\n",
-       "==================================================\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for _, row in samples_df.iterrows():\n",
-    "    print(f\"COMMENT: {row.comments}\")\n",
-    "    print(f\"SCORE: {row.scores}\")\n",
-    "    print(f\"TITLE: {row.title}\")\n",
-    "    print(f\"URL: {row.html_url}\")\n",
-    "    print(\"=\" * 50)\n",
-    "    print()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "使用 FAISS 进行语义搜索 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter5/section8.ipynb b/course/zh-CN/chapter5/section8.ipynb
deleted file mode 100644
index 2ee82116..00000000
--- a/course/zh-CN/chapter5/section8.ipynb
+++ /dev/null
@@ -1,58 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 章末小测验"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"allocine\", streaming=True, split=\"train\")\n",
-    "dataset[0]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "章末小测验",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section2.ipynb b/course/zh-CN/chapter6/section2.ipynb
deleted file mode 100644
index 4ecfd3b5..00000000
--- a/course/zh-CN/chapter6/section2.ipynb
+++ /dev/null
@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 根据已有的tokenizer训练新的tokenizer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "# This can take a few minutes to load, so grab a coffee or tea while you wait!\n",
-    "raw_datasets = load_dataset(\"code_search_net\", \"python\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['repository_name', 'func_path_in_repository', 'func_name', 'whole_func_string', 'language', \n",
-       "      'func_code_string', 'func_code_tokens', 'func_documentation_string', 'func_documentation_tokens', 'split_name', \n",
-       "      'func_code_url'\n",
-       "    ],\n",
-       "    num_rows: 412178\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(raw_datasets[\"train\"][123456][\"whole_func_string\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Don't uncomment the following line unless your dataset is small!\n",
-    "# training_corpus = [raw_datasets[\"train\"][i: i + 1000][\"whole_func_string\"] for i in range(0, len(raw_datasets[\"train\"]), 1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_corpus = (\n",
-    "    raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "    for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
-       "[]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen = (i for i in range(10))\n",
-    "print(list(gen))\n",
-    "print(list(gen))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    return (\n",
-    "        raw_datasets[\"train\"][i : i + 1000][\"whole_func_string\"]\n",
-    "        for i in range(0, len(raw_datasets[\"train\"]), 1000)\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "training_corpus = get_training_corpus()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_training_corpus():\n",
-    "    dataset = raw_datasets[\"train\"]\n",
-    "    for start_idx in range(0, len(dataset), 1000):\n",
-    "        samples = dataset[start_idx : start_idx + 1000]\n",
-    "        yield samples[\"whole_func_string\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "old_tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'n', 'umbers', '(', 'a', ',', 'Ġb', '):', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo',\n",
-       " 'Ġnumbers', 'Ġ`', 'a', '`', 'Ġand', 'Ġ`', 'b', '`', '.\"', '\"\"', 'Ċ', 'Ġ', 'Ġ', 'Ġ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = '''def add_numbers(a, b):\n",
-    "    \"\"\"Add the two numbers `a` and `b`.\"\"\"\n",
-    "    return a + b'''\n",
-    "\n",
-    "tokens = old_tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['def', 'Ġadd', '_', 'numbers', '(', 'a', ',', 'Ġb', '):', 'ĊĠĠĠ', 'Ġ\"\"\"', 'Add', 'Ġthe', 'Ġtwo', 'Ġnumbers', 'Ġ`',\n",
-       " 'a', '`', 'Ġand', 'Ġ`', 'b', '`.\"\"\"', 'ĊĠĠĠ', 'Ġreturn', 'Ġa', 'Ġ+', 'Ġb']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokens = tokenizer.tokenize(example)\n",
-    "tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "27\n",
-       "36"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(len(tokens))\n",
-    "print(len(old_tokenizer.tokenize(example)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['class', 'ĠLinear', 'Layer', '():', 'ĊĠĠĠ', 'Ġdef', 'Ġ__', 'init', '__(', 'self', ',', 'Ġinput', '_', 'size', ',',\n",
-       " 'Ġoutput', '_', 'size', '):', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'weight', 'Ġ=', 'Ġtorch', '.', 'randn', '(', 'input', '_',\n",
-       " 'size', ',', 'Ġoutput', '_', 'size', ')', 'ĊĠĠĠĠĠĠĠ', 'Ġself', '.', 'bias', 'Ġ=', 'Ġtorch', '.', 'zeros', '(',\n",
-       " 'output', '_', 'size', ')', 'ĊĊĠĠĠ', 'Ġdef', 'Ġ__', 'call', '__(', 'self', ',', 'Ġx', '):', 'ĊĠĠĠĠĠĠĠ',\n",
-       " 'Ġreturn', 'Ġx', 'Ġ@', 'Ġself', '.', 'weights', 'Ġ+', 'Ġself', '.', 'bias', 'ĊĠĠĠĠ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example = \"\"\"class LinearLayer():\n",
-    "    def __init__(self, input_size, output_size):\n",
-    "        self.weight = torch.randn(input_size, output_size)\n",
-    "        self.bias = torch.zeros(output_size)\n",
-    "\n",
-    "    def __call__(self, x):\n",
-    "        return x @ self.weights + self.bias\n",
-    "    \"\"\"\n",
-    "tokenizer.tokenize(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"code-search-net-tokenizer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Replace \"huggingface-course\" below with your actual namespace to use your own tokenizer\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "根据已有的tokenizer训练新的tokenizer",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section3_pt.ipynb b/course/zh-CN/chapter6/section3_pt.ipynb
deleted file mode 100644
index e2df12cc..00000000
--- a/course/zh-CN/chapter6/section3_pt.ipynb
+++ /dev/null
@@ -1,515 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 快速标记器的特殊能力 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"pt\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 19])\n",
-       "torch.Size([1, 19, 9])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()\n",
-    "predictions = outputs.logits.argmax(dim=-1)[0].tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Remove the B- or I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Grab all the tokens labeled with I-label\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # The score is the mean of all the scores of the tokens in that grouped entity\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "快速标记器的特殊能力 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section3_tf.ipynb b/course/zh-CN/chapter6/section3_tf.ipynb
deleted file mode 100644
index 90a2c712..00000000
--- a/course/zh-CN/chapter6/section3_tf.ipynb
+++ /dev/null
@@ -1,517 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 快速标记器的特殊能力 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'transformers.tokenization_utils_base.BatchEncoding'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "encoding = tokenizer(example)\n",
-    "print(type(encoding))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'My', 'name', 'is', 'S', '##yl', '##va', '##in', 'and', 'I', 'work', 'at', 'Hu', '##gging', 'Face', 'in',\n",
-       " 'Brooklyn', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 9, 10, 11, 12, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sylvain"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start, end = encoding.word_to_chars(3)\n",
-    "example[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "token_classifier = pipeline(\"token-classification\", aggregation_strategy=\"simple\")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForTokenClassification\n",
-    "\n",
-    "model_checkpoint = \"dbmdz/bert-large-cased-finetuned-conll03-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = TFAutoModelForTokenClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "example = \"My name is Sylvain and I work at Hugging Face in Brooklyn.\"\n",
-    "inputs = tokenizer(example, return_tensors=\"tf\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 19)\n",
-       "(1, 19, 9)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"input_ids\"].shape)\n",
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 6, 6, 6, 0, 8, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "probabilities = tf.math.softmax(outputs.logits, axis=-1)[0]\n",
-    "probabilities = probabilities.numpy().tolist()\n",
-    "predictions = tf.math.argmax(outputs.logits, axis=-1)[0]\n",
-    "predictions = predictions.numpy().tolist()\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'O',\n",
-       " 1: 'B-MISC',\n",
-       " 2: 'I-MISC',\n",
-       " 3: 'B-PER',\n",
-       " 4: 'I-PER',\n",
-       " 5: 'B-ORG',\n",
-       " 6: 'I-ORG',\n",
-       " 7: 'B-LOC',\n",
-       " 8: 'I-LOC'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S'},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl'},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va'},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in'},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu'},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging'},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face'},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "tokens = inputs.tokens()\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        results.append(\n",
-    "            {\"entity\": label, \"score\": probabilities[idx][pred], \"word\": tokens[idx]}\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 0), (0, 2), (3, 7), (8, 10), (11, 12), (12, 14), (14, 16), (16, 18), (19, 22), (23, 24), (25, 29), (30, 32),\n",
-       " (33, 35), (35, 40), (41, 45), (46, 48), (49, 57), (57, 58), (0, 0)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "inputs_with_offsets[\"offset_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "yl"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[12:14]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity': 'I-PER', 'score': 0.9993828, 'index': 4, 'word': 'S', 'start': 11, 'end': 12},\n",
-       " {'entity': 'I-PER', 'score': 0.99815476, 'index': 5, 'word': '##yl', 'start': 12, 'end': 14},\n",
-       " {'entity': 'I-PER', 'score': 0.99590725, 'index': 6, 'word': '##va', 'start': 14, 'end': 16},\n",
-       " {'entity': 'I-PER', 'score': 0.9992327, 'index': 7, 'word': '##in', 'start': 16, 'end': 18},\n",
-       " {'entity': 'I-ORG', 'score': 0.97389334, 'index': 12, 'word': 'Hu', 'start': 33, 'end': 35},\n",
-       " {'entity': 'I-ORG', 'score': 0.976115, 'index': 13, 'word': '##gging', 'start': 35, 'end': 40},\n",
-       " {'entity': 'I-ORG', 'score': 0.98879766, 'index': 14, 'word': 'Face', 'start': 41, 'end': 45},\n",
-       " {'entity': 'I-LOC', 'score': 0.99321055, 'index': 16, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "for idx, pred in enumerate(predictions):\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        start, end = offsets[idx]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity\": label,\n",
-    "                \"score\": probabilities[idx][pred],\n",
-    "                \"word\": tokens[idx],\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hugging Face"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example[33:45]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9981694, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.97960204, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.99321055, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "results = []\n",
-    "inputs_with_offsets = tokenizer(example, return_offsets_mapping=True)\n",
-    "tokens = inputs_with_offsets.tokens()\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "idx = 0\n",
-    "while idx < len(predictions):\n",
-    "    pred = predictions[idx]\n",
-    "    label = model.config.id2label[pred]\n",
-    "    if label != \"O\":\n",
-    "        # Remove the B- or I-\n",
-    "        label = label[2:]\n",
-    "        start, _ = offsets[idx]\n",
-    "\n",
-    "        # Grab all the tokens labeled with I-label\n",
-    "        all_scores = []\n",
-    "        while (\n",
-    "            idx < len(predictions)\n",
-    "            and model.config.id2label[predictions[idx]] == f\"I-{label}\"\n",
-    "        ):\n",
-    "            all_scores.append(probabilities[idx][pred])\n",
-    "            _, end = offsets[idx]\n",
-    "            idx += 1\n",
-    "\n",
-    "        # The score is the mean of all the scores of the tokens in that grouped entity\n",
-    "        score = np.mean(all_scores).item()\n",
-    "        word = example[start:end]\n",
-    "        results.append(\n",
-    "            {\n",
-    "                \"entity_group\": label,\n",
-    "                \"score\": score,\n",
-    "                \"word\": word,\n",
-    "                \"start\": start,\n",
-    "                \"end\": end,\n",
-    "            }\n",
-    "        )\n",
-    "    idx += 1\n",
-    "\n",
-    "print(results)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "快速标记器的特殊能力 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section3b_pt.ipynb b/course/zh-CN/chapter6/section3b_pt.ipynb
deleted file mode 100644
index cf46ac1f..00000000
--- a/course/zh-CN/chapter6/section3b_pt.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# QA 管道中的快速标记器 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97773,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch, and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97149,\n",
-       " 'start': 1892,\n",
-       " 'end': 1919,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "long_context = \"\"\"\n",
-    "🤗 Transformers: State of the Art NLP\n",
-    "\n",
-    "🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-    "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-    "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-    "\n",
-    "🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-    "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-    "can be modified to enable quick research experiments.\n",
-    "\n",
-    "Why should I use transformers?\n",
-    "\n",
-    "1. Easy-to-use state-of-the-art models:\n",
-    "  - High performance on NLU and NLG tasks.\n",
-    "  - Low barrier to entry for educators and practitioners.\n",
-    "  - Few user-facing abstractions with just three classes to learn.\n",
-    "  - A unified API for using all our pretrained models.\n",
-    "  - Lower compute costs, smaller carbon footprint:\n",
-    "\n",
-    "2. Researchers can share trained models instead of always retraining.\n",
-    "  - Practitioners can reduce compute time and production costs.\n",
-    "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-    "\n",
-    "3. Choose the right framework for every part of a model's lifetime:\n",
-    "  - Train state-of-the-art models in 3 lines of code.\n",
-    "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-    "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-    "\n",
-    "4. Easily customize a model or an example to your needs:\n",
-    "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-    "  - Model internals are exposed as consistently as possible.\n",
-    "  - Model files can be used independently of the library for quick experiments.\n",
-    "\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question_answerer(question=question, context=long_context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "inputs = tokenizer(question, context, return_tensors=\"pt\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 66]) torch.Size([1, 66])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "mask = torch.tensor(mask)[None]\n",
-    "\n",
-    "start_logits[mask] = -10000\n",
-    "end_logits[mask] = -10000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)[0]\n",
-    "end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = start_probabilities[:, None] * end_probabilities[None, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = torch.triu(scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97773"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_index = scores.argmax().item()\n",
-    "start_index = max_index // scores.shape[1]\n",
-    "end_index = max_index % scores.shape[1]\n",
-    "print(scores[start_index, end_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "start_char, _ = offsets[start_index]\n",
-    "_, end_char = offsets[end_index]\n",
-    "answer = context[start_char:end_char]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': 'Jax, PyTorch and TensorFlow',\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'score': 0.97773}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = {\n",
-    "    \"answer\": answer,\n",
-    "    \"start\": start_char,\n",
-    "    \"end\": end_char,\n",
-    "    \"score\": scores[start_index, end_index],\n",
-    "}\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "461"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context)\n",
-    "print(len(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "[CLS] Which deep learning libraries back [UNK] Transformers? [SEP] [UNK] Transformers : State of the Art NLP\n",
-       "\n",
-       "[UNK] Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-       "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-       "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-       "\n",
-       "[UNK] Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-       "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-       "can be modified to enable quick research experiments.\n",
-       "\n",
-       "Why should I use transformers?\n",
-       "\n",
-       "1. Easy-to-use state-of-the-art models:\n",
-       "  - High performance on NLU and NLG tasks.\n",
-       "  - Low barrier to entry for educators and practitioners.\n",
-       "  - Few user-facing abstractions with just three classes to learn.\n",
-       "  - A unified API for using all our pretrained models.\n",
-       "  - Lower compute costs, smaller carbon footprint:\n",
-       "\n",
-       "2. Researchers can share trained models instead of always retraining.\n",
-       "  - Practitioners can reduce compute time and production costs.\n",
-       "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-       "\n",
-       "3. Choose the right framework for every part of a model's lifetime:\n",
-       "  - Train state-of-the-art models in 3 lines of code.\n",
-       "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-       "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-       "\n",
-       "4. Easily customize a model or an example to your needs:\n",
-       "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-       "  - Model internal [SEP]\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n",
-    "print(tokenizer.decode(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] This sentence is not [SEP]'\n",
-       "'[CLS] is not too long [SEP]'\n",
-       "'[CLS] too long but we [SEP]'\n",
-       "'[CLS] but we are going [SEP]'\n",
-       "'[CLS] are going to split [SEP]'\n",
-       "'[CLS] to split it anyway [SEP]'\n",
-       "'[CLS] it anyway. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"This sentence is not too long but we are going to split it anyway.\"\n",
-    "inputs = tokenizer(\n",
-    "    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'attention_mask', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentences = [\n",
-    "    \"This sentence is not too long but we are going to split it anyway.\",\n",
-    "    \"This sentence is shorter but will still get split.\",\n",
-    "]\n",
-    "inputs = tokenizer(\n",
-    "    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    long_context,\n",
-    "    stride=128,\n",
-    "    max_length=384,\n",
-    "    padding=\"longest\",\n",
-    "    truncation=\"only_second\",\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 384])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_ = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "offsets = inputs.pop(\"offset_mapping\")\n",
-    "\n",
-    "inputs = inputs.convert_to_tensors(\"pt\")\n",
-    "print(inputs[\"input_ids\"].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([2, 384]) torch.Size([2, 384])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "\n",
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "# Mask all the [PAD] tokens\n",
-    "mask = torch.logical_or(torch.tensor(mask)[None], (inputs[\"attention_mask\"] == 0))\n",
-    "\n",
-    "start_logits[mask] = -10000\n",
-    "end_logits[mask] = -10000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = torch.nn.functional.softmax(start_logits, dim=-1)\n",
-    "end_probabilities = torch.nn.functional.softmax(end_logits, dim=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 18, 0.33867), (173, 184, 0.97149)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidates = []\n",
-    "for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n",
-    "    scores = start_probs[:, None] * end_probs[None, :]\n",
-    "    idx = torch.triu(scores).argmax().item()\n",
-    "\n",
-    "    start_idx = idx // scores.shape[1]\n",
-    "    end_idx = idx % scores.shape[1]\n",
-    "    score = scores[start_idx, end_idx].item()\n",
-    "    candidates.append((start_idx, end_idx, score))\n",
-    "\n",
-    "print(candidates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': '\\n🤗 Transformers: State of the Art NLP', 'start': 0, 'end': 37, 'score': 0.33867}\n",
-       "{'answer': 'Jax, PyTorch and TensorFlow', 'start': 1892, 'end': 1919, 'score': 0.97149}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for candidate, offset in zip(candidates, offsets):\n",
-    "    start_token, end_token, score = candidate\n",
-    "    start_char, _ = offset[start_token]\n",
-    "    _, end_char = offset[end_token]\n",
-    "    answer = long_context[start_char:end_char]\n",
-    "    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n",
-    "    print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "QA 管道中的快速标记器 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section3b_tf.ipynb b/course/zh-CN/chapter6/section3b_tf.ipynb
deleted file mode 100644
index 2d79af56..00000000
--- a/course/zh-CN/chapter6/section3b_tf.ipynb
+++ /dev/null
@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# QA 管道中的快速标记器 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97773,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "question_answerer = pipeline(\"question-answering\")\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch, and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.97149,\n",
-       " 'start': 1892,\n",
-       " 'end': 1919,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "long_context = \"\"\"\n",
-    "🤗 Transformers: State of the Art NLP\n",
-    "\n",
-    "🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-    "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-    "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-    "\n",
-    "🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-    "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-    "can be modified to enable quick research experiments.\n",
-    "\n",
-    "Why should I use transformers?\n",
-    "\n",
-    "1. Easy-to-use state-of-the-art models:\n",
-    "  - High performance on NLU and NLG tasks.\n",
-    "  - Low barrier to entry for educators and practitioners.\n",
-    "  - Few user-facing abstractions with just three classes to learn.\n",
-    "  - A unified API for using all our pretrained models.\n",
-    "  - Lower compute costs, smaller carbon footprint:\n",
-    "\n",
-    "2. Researchers can share trained models instead of always retraining.\n",
-    "  - Practitioners can reduce compute time and production costs.\n",
-    "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-    "\n",
-    "3. Choose the right framework for every part of a model's lifetime:\n",
-    "  - Train state-of-the-art models in 3 lines of code.\n",
-    "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-    "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-    "\n",
-    "4. Easily customize a model or an example to your needs:\n",
-    "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-    "  - Model internals are exposed as consistently as possible.\n",
-    "  - Model files can be used independently of the library for quick experiments.\n",
-    "\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question_answerer(question=question, context=long_context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "inputs = tokenizer(question, context, return_tensors=\"tf\")\n",
-    "outputs = model(**inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 66) (1, 66)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "mask = tf.constant(mask)[None]\n",
-    "\n",
-    "start_logits = tf.where(mask, -10000, start_logits)\n",
-    "end_logits = tf.where(mask, -10000, end_logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = tf.math.softmax(start_logits, axis=-1)[0].numpy()\n",
-    "end_probabilities = tf.math.softmax(end_logits, axis=-1)[0].numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = start_probabilities[:, None] * end_probabilities[None, :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scores = np.triu(scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.97773"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max_index = scores.argmax().item()\n",
-    "start_index = max_index // scores.shape[1]\n",
-    "end_index = max_index % scores.shape[1]\n",
-    "print(scores[start_index, end_index])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs_with_offsets = tokenizer(question, context, return_offsets_mapping=True)\n",
-    "offsets = inputs_with_offsets[\"offset_mapping\"]\n",
-    "\n",
-    "start_char, _ = offsets[start_index]\n",
-    "_, end_char = offsets[end_index]\n",
-    "answer = context[start_char:end_char]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': 'Jax, PyTorch and TensorFlow',\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'score': 0.97773}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result = {\n",
-    "    \"answer\": answer,\n",
-    "    \"start\": start_char,\n",
-    "    \"end\": end_char,\n",
-    "    \"score\": scores[start_index, end_index],\n",
-    "}\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "461"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context)\n",
-    "print(len(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "[CLS] Which deep learning libraries back [UNK] Transformers? [SEP] [UNK] Transformers : State of the Art NLP\n",
-       "\n",
-       "[UNK] Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction,\n",
-       "question answering, summarization, translation, text generation and more in over 100 languages.\n",
-       "Its aim is to make cutting-edge NLP easier to use for everyone.\n",
-       "\n",
-       "[UNK] Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and\n",
-       "then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and\n",
-       "can be modified to enable quick research experiments.\n",
-       "\n",
-       "Why should I use transformers?\n",
-       "\n",
-       "1. Easy-to-use state-of-the-art models:\n",
-       "  - High performance on NLU and NLG tasks.\n",
-       "  - Low barrier to entry for educators and practitioners.\n",
-       "  - Few user-facing abstractions with just three classes to learn.\n",
-       "  - A unified API for using all our pretrained models.\n",
-       "  - Lower compute costs, smaller carbon footprint:\n",
-       "\n",
-       "2. Researchers can share trained models instead of always retraining.\n",
-       "  - Practitioners can reduce compute time and production costs.\n",
-       "  - Dozens of architectures with over 10,000 pretrained models, some in more than 100 languages.\n",
-       "\n",
-       "3. Choose the right framework for every part of a model's lifetime:\n",
-       "  - Train state-of-the-art models in 3 lines of code.\n",
-       "  - Move a single model between TF2.0/PyTorch frameworks at will.\n",
-       "  - Seamlessly pick the right framework for training, evaluation and production.\n",
-       "\n",
-       "4. Easily customize a model or an example to your needs:\n",
-       "  - We provide examples for each architecture to reproduce the results published by its original authors.\n",
-       "  - Model internal [SEP]\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(question, long_context, max_length=384, truncation=\"only_second\")\n",
-    "print(tokenizer.decode(inputs[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] This sentence is not [SEP]'\n",
-       "'[CLS] is not too long [SEP]'\n",
-       "'[CLS] too long but we [SEP]'\n",
-       "'[CLS] but we are going [SEP]'\n",
-       "'[CLS] are going to split [SEP]'\n",
-       "'[CLS] to split it anyway [SEP]'\n",
-       "'[CLS] it anyway. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"This sentence is not too long but we are going to split it anyway.\"\n",
-    "inputs = tokenizer(\n",
-    "    sentence, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'attention_mask', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentences = [\n",
-    "    \"This sentence is not too long but we are going to split it anyway.\",\n",
-    "    \"This sentence is shorter but will still get split.\",\n",
-    "]\n",
-    "inputs = tokenizer(\n",
-    "    sentences, truncation=True, return_overflowing_tokens=True, max_length=6, stride=2\n",
-    ")\n",
-    "\n",
-    "print(inputs[\"overflow_to_sample_mapping\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    long_context,\n",
-    "    stride=128,\n",
-    "    max_length=384,\n",
-    "    padding=\"longest\",\n",
-    "    truncation=\"only_second\",\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 384)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_ = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "offsets = inputs.pop(\"offset_mapping\")\n",
-    "\n",
-    "inputs = inputs.convert_to_tensors(\"tf\")\n",
-    "print(inputs[\"input_ids\"].shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 384) (2, 384)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(**inputs)\n",
-    "\n",
-    "start_logits = outputs.start_logits\n",
-    "end_logits = outputs.end_logits\n",
-    "print(start_logits.shape, end_logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence_ids = inputs.sequence_ids()\n",
-    "# Mask everything apart from the tokens of the context\n",
-    "mask = [i != 1 for i in sequence_ids]\n",
-    "# Unmask the [CLS] token\n",
-    "mask[0] = False\n",
-    "# Mask all the [PAD] tokens\n",
-    "mask = tf.math.logical_or(tf.constant(mask)[None], inputs[\"attention_mask\"] == 0)\n",
-    "\n",
-    "start_logits = tf.where(mask, -10000, start_logits)\n",
-    "end_logits = tf.where(mask, -10000, end_logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_probabilities = tf.math.softmax(start_logits, axis=-1).numpy()\n",
-    "end_probabilities = tf.math.softmax(end_logits, axis=-1).numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0, 18, 0.33867), (173, 184, 0.97149)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "candidates = []\n",
-    "for start_probs, end_probs in zip(start_probabilities, end_probabilities):\n",
-    "    scores = start_probs[:, None] * end_probs[None, :]\n",
-    "    idx = np.triu(scores).argmax().item()\n",
-    "\n",
-    "    start_idx = idx // scores.shape[1]\n",
-    "    end_idx = idx % scores.shape[1]\n",
-    "    score = scores[start_idx, end_idx].item()\n",
-    "    candidates.append((start_idx, end_idx, score))\n",
-    "\n",
-    "print(candidates)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'answer': '\\n🤗 Transformers: State of the Art NLP', 'start': 0, 'end': 37, 'score': 0.33867}\n",
-       "{'answer': 'Jax, PyTorch and TensorFlow', 'start': 1892, 'end': 1919, 'score': 0.97149}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for candidate, offset in zip(candidates, offsets):\n",
-    "    start_token, end_token, score = candidate\n",
-    "    start_char, _ = offset[start_token]\n",
-    "    _, end_char = offset[end_token]\n",
-    "    answer = long_context[start_char:end_char]\n",
-    "    result = {\"answer\": answer, \"start\": start_char, \"end\": end_char, \"score\": score}\n",
-    "    print(result)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "QA 管道中的快速标记器 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section4.ipynb b/course/zh-CN/chapter6/section4.ipynb
deleted file mode 100644
index e9bc63f1..00000000
--- a/course/zh-CN/chapter6/section4.ipynb
+++ /dev/null
@@ -1,141 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 标准化和预标记化"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<class 'tokenizers.Tokenizer'>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
-    "print(type(tokenizer.backend_tokenizer))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello how are u?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.backend_tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Hello', (0, 5)), (',', (5, 6)), ('how', (7, 10)), ('are', (11, 14)), ('you', (16, 19)), ('?', (19, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Hello', (0, 5)), (',', (5, 6)), ('Ġhow', (6, 10)), ('Ġare', (10, 14)), ('Ġ', (14, 15)), ('Ġyou', (15, 19)),\n",
-       " ('?', (19, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('▁Hello,', (0, 6)), ('▁how', (7, 10)), ('▁are', (11, 14)), ('▁you?', (16, 20))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n",
-    "tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(\"Hello, how are  you?\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "标准化和预标记化",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section5.ipynb b/course/zh-CN/chapter6/section5.ipynb
deleted file mode 100644
index 7cf7b2e4..00000000
--- a/course/zh-CN/chapter6/section5.ipynb
+++ /dev/null
@@ -1,378 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 字节对编码标记化"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(int, {'This': 3, 'Ġis': 2, 'Ġthe': 1, 'ĠHugging': 1, 'ĠFace': 1, 'ĠCourse': 1, '.': 4, 'Ġchapter': 1,\n",
-       "    'Ġabout': 1, 'Ġtokenization': 1, 'Ġsection': 1, 'Ġshows': 1, 'Ġseveral': 1, 'Ġtokenizer': 1, 'Ġalgorithms': 1,\n",
-       "    'Hopefully': 1, ',': 1, 'Ġyou': 1, 'Ġwill': 1, 'Ġbe': 1, 'Ġable': 1, 'Ġto': 1, 'Ġunderstand': 1, 'Ġhow': 1,\n",
-       "    'Ġthey': 1, 'Ġare': 1, 'Ġtrained': 1, 'Ġand': 1, 'Ġgenerate': 1, 'Ġtokens': 1})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "print(word_freqs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[ ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's',\n",
-       "  't', 'u', 'v', 'w', 'y', 'z', 'Ġ']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alphabet = []\n",
-    "\n",
-    "for word in word_freqs.keys():\n",
-    "    for letter in word:\n",
-    "        if letter not in alphabet:\n",
-    "            alphabet.append(letter)\n",
-    "alphabet.sort()\n",
-    "\n",
-    "print(alphabet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab = [\"<|endoftext|>\"] + alphabet.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "splits = {word: [c for c in word] for word in word_freqs.keys()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pair_freqs(splits):\n",
-    "    pair_freqs = defaultdict(int)\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "        for i in range(len(split) - 1):\n",
-    "            pair = (split[i], split[i + 1])\n",
-    "            pair_freqs[pair] += freq\n",
-    "    return pair_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('T', 'h'): 3\n",
-       "('h', 'i'): 3\n",
-       "('i', 's'): 5\n",
-       "('Ġ', 'i'): 2\n",
-       "('Ġ', 't'): 7\n",
-       "('t', 'h'): 3"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pair_freqs = compute_pair_freqs(splits)\n",
-    "\n",
-    "for i, key in enumerate(pair_freqs.keys()):\n",
-    "    print(f\"{key}: {pair_freqs[key]}\")\n",
-    "    if i >= 5:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('Ġ', 't') 7"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_pair = \"\"\n",
-    "max_freq = None\n",
-    "\n",
-    "for pair, freq in pair_freqs.items():\n",
-    "    if max_freq is None or max_freq < freq:\n",
-    "        best_pair = pair\n",
-    "        max_freq = freq\n",
-    "\n",
-    "print(best_pair, max_freq)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "merges = {(\"Ġ\", \"t\"): \"Ġt\"}\n",
-    "vocab.append(\"Ġt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_pair(a, b, splits):\n",
-    "    for word in word_freqs:\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "\n",
-    "        i = 0\n",
-    "        while i < len(split) - 1:\n",
-    "            if split[i] == a and split[i + 1] == b:\n",
-    "                split = split[:i] + [a + b] + split[i + 2 :]\n",
-    "            else:\n",
-    "                i += 1\n",
-    "        splits[word] = split\n",
-    "    return splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Ġt', 'r', 'a', 'i', 'n', 'e', 'd']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "splits = merge_pair(\"Ġ\", \"t\", splits)\n",
-    "print(splits[\"Ġtrained\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab_size = 50\n",
-    "\n",
-    "while len(vocab) < vocab_size:\n",
-    "    pair_freqs = compute_pair_freqs(splits)\n",
-    "    best_pair = \"\"\n",
-    "    max_freq = None\n",
-    "    for pair, freq in pair_freqs.items():\n",
-    "        if max_freq is None or max_freq < freq:\n",
-    "            best_pair = pair\n",
-    "            max_freq = freq\n",
-    "    splits = merge_pair(*best_pair, splits)\n",
-    "    merges[best_pair] = best_pair[0] + best_pair[1]\n",
-    "    vocab.append(best_pair[0] + best_pair[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{('Ġ', 't'): 'Ġt', ('i', 's'): 'is', ('e', 'r'): 'er', ('Ġ', 'a'): 'Ġa', ('Ġt', 'o'): 'Ġto', ('e', 'n'): 'en',\n",
-       " ('T', 'h'): 'Th', ('Th', 'is'): 'This', ('o', 'u'): 'ou', ('s', 'e'): 'se', ('Ġto', 'k'): 'Ġtok',\n",
-       " ('Ġtok', 'en'): 'Ġtoken', ('n', 'd'): 'nd', ('Ġ', 'is'): 'Ġis', ('Ġt', 'h'): 'Ġth', ('Ġth', 'e'): 'Ġthe',\n",
-       " ('i', 'n'): 'in', ('Ġa', 'b'): 'Ġab', ('Ġtoken', 'i'): 'Ġtokeni'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(merges)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['<|endoftext|>', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'k', 'l', 'm', 'n', 'o',\n",
-       " 'p', 'r', 's', 't', 'u', 'v', 'w', 'y', 'z', 'Ġ', 'Ġt', 'is', 'er', 'Ġa', 'Ġto', 'en', 'Th', 'This', 'ou', 'se',\n",
-       " 'Ġtok', 'Ġtoken', 'nd', 'Ġis', 'Ġth', 'Ġthe', 'in', 'Ġab', 'Ġtokeni']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize(text):\n",
-    "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
-    "    splits = [[l for l in word] for word in pre_tokenized_text]\n",
-    "    for pair, merge in merges.items():\n",
-    "        for idx, split in enumerate(splits):\n",
-    "            i = 0\n",
-    "            while i < len(split) - 1:\n",
-    "                if split[i] == pair[0] and split[i + 1] == pair[1]:\n",
-    "                    split = split[:i] + [merge] + split[i + 2 :]\n",
-    "                else:\n",
-    "                    i += 1\n",
-    "            splits[idx] = split\n",
-    "\n",
-    "    return sum(splits, [])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['This', 'Ġis', 'Ġ', 'n', 'o', 't', 'Ġa', 'Ġtoken', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenize(\"This is not a token.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "字节对编码标记化",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section6.ipynb b/course/zh-CN/chapter6/section6.ipynb
deleted file mode 100644
index a15a7dec..00000000
--- a/course/zh-CN/chapter6/section6.ipynb
+++ /dev/null
@@ -1,406 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# WordPiece 标记化"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(\n",
-       "    int, {'This': 3, 'is': 2, 'the': 1, 'Hugging': 1, 'Face': 1, 'Course': 1, '.': 4, 'chapter': 1, 'about': 1,\n",
-       "    'tokenization': 1, 'section': 1, 'shows': 1, 'several': 1, 'tokenizer': 1, 'algorithms': 1, 'Hopefully': 1,\n",
-       "    ',': 1, 'you': 1, 'will': 1, 'be': 1, 'able': 1, 'to': 1, 'understand': 1, 'how': 1, 'they': 1, 'are': 1,\n",
-       "    'trained': 1, 'and': 1, 'generate': 1, 'tokens': 1})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "word_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['##a', '##b', '##c', '##d', '##e', '##f', '##g', '##h', '##i', '##k', '##l', '##m', '##n', '##o', '##p', '##r', '##s',\n",
-       " '##t', '##u', '##v', '##w', '##y', '##z', ',', '.', 'C', 'F', 'H', 'T', 'a', 'b', 'c', 'g', 'h', 'i', 's', 't', 'u',\n",
-       " 'w', 'y']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alphabet = []\n",
-    "for word in word_freqs.keys():\n",
-    "    if word[0] not in alphabet:\n",
-    "        alphabet.append(word[0])\n",
-    "    for letter in word[1:]:\n",
-    "        if f\"##{letter}\" not in alphabet:\n",
-    "            alphabet.append(f\"##{letter}\")\n",
-    "\n",
-    "alphabet.sort()\n",
-    "alphabet\n",
-    "\n",
-    "print(alphabet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab = [\"[PAD]\", \"[UNK]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"] + alphabet.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "splits = {\n",
-    "    word: [c if i == 0 else f\"##{c}\" for i, c in enumerate(word)]\n",
-    "    for word in word_freqs.keys()\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_pair_scores(splits):\n",
-    "    letter_freqs = defaultdict(int)\n",
-    "    pair_freqs = defaultdict(int)\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            letter_freqs[split[0]] += freq\n",
-    "            continue\n",
-    "        for i in range(len(split) - 1):\n",
-    "            pair = (split[i], split[i + 1])\n",
-    "            letter_freqs[split[i]] += freq\n",
-    "            pair_freqs[pair] += freq\n",
-    "        letter_freqs[split[-1]] += freq\n",
-    "\n",
-    "    scores = {\n",
-    "        pair: freq / (letter_freqs[pair[0]] * letter_freqs[pair[1]])\n",
-    "        for pair, freq in pair_freqs.items()\n",
-    "    }\n",
-    "    return scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('T', '##h'): 0.125\n",
-       "('##h', '##i'): 0.03409090909090909\n",
-       "('##i', '##s'): 0.02727272727272727\n",
-       "('i', '##s'): 0.1\n",
-       "('t', '##h'): 0.03571428571428571\n",
-       "('##h', '##e'): 0.011904761904761904"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pair_scores = compute_pair_scores(splits)\n",
-    "for i, key in enumerate(pair_scores.keys()):\n",
-    "    print(f\"{key}: {pair_scores[key]}\")\n",
-    "    if i >= 5:\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('a', '##b') 0.2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_pair = \"\"\n",
-    "max_score = None\n",
-    "for pair, score in pair_scores.items():\n",
-    "    if max_score is None or max_score < score:\n",
-    "        best_pair = pair\n",
-    "        max_score = score\n",
-    "\n",
-    "print(best_pair, max_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab.append(\"ab\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def merge_pair(a, b, splits):\n",
-    "    for word in word_freqs:\n",
-    "        split = splits[word]\n",
-    "        if len(split) == 1:\n",
-    "            continue\n",
-    "        i = 0\n",
-    "        while i < len(split) - 1:\n",
-    "            if split[i] == a and split[i + 1] == b:\n",
-    "                merge = a + b[2:] if b.startswith(\"##\") else a + b\n",
-    "                split = split[:i] + [merge] + split[i + 2 :]\n",
-    "            else:\n",
-    "                i += 1\n",
-    "        splits[word] = split\n",
-    "    return splits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['ab', '##o', '##u', '##t']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "splits = merge_pair(\"a\", \"##b\", splits)\n",
-    "splits[\"about\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vocab_size = 70\n",
-    "while len(vocab) < vocab_size:\n",
-    "    scores = compute_pair_scores(splits)\n",
-    "    best_pair, max_score = \"\", None\n",
-    "    for pair, score in scores.items():\n",
-    "        if max_score is None or max_score < score:\n",
-    "            best_pair = pair\n",
-    "            max_score = score\n",
-    "    splits = merge_pair(*best_pair, splits)\n",
-    "    new_token = (\n",
-    "        best_pair[0] + best_pair[1][2:]\n",
-    "        if best_pair[1].startswith(\"##\")\n",
-    "        else best_pair[0] + best_pair[1]\n",
-    "    )\n",
-    "    vocab.append(new_token)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]', '##a', '##b', '##c', '##d', '##e', '##f', '##g', '##h', '##i', '##k',\n",
-       " '##l', '##m', '##n', '##o', '##p', '##r', '##s', '##t', '##u', '##v', '##w', '##y', '##z', ',', '.', 'C', 'F', 'H',\n",
-       " 'T', 'a', 'b', 'c', 'g', 'h', 'i', 's', 't', 'u', 'w', 'y', '##fu', 'Fa', 'Fac', '##ct', '##ful', '##full', '##fully',\n",
-       " 'Th', 'ch', '##hm', 'cha', 'chap', 'chapt', '##thm', 'Hu', 'Hug', 'Hugg', 'sh', 'th', 'is', '##thms', '##za', '##zat',\n",
-       " '##ut']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(vocab)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_word(word):\n",
-    "    tokens = []\n",
-    "    while len(word) > 0:\n",
-    "        i = len(word)\n",
-    "        while i > 0 and word[:i] not in vocab:\n",
-    "            i -= 1\n",
-    "        if i == 0:\n",
-    "            return [\"[UNK]\"]\n",
-    "        tokens.append(word[:i])\n",
-    "        word = word[i:]\n",
-    "        if len(word) > 0:\n",
-    "            word = f\"##{word}\"\n",
-    "    return tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Hugg', '##i', '##n', '##g']\n",
-       "['[UNK]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(encode_word(\"Hugging\"))\n",
-    "print(encode_word(\"HOgging\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize(text):\n",
-    "    pre_tokenize_result = tokenizer._tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in pre_tokenize_result]\n",
-    "    encoded_words = [encode_word(word) for word in pre_tokenized_text]\n",
-    "    return sum(encoded_words, [])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Th', '##i', '##s', 'is', 'th', '##e', 'Hugg', '##i', '##n', '##g', 'Fac', '##e', 'c', '##o', '##u', '##r', '##s',\n",
-       " '##e', '[UNK]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenize(\"This is the Hugging Face course!\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "WordPiece 标记化",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section7.ipynb b/course/zh-CN/chapter6/section7.ipynb
deleted file mode 100644
index bcb9f7c9..00000000
--- a/course/zh-CN/chapter6/section7.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Unigram标记化"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "corpus = [\n",
-    "    \"This is the Hugging Face course.\",\n",
-    "    \"This chapter is about tokenization.\",\n",
-    "    \"This section shows several tokenizer algorithms.\",\n",
-    "    \"Hopefully, you will be able to understand how they are trained and generate tokens.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"xlnet-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict\n",
-    "\n",
-    "word_freqs = defaultdict(int)\n",
-    "for text in corpus:\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    new_words = [word for word, offset in words_with_offsets]\n",
-    "    for word in new_words:\n",
-    "        word_freqs[word] += 1\n",
-    "\n",
-    "word_freqs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('▁t', 7), ('is', 5), ('er', 5), ('▁a', 5), ('▁to', 4), ('to', 4), ('en', 4), ('▁T', 3), ('▁Th', 3), ('▁Thi', 3)]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "char_freqs = defaultdict(int)\n",
-    "subwords_freqs = defaultdict(int)\n",
-    "for word, freq in word_freqs.items():\n",
-    "    for i in range(len(word)):\n",
-    "        char_freqs[word[i]] += freq\n",
-    "        # Loop through the subwords of length at least 2\n",
-    "        for j in range(i + 2, len(word) + 1):\n",
-    "            subwords_freqs[word[i:j]] += freq\n",
-    "\n",
-    "# Sort subwords by frequency\n",
-    "sorted_subwords = sorted(subwords_freqs.items(), key=lambda x: x[1], reverse=True)\n",
-    "sorted_subwords[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "token_freqs = list(char_freqs.items()) + sorted_subwords[: 300 - len(char_freqs)]\n",
-    "token_freqs = {token: freq for token, freq in token_freqs}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from math import log\n",
-    "\n",
-    "total_sum = sum([freq for token, freq in token_freqs.items()])\n",
-    "model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_word(word, model):\n",
-    "    best_segmentations = [{\"start\": 0, \"score\": 1}] + [\n",
-    "        {\"start\": None, \"score\": None} for _ in range(len(word))\n",
-    "    ]\n",
-    "    for start_idx in range(len(word)):\n",
-    "        # This should be properly filled by the previous steps of the loop\n",
-    "        best_score_at_start = best_segmentations[start_idx][\"score\"]\n",
-    "        for end_idx in range(start_idx + 1, len(word) + 1):\n",
-    "            token = word[start_idx:end_idx]\n",
-    "            if token in model and best_score_at_start is not None:\n",
-    "                score = model[token] + best_score_at_start\n",
-    "                # If we have found a better segmentation ending at end_idx, we update\n",
-    "                if (\n",
-    "                    best_segmentations[end_idx][\"score\"] is None\n",
-    "                    or best_segmentations[end_idx][\"score\"] > score\n",
-    "                ):\n",
-    "                    best_segmentations[end_idx] = {\"start\": start_idx, \"score\": score}\n",
-    "\n",
-    "    segmentation = best_segmentations[-1]\n",
-    "    if segmentation[\"score\"] is None:\n",
-    "        # We did not find a tokenization of the word -> unknown\n",
-    "        return [\"<unk>\"], None\n",
-    "\n",
-    "    score = segmentation[\"score\"]\n",
-    "    start = segmentation[\"start\"]\n",
-    "    end = len(word)\n",
-    "    tokens = []\n",
-    "    while start != 0:\n",
-    "        tokens.insert(0, word[start:end])\n",
-    "        next_start = best_segmentations[start][\"start\"]\n",
-    "        end = start\n",
-    "        start = next_start\n",
-    "    tokens.insert(0, word[start:end])\n",
-    "    return tokens, score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(['H', 'o', 'p', 'e', 'f', 'u', 'll', 'y'], 41.5157494601402)\n",
-       "(['This'], 6.288267030694535)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(encode_word(\"Hopefully\", model))\n",
-    "print(encode_word(\"This\", model))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_loss(model):\n",
-    "    loss = 0\n",
-    "    for word, freq in word_freqs.items():\n",
-    "        _, word_loss = encode_word(word, model)\n",
-    "        loss += freq * word_loss\n",
-    "    return loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "413.10377642940875"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_loss(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "\n",
-    "\n",
-    "def compute_scores(model):\n",
-    "    scores = {}\n",
-    "    model_loss = compute_loss(model)\n",
-    "    for token, score in model.items():\n",
-    "        # We always keep tokens of length 1\n",
-    "        if len(token) == 1:\n",
-    "            continue\n",
-    "        model_without_token = copy.deepcopy(model)\n",
-    "        _ = model_without_token.pop(token)\n",
-    "        scores[token] = compute_loss(model_without_token) - model_loss\n",
-    "    return scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.376412403623874\n",
-       "0.0"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = compute_scores(model)\n",
-    "print(scores[\"ll\"])\n",
-    "print(scores[\"his\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "percent_to_remove = 0.1\n",
-    "while len(model) > 100:\n",
-    "    scores = compute_scores(model)\n",
-    "    sorted_scores = sorted(scores.items(), key=lambda x: x[1])\n",
-    "    # Remove percent_to_remove tokens with the lowest scores.\n",
-    "    for i in range(int(len(model) * percent_to_remove)):\n",
-    "        _ = token_freqs.pop(sorted_scores[i][0])\n",
-    "\n",
-    "    total_sum = sum([freq for token, freq in token_freqs.items()])\n",
-    "    model = {token: -log(freq / total_sum) for token, freq in token_freqs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁This', '▁is', '▁the', '▁Hugging', '▁Face', '▁', 'c', 'ou', 'r', 's', 'e', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(text, model):\n",
-    "    words_with_offsets = tokenizer.backend_tokenizer.pre_tokenizer.pre_tokenize_str(text)\n",
-    "    pre_tokenized_text = [word for word, offset in words_with_offsets]\n",
-    "    encoded_words = [encode_word(word, model)[0] for word in pre_tokenized_text]\n",
-    "    return sum(encoded_words, [])\n",
-    "\n",
-    "\n",
-    "tokenize(\"This is the Hugging Face course.\", model)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Unigram标记化",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter6/section8.ipynb b/course/zh-CN/chapter6/section8.ipynb
deleted file mode 100644
index aff76f67..00000000
--- a/course/zh-CN/chapter6/section8.ipynb
+++ /dev/null
@@ -1,779 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 逐块地构建标记器"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "dataset = load_dataset(\"wikitext\", name=\"wikitext-2-raw-v1\", split=\"train\")\n",
-    "\n",
-    "\n",
-    "def get_training_corpus():\n",
-    "    for i in range(0, len(dataset), 1000):\n",
-    "        yield dataset[i : i + 1000][\"text\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"wikitext-2.txt\", \"w\", encoding=\"utf-8\") as f:\n",
-    "    for i in range(len(dataset)):\n",
-    "        f.write(dataset[i][\"text\"] + \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tokenizers import (\n",
-    "    decoders,\n",
-    "    models,\n",
-    "    normalizers,\n",
-    "    pre_tokenizers,\n",
-    "    processors,\n",
-    "    trainers,\n",
-    "    Tokenizer,\n",
-    ")\n",
-    "\n",
-    "tokenizer = Tokenizer(models.WordPiece(unk_token=\"[UNK]\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.normalizer = normalizers.Sequence(\n",
-    "    [normalizers.NFD(), normalizers.Lowercase(), normalizers.StripAccents()]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "hello how are u?"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.normalizer.normalize_str(\"Héllò hôw are ü?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.Whitespace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'\", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),\n",
-       " ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(\"Let's\", (0, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre-tokenizer.', (14, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pre_tokenizer = pre_tokenizers.WhitespaceSplit()\n",
-    "pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'\", (3, 4)), ('s', (4, 5)), ('test', (6, 10)), ('my', (11, 13)), ('pre', (14, 17)),\n",
-       " ('-', (17, 18)), ('tokenizer', (18, 27)), ('.', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pre_tokenizer = pre_tokenizers.Sequence(\n",
-    "    [pre_tokenizers.WhitespaceSplit(), pre_tokenizers.Punctuation()]\n",
-    ")\n",
-    "pre_tokenizer.pre_tokenize_str(\"Let's test my pre-tokenizer.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "special_tokens = [\"[UNK]\", \"[PAD]\", \"[CLS]\", \"[SEP]\", \"[MASK]\"]\n",
-    "trainer = trainers.WordPieceTrainer(vocab_size=25000, special_tokens=special_tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.WordPiece(unk_token=\"[UNK]\")\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cls_token_id = tokenizer.token_to_id(\"[CLS]\")\n",
-    "sep_token_id = tokenizer.token_to_id(\"[SEP]\")\n",
-    "print(cls_token_id, sep_token_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.TemplateProcessing(\n",
-    "    single=f\"[CLS]:0 $A:0 [SEP]:0\",\n",
-    "    pair=f\"[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1\",\n",
-    "    special_tokens=[(\"[CLS]\", cls_token_id), (\"[SEP]\", sep_token_id)],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'let', \"'\", 's', 'test', 'this', 'tok', '##eni', '##zer', '...', '[SEP]', 'on', 'a', 'pair', 'of', 'sentences', '.', '[SEP]']\n",
-       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer...\", \"on a pair of sentences.\")\n",
-    "print(encoding.tokens)\n",
-    "print(encoding.type_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.WordPiece(prefix=\"##\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"let's test this tokenizer... on a pair of sentences.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(encoding.ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save(\"tokenizer.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_tokenizer = Tokenizer.from_file(\"tokenizer.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    # tokenizer_file=\"tokenizer.json\", # You can load from the tokenizer file, alternatively\n",
-    "    unk_token=\"[UNK]\",\n",
-    "    pad_token=\"[PAD]\",\n",
-    "    cls_token=\"[CLS]\",\n",
-    "    sep_token=\"[SEP]\",\n",
-    "    mask_token=\"[MASK]\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = BertTokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = Tokenizer(models.BPE())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Let', (0, 3)), (\"'s\", (3, 5)), ('Ġtest', (5, 10)), ('Ġpre', (10, 14)), ('-', (14, 15)),\n",
-       " ('tokenization', (15, 27)), ('!', (27, 28))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test pre-tokenization!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer = trainers.BpeTrainer(vocab_size=25000, special_tokens=[\"<|endoftext|>\"])\n",
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.BPE()\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['L', 'et', \"'\", 's', 'Ġtest', 'Ġthis', 'Ġto', 'ken', 'izer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' test'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sentence = \"Let's test this tokenizer.\"\n",
-    "encoding = tokenizer.encode(sentence)\n",
-    "start, end = encoding.offsets[4]\n",
-    "sentence[start:end]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.ByteLevel()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Let's test this tokenizer.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(encoding.ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    bos_token=\"<|endoftext|>\",\n",
-    "    eos_token=\"<|endoftext|>\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT2TokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = GPT2TokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = Tokenizer(models.Unigram())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tokenizers import Regex\n",
-    "\n",
-    "tokenizer.normalizer = normalizers.Sequence(\n",
-    "    [\n",
-    "        normalizers.Replace(\"``\", '\"'),\n",
-    "        normalizers.Replace(\"''\", '\"'),\n",
-    "        normalizers.NFKD(),\n",
-    "        normalizers.StripAccents(),\n",
-    "        normalizers.Replace(Regex(\" {2,}\"), \" \"),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(\"▁Let's\", (0, 5)), ('▁test', (5, 10)), ('▁the', (10, 14)), ('▁pre-tokenizer!', (14, 29))]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.pre_tokenizer.pre_tokenize_str(\"Let's test the pre-tokenizer!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "special_tokens = [\"<cls>\", \"<sep>\", \"<unk>\", \"<pad>\", \"<mask>\", \"<s>\", \"</s>\"]\n",
-    "trainer = trainers.UnigramTrainer(\n",
-    "    vocab_size=25000, special_tokens=special_tokens, unk_token=\"<unk>\"\n",
-    ")\n",
-    "tokenizer.train_from_iterator(get_training_corpus(), trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.model = models.Unigram()\n",
-    "tokenizer.train([\"wikitext-2.txt\"], trainer=trainer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Let', \"'\", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer.\")\n",
-    "print(encoding.tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0 1"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cls_token_id = tokenizer.token_to_id(\"<cls>\")\n",
-    "sep_token_id = tokenizer.token_to_id(\"<sep>\")\n",
-    "print(cls_token_id, sep_token_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.post_processor = processors.TemplateProcessing(\n",
-    "    single=\"$A:0 <sep>:0 <cls>:2\",\n",
-    "    pair=\"$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2\",\n",
-    "    special_tokens=[(\"<sep>\", sep_token_id), (\"<cls>\", cls_token_id)],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Let', \"'\", 's', '▁test', '▁this', '▁to', 'ken', 'izer', '.', '.', '.', '<sep>', '▁', 'on', '▁', 'a', '▁pair', \n",
-       "  '▁of', '▁sentence', 's', '!', '<sep>', '<cls>']\n",
-       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "encoding = tokenizer.encode(\"Let's test this tokenizer...\", \"on a pair of sentences!\")\n",
-    "print(encoding.tokens)\n",
-    "print(encoding.type_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.decoder = decoders.Metaspace()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PreTrainedTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = PreTrainedTokenizerFast(\n",
-    "    tokenizer_object=tokenizer,\n",
-    "    bos_token=\"<s>\",\n",
-    "    eos_token=\"</s>\",\n",
-    "    unk_token=\"<unk>\",\n",
-    "    pad_token=\"<pad>\",\n",
-    "    cls_token=\"<cls>\",\n",
-    "    sep_token=\"<sep>\",\n",
-    "    mask_token=\"<mask>\",\n",
-    "    padding_side=\"left\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import XLNetTokenizerFast\n",
-    "\n",
-    "wrapped_tokenizer = XLNetTokenizerFast(tokenizer_object=tokenizer)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "逐块地构建标记器",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section2_pt.ipynb b/course/zh-CN/chapter7/section2_pt.ipynb
deleted file mode 100644
index 8370720a..00000000
--- a/course/zh-CN/chapter7/section2_pt.ipynb
+++ /dev/null
@@ -1,891 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 标记（token）分类 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"conll2003\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 14041\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3250\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3453\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"tokens\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"ner_tags\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], names_file=None, id=None), length=-1, id=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n",
-    "ner_feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label_names = ner_feature.feature.names\n",
-    "label_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'EU    rejects German call to boycott British lamb .'\n",
-       "'B-ORG O       B-MISC O    O  O       B-MISC  O    O'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "words = raw_datasets[\"train\"][0][\"tokens\"]\n",
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "line1 = \"\"\n",
-    "line2 = \"\"\n",
-    "for word, label in zip(words, labels):\n",
-    "    full_label = label_names[label]\n",
-    "    max_length = max(len(word), len(full_label))\n",
-    "    line1 += word + \" \" * (max_length - len(word) + 1)\n",
-    "    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
-    "\n",
-    "print(line1)\n",
-    "print(line2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'la', '##mb', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
-    "inputs.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def align_labels_with_tokens(labels, word_ids):\n",
-    "    new_labels = []\n",
-    "    current_word = None\n",
-    "    for word_id in word_ids:\n",
-    "        if word_id != current_word:\n",
-    "            # Start of a new word!\n",
-    "            current_word = word_id\n",
-    "            label = -100 if word_id is None else labels[word_id]\n",
-    "            new_labels.append(label)\n",
-    "        elif word_id is None:\n",
-    "            # Special token\n",
-    "            new_labels.append(-100)\n",
-    "        else:\n",
-    "            # Same word as previous token\n",
-    "            label = labels[word_id]\n",
-    "            # If the label is B-XXX we change it to I-XXX\n",
-    "            if label % 2 == 1:\n",
-    "                label += 1\n",
-    "            new_labels.append(label)\n",
-    "\n",
-    "    return new_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n",
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "word_ids = inputs.word_ids()\n",
-    "print(labels)\n",
-    "print(align_labels_with_tokens(labels, word_ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_align_labels(examples):\n",
-    "    tokenized_inputs = tokenizer(\n",
-    "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
-    "    )\n",
-    "    all_labels = examples[\"ner_tags\"]\n",
-    "    new_labels = []\n",
-    "    for i, labels in enumerate(all_labels):\n",
-    "        word_ids = tokenized_inputs.word_ids(i)\n",
-    "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
-    "\n",
-    "    tokenized_inputs[\"labels\"] = new_labels\n",
-    "    return tokenized_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize_and_align_labels,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForTokenClassification\n",
-    "\n",
-    "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
-       "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
-       "[-100, 1, 2, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(2):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install seqeval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"seqeval\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "labels = [label_names[i] for i in labels]\n",
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MISC': {'precision': 1.0, 'recall': 0.5, 'f1': 0.67, 'number': 2},\n",
-       " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
-       " 'overall_precision': 1.0,\n",
-       " 'overall_recall': 0.67,\n",
-       " 'overall_f1': 0.8,\n",
-       " 'overall_accuracy': 0.89}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = labels.copy()\n",
-    "predictions[2] = \"O\"\n",
-    "metric.compute(predictions=[predictions], references=[labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_preds):\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "\n",
-    "    # Remove ignored index (special tokens) and convert to labels\n",
-    "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
-    "    true_predictions = [\n",
-    "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
-    "    return {\n",
-    "        \"precision\": all_metrics[\"overall_precision\"],\n",
-    "        \"recall\": all_metrics[\"overall_recall\"],\n",
-    "        \"f1\": all_metrics[\"overall_f1\"],\n",
-    "        \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id2label = {i: label for i, label in enumerate(label_names)}\n",
-    "label2id = {v: k for k, v in id2label.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForTokenClassification\n",
-    "\n",
-    "model = AutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    \"bert-finetuned-ner\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/bert-finetuned-ner/commit/26ab21e5b1568f9afeccdaed2d8715f571d786ed'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/bert-finetuned-ner-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"bert-finetuned-ner-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"bert-finetuned-ner-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess(predictions, labels):\n",
-    "    predictions = predictions.detach().cpu().clone().numpy()\n",
-    "    labels = labels.detach().cpu().clone().numpy()\n",
-    "\n",
-    "    # Remove ignored index (special tokens) and convert to labels\n",
-    "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
-    "    true_predictions = [\n",
-    "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
-    "        for prediction, label in zip(predictions, labels)\n",
-    "    ]\n",
-    "    return true_labels, true_predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    for batch in eval_dataloader:\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        predictions = outputs.logits.argmax(dim=-1)\n",
-    "        labels = batch[\"labels\"]\n",
-    "\n",
-    "        # Necessary to pad predictions and labels for being gathered\n",
-    "        predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)\n",
-    "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
-    "\n",
-    "        predictions_gathered = accelerator.gather(predictions)\n",
-    "        labels_gathered = accelerator.gather(labels)\n",
-    "\n",
-    "        true_predictions, true_labels = postprocess(predictions_gathered, labels_gathered)\n",
-    "        metric.add_batch(predictions=true_predictions, references=true_labels)\n",
-    "\n",
-    "    results = metric.compute()\n",
-    "    print(\n",
-    "        f\"epoch {epoch}:\",\n",
-    "        {\n",
-    "            key: results[f\"overall_{key}\"]\n",
-    "            for key in [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n",
-    "        },\n",
-    "    )\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accelerator.wait_for_everyone()\n",
-    "unwrapped_model = accelerator.unwrap_model(model)\n",
-    "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9988506, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.9647625, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.9986118, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-ner\"\n",
-    "token_classifier = pipeline(\n",
-    "    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",
-    ")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "标记（token）分类 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section2_tf.ipynb b/course/zh-CN/chapter7/section2_tf.ipynb
deleted file mode 100644
index 8cc093bb..00000000
--- a/course/zh-CN/chapter7/section2_tf.ipynb
+++ /dev/null
@@ -1,707 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 标记（token）分类 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"conll2003\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 14041\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3250\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['chunk_tags', 'id', 'ner_tags', 'pos_tags', 'tokens'],\n",
-       "        num_rows: 3453\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"tokens\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"][0][\"ner_tags\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], names_file=None, id=None), length=-1, id=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n",
-    "ner_feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label_names = ner_feature.feature.names\n",
-    "label_names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'EU    rejects German call to boycott British lamb .'\n",
-       "'B-ORG O       B-MISC O    O  O       B-MISC  O    O'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "words = raw_datasets[\"train\"][0][\"tokens\"]\n",
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "line1 = \"\"\n",
-    "line2 = \"\"\n",
-    "for word, label in zip(words, labels):\n",
-    "    full_label = label_names[label]\n",
-    "    max_length = max(len(word), len(full_label))\n",
-    "    line1 += word + \" \" * (max_length - len(word) + 1)\n",
-    "    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
-    "\n",
-    "print(line1)\n",
-    "print(line2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'la', '##mb', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
-    "inputs.tokens()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs.word_ids()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def align_labels_with_tokens(labels, word_ids):\n",
-    "    new_labels = []\n",
-    "    current_word = None\n",
-    "    for word_id in word_ids:\n",
-    "        if word_id != current_word:\n",
-    "            # Start of a new word!\n",
-    "            current_word = word_id\n",
-    "            label = -100 if word_id is None else labels[word_id]\n",
-    "            new_labels.append(label)\n",
-    "        elif word_id is None:\n",
-    "            # Special token\n",
-    "            new_labels.append(-100)\n",
-    "        else:\n",
-    "            # Same word as previous token\n",
-    "            label = labels[word_id]\n",
-    "            # If the label is B-XXX we change it to I-XXX\n",
-    "            if label % 2 == 1:\n",
-    "                label += 1\n",
-    "            new_labels.append(label)\n",
-    "\n",
-    "    return new_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n",
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "word_ids = inputs.word_ids()\n",
-    "print(labels)\n",
-    "print(align_labels_with_tokens(labels, word_ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_and_align_labels(examples):\n",
-    "    tokenized_inputs = tokenizer(\n",
-    "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
-    "    )\n",
-    "    all_labels = examples[\"ner_tags\"]\n",
-    "    new_labels = []\n",
-    "    for i, labels in enumerate(all_labels):\n",
-    "        word_ids = tokenized_inputs.word_ids(i)\n",
-    "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
-    "\n",
-    "    tokenized_inputs[\"labels\"] = new_labels\n",
-    "    return tokenized_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize_and_align_labels,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForTokenClassification\n",
-    "\n",
-    "data_collator = DataCollatorForTokenClassification(\n",
-    "    tokenizer=tokenizer, return_tensors=\"tf\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
-       "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
-       "[-100, 1, 2, -100]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(2):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=16,\n",
-    ")\n",
-    "\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "id2label = {i: label for i, label in enumerate(label_names)}\n",
-    "label2id = {v: k for k, v in id2label.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForTokenClassification\n",
-    "\n",
-    "model = TFAutoModelForTokenClassification.from_pretrained(\n",
-    "    model_checkpoint,\n",
-    "    id2label=id2label,\n",
-    "    label2id=label2id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "# Comment this line out if you're using a GPU that will not benefit from this\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"bert-finetuned-ner\", tokenizer=tokenizer)\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_eval_dataset,\n",
-    "    callbacks=[callback],\n",
-    "    epochs=num_epochs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install seqeval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"seqeval\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
-    "labels = [label_names[i] for i in labels]\n",
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MISC': {'precision': 1.0, 'recall': 0.5, 'f1': 0.67, 'number': 2},\n",
-       " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
-       " 'overall_precision': 1.0,\n",
-       " 'overall_recall': 0.67,\n",
-       " 'overall_f1': 0.8,\n",
-       " 'overall_accuracy': 0.89}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = labels.copy()\n",
-    "predictions[2] = \"O\"\n",
-    "metric.compute(predictions=[predictions], references=[labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'LOC': {'precision': 0.91, 'recall': 0.92, 'f1': 0.91, 'number': 1668},\n",
-       " 'MISC': {'precision': 0.70, 'recall': 0.79, 'f1': 0.74, 'number': 702},\n",
-       " 'ORG': {'precision': 0.85, 'recall': 0.90, 'f1': 0.88, 'number': 1661},\n",
-       " 'PER': {'precision': 0.95, 'recall': 0.95, 'f1': 0.95, 'number': 1617},\n",
-       " 'overall_precision': 0.87,\n",
-       " 'overall_recall': 0.91,\n",
-       " 'overall_f1': 0.89,\n",
-       " 'overall_accuracy': 0.97}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "all_predictions = []\n",
-    "all_labels = []\n",
-    "for batch in tf_eval_dataset:\n",
-    "    logits = model.predict(batch)[\"logits\"]\n",
-    "    labels = batch[\"labels\"]\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    for prediction, label in zip(predictions, labels):\n",
-    "        for predicted_idx, label_idx in zip(prediction, label):\n",
-    "            if label_idx == -100:\n",
-    "                continue\n",
-    "            all_predictions.append(label_names[predicted_idx])\n",
-    "            all_labels.append(label_names[label_idx])\n",
-    "metric.compute(predictions=[all_predictions], references=[all_labels])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'entity_group': 'PER', 'score': 0.9988506, 'word': 'Sylvain', 'start': 11, 'end': 18},\n",
-       " {'entity_group': 'ORG', 'score': 0.9647625, 'word': 'Hugging Face', 'start': 33, 'end': 45},\n",
-       " {'entity_group': 'LOC', 'score': 0.9986118, 'word': 'Brooklyn', 'start': 49, 'end': 57}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-ner\"\n",
-    "token_classifier = pipeline(\n",
-    "    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",
-    ")\n",
-    "token_classifier(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "标记（token）分类 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section3_pt.ipynb b/course/zh-CN/chapter7/section3_pt.ipynb
deleted file mode 100644
index c386f176..00000000
--- a/course/zh-CN/chapter7/section3_pt.ipynb
+++ /dev/null
@@ -1,957 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 微调一个掩码（mask）语言模型 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForMaskedLM\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> DistilBERT number of parameters: 67M'\n",
-       "'>>> BERT number of parameters: 110M'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "distilbert_num_parameters = model.num_parameters() / 1_000_000\n",
-    "print(f\"'>>> DistilBERT number of parameters: {round(distilbert_num_parameters)}M'\")\n",
-    "print(f\"'>>> BERT number of parameters: 110M'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a great [MASK].\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> This is a great deal.'\n",
-       "'>>> This is a great success.'\n",
-       "'>>> This is a great adventure.'\n",
-       "'>>> This is a great idea.'\n",
-       "'>>> This is a great feat.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "token_logits = model(**inputs).logits\n",
-    "# Find the location of [MASK] and extract its logits\n",
-    "mask_token_index = torch.where(inputs[\"input_ids\"] == tokenizer.mask_token_id)[1]\n",
-    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
-    "# Pick the [MASK] candidates with the highest logits\n",
-    "top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()\n",
-    "\n",
-    "for token in top_5_tokens:\n",
-    "    print(f\"'>>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "imdb_dataset = load_dataset(\"imdb\")\n",
-    "imdb_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "'>>> Review: This is your typical Priyadarshan movie--a bunch of loony characters out on some silly mission. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, \"kodokoo\" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. Don\\'t get me wrong; as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Now, the hilarity of his films is fading as he is using the same formula over and over again.<br /><br />Songs are good. Tanushree Datta looks awesome. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Kunal Khemu is OK, and Sharman Joshi is the best.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam\" Peckinpah directed the film. Somehow, his direction is not enough. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.<br /><br />The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. Watch the great Peckinpah films. Skip this mess.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: I saw this movie at the theaters when I was about 6 or 7 years old. I loved it then, and have recently come to own a VHS version. <br /><br />My 4 and 6 year old children love this movie and have been asking again and again to watch it. <br /><br />I have enjoyed watching it again too. Though I have to admit it is not as good on a little TV.<br /><br />I do not have older children so I do not know what they would think of it. <br /><br />The songs are very cute. My daughter keeps singing them over and over.<br /><br />Hope this helps.'\n",
-       "'>>> Label: 1'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
-    "\n",
-    "for row in sample:\n",
-    "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
-    "    print(f\"'>>> Label: {row['label']}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize_function(examples):\n",
-    "    result = tokenizer(examples[\"text\"])\n",
-    "    if tokenizer.is_fast:\n",
-    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Use batched=True to activate fast multithreading!\n",
-    "tokenized_datasets = imdb_dataset.map(\n",
-    "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "512"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.model_max_length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 128"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review 0 length: 200'\n",
-       "'>>> Review 1 length: 559'\n",
-       "'>>> Review 2 length: 192'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Slicing produces a list of lists for each feature\n",
-    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
-    "\n",
-    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
-    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Concatenated reviews length: 951'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatenated_examples = {\n",
-    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
-    "}\n",
-    "total_length = len(concatenated_examples[\"input_ids\"])\n",
-    "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 55'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chunks = {\n",
-    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "    for k, t in concatenated_examples.items()\n",
-    "}\n",
-    "\n",
-    "for chunk in chunks[\"input_ids\"]:\n",
-    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def group_texts(examples):\n",
-    "    # Concatenate all texts\n",
-    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
-    "    # Compute length of concatenated texts\n",
-    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
-    "    # We drop the last chunk if it's smaller than chunk_size\n",
-    "    total_length = (total_length // chunk_size) * chunk_size\n",
-    "    # Split by chunks of max_len\n",
-    "    result = {\n",
-    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "        for k, t in concatenated_examples.items()\n",
-    "    }\n",
-    "    # Create a new labels column\n",
-    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 61289\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 59905\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 122963\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
-    "lm_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\".... at.......... high. a classic line : inspector : i'm here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn't! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "for sample in samples:\n",
-    "    _ = sample.pop(\"word_ids\")\n",
-    "\n",
-    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "import numpy as np\n",
-    "\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "wwm_probability = 0.2\n",
-    "\n",
-    "\n",
-    "def whole_word_masking_data_collator(features):\n",
-    "    for feature in features:\n",
-    "        word_ids = feature.pop(\"word_ids\")\n",
-    "\n",
-    "        # Create a map between words and corresponding token indices\n",
-    "        mapping = collections.defaultdict(list)\n",
-    "        current_word_index = -1\n",
-    "        current_word = None\n",
-    "        for idx, word_id in enumerate(word_ids):\n",
-    "            if word_id is not None:\n",
-    "                if word_id != current_word:\n",
-    "                    current_word = word_id\n",
-    "                    current_word_index += 1\n",
-    "                mapping[current_word_index].append(idx)\n",
-    "\n",
-    "        # Randomly mask words\n",
-    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
-    "        input_ids = feature[\"input_ids\"]\n",
-    "        labels = feature[\"labels\"]\n",
-    "        new_labels = [-100] * len(labels)\n",
-    "        for word_id in np.where(mask)[0]:\n",
-    "            word_id = word_id.item()\n",
-    "            for idx in mapping[word_id]:\n",
-    "                new_labels[idx] = labels[idx]\n",
-    "                input_ids[idx] = tokenizer.mask_token_id\n",
-    "        feature[\"labels\"] = new_labels\n",
-    "\n",
-    "    return default_data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> [CLS] bromwell high is a cartoon comedy [MASK] it ran at the same time as some other programs about school life, such as \" teachers \". my 35 years in the teaching profession lead me to believe that bromwell high\\'s satire is much closer to reality than is \" teachers \". the scramble to survive financially, the insightful students who can see right through their pathetic teachers\\'pomp, the pettiness of the whole situation, all remind me of the schools i knew and their students. when i saw the episode in which a student repeatedly tried to burn down the school, i immediately recalled.....'\n",
-       "\n",
-       "'>>> .... [MASK] [MASK] [MASK] [MASK]....... high. a classic line : inspector : i\\'m here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn\\'t! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "batch = whole_word_masking_data_collator(samples)\n",
-    "\n",
-    "for chunk in batch[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 10000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 1000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_size = 10_000\n",
-    "test_size = int(0.1 * train_size)\n",
-    "\n",
-    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
-    "    train_size=train_size, test_size=test_size, seed=42\n",
-    ")\n",
-    "downsampled_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "batch_size = 64\n",
-    "# Show the training loss with every epoch\n",
-    "logging_steps = len(downsampled_dataset[\"train\"]) // batch_size\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "training_args = TrainingArguments(\n",
-    "    output_dir=f\"{model_name}-finetuned-imdb\",\n",
-    "    overwrite_output_dir=True,\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    weight_decay=0.01,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    push_to_hub=True,\n",
-    "    fp16=True,\n",
-    "    logging_steps=logging_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=training_args,\n",
-    "    train_dataset=downsampled_dataset[\"train\"],\n",
-    "    eval_dataset=downsampled_dataset[\"test\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 21.75"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 11.32"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eval_results = trainer.evaluate()\n",
-    "print(f\">>> Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def insert_random_mask(batch):\n",
-    "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
-    "    masked_inputs = data_collator(features)\n",
-    "    # Create a new \"masked\" column for each column in the dataset\n",
-    "    return {\"masked_\" + k: v.numpy() for k, v in masked_inputs.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downsampled_dataset = downsampled_dataset.remove_columns([\"word_ids\"])\n",
-    "eval_dataset = downsampled_dataset[\"test\"].map(\n",
-    "    insert_random_mask,\n",
-    "    batched=True,\n",
-    "    remove_columns=downsampled_dataset[\"test\"].column_names,\n",
-    ")\n",
-    "eval_dataset = eval_dataset.rename_columns(\n",
-    "    {\n",
-    "        \"masked_input_ids\": \"input_ids\",\n",
-    "        \"masked_attention_mask\": \"attention_mask\",\n",
-    "        \"masked_labels\": \"labels\",\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "batch_size = 64\n",
-    "train_dataloader = DataLoader(\n",
-    "    downsampled_dataset[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    batch_size=batch_size,\n",
-    "    collate_fn=data_collator,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    eval_dataset, batch_size=batch_size, collate_fn=default_data_collator\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'lewtun/distilbert-base-uncased-finetuned-imdb-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import get_full_repo_name\n",
-    "\n",
-    "model_name = \"distilbert-base-uncased-finetuned-imdb-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "output_dir = model_name\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Epoch 0: Perplexity: 11.397545307900472\n",
-       ">>> Epoch 1: Perplexity: 10.904909330983092\n",
-       ">>> Epoch 2: Perplexity: 10.729503505340409"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "import math\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        loss = outputs.loss\n",
-    "        losses.append(accelerator.gather(loss.repeat(batch_size)))\n",
-    "\n",
-    "    losses = torch.cat(losses)\n",
-    "    losses = losses[: len(eval_dataset)]\n",
-    "    try:\n",
-    "        perplexity = math.exp(torch.mean(losses))\n",
-    "    except OverflowError:\n",
-    "        perplexity = float(\"inf\")\n",
-    "\n",
-    "    print(f\">>> Epoch {epoch}: Perplexity: {perplexity}\")\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "mask_filler = pipeline(\n",
-    "    \"fill-mask\", model=\"huggingface-course/distilbert-base-uncased-finetuned-imdb\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> this is a great movie.'\n",
-       "'>>> this is a great film.'\n",
-       "'>>> this is a great story.'\n",
-       "'>>> this is a great movies.'\n",
-       "'>>> this is a great character.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds = mask_filler(text)\n",
-    "\n",
-    "for pred in preds:\n",
-    "    print(f\">>> {pred['sequence']}\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "微调一个掩码（mask）语言模型 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section3_tf.ipynb b/course/zh-CN/chapter7/section3_tf.ipynb
deleted file mode 100644
index d88e0741..00000000
--- a/course/zh-CN/chapter7/section3_tf.ipynb
+++ /dev/null
@@ -1,759 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 微调一个掩码（mask）语言模型 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Model: \"tf_distil_bert_for_masked_lm\"\n",
-       "_________________________________________________________________\n",
-       "Layer (type)                 Output Shape              Param #   \n",
-       "=================================================================\n",
-       "distilbert (TFDistilBertMain multiple                  66362880  \n",
-       "_________________________________________________________________\n",
-       "vocab_transform (Dense)      multiple                  590592    \n",
-       "_________________________________________________________________\n",
-       "vocab_layer_norm (LayerNorma multiple                  1536      \n",
-       "_________________________________________________________________\n",
-       "vocab_projector (TFDistilBer multiple                  23866170  \n",
-       "=================================================================\n",
-       "Total params: 66,985,530\n",
-       "Trainable params: 66,985,530\n",
-       "Non-trainable params: 0\n",
-       "_________________________________________________________________"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model(model.dummy_inputs)  # Build the model\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a great [MASK].\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> This is a great deal.'\n",
-       "'>>> This is a great success.'\n",
-       "'>>> This is a great adventure.'\n",
-       "'>>> This is a great idea.'\n",
-       "'>>> This is a great feat.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"np\")\n",
-    "token_logits = model(**inputs).logits\n",
-    "# Find the location of [MASK] and extract its logits\n",
-    "mask_token_index = np.argwhere(inputs[\"input_ids\"] == tokenizer.mask_token_id)[0, 1]\n",
-    "mask_token_logits = token_logits[0, mask_token_index, :]\n",
-    "# Pick the [MASK] candidates with the highest logits\n",
-    "# We negate the array before argsort to get the largest, not the smallest, logits\n",
-    "top_5_tokens = np.argsort(-mask_token_logits)[:5].tolist()\n",
-    "\n",
-    "for token in top_5_tokens:\n",
-    "    print(f\">>> {text.replace(tokenizer.mask_token, tokenizer.decode([token]))}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['text', 'label'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "imdb_dataset = load_dataset(\"imdb\")\n",
-    "imdb_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "'>>> Review: This is your typical Priyadarshan movie--a bunch of loony characters out on some silly mission. His signature climax has the entire cast of the film coming together and fighting each other in some crazy moshpit over hidden money. Whether it is a winning lottery ticket in Malamaal Weekly, black money in Hera Pheri, \"kodokoo\" in Phir Hera Pheri, etc., etc., the director is becoming ridiculously predictable. Don\\'t get me wrong; as clichéd and preposterous his movies may be, I usually end up enjoying the comedy. However, in most his previous movies there has actually been some good humor, (Hungama and Hera Pheri being noteworthy ones). Now, the hilarity of his films is fading as he is using the same formula over and over again.<br /><br />Songs are good. Tanushree Datta looks awesome. Rajpal Yadav is irritating, and Tusshar is not a whole lot better. Kunal Khemu is OK, and Sharman Joshi is the best.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: Okay, the story makes no sense, the characters lack any dimensionally, the best dialogue is ad-libs about the low quality of movie, the cinematography is dismal, and only editing saves a bit of the muddle, but Sam\" Peckinpah directed the film. Somehow, his direction is not enough. For those who appreciate Peckinpah and his great work, this movie is a disappointment. Even a great cast cannot redeem the time the viewer wastes with this minimal effort.<br /><br />The proper response to the movie is the contempt that the director San Peckinpah, James Caan, Robert Duvall, Burt Young, Bo Hopkins, Arthur Hill, and even Gig Young bring to their work. Watch the great Peckinpah films. Skip this mess.'\n",
-       "'>>> Label: 0'\n",
-       "\n",
-       "'>>> Review: I saw this movie at the theaters when I was about 6 or 7 years old. I loved it then, and have recently come to own a VHS version. <br /><br />My 4 and 6 year old children love this movie and have been asking again and again to watch it. <br /><br />I have enjoyed watching it again too. Though I have to admit it is not as good on a little TV.<br /><br />I do not have older children so I do not know what they would think of it. <br /><br />The songs are very cute. My daughter keeps singing them over and over.<br /><br />Hope this helps.'\n",
-       "'>>> Label: 1'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sample = imdb_dataset[\"train\"].shuffle(seed=42).select(range(3))\n",
-    "\n",
-    "for row in sample:\n",
-    "    print(f\"\\n'>>> Review: {row['text']}'\")\n",
-    "    print(f\"'>>> Label: {row['label']}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 25000\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'word_ids'],\n",
-       "        num_rows: 50000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize_function(examples):\n",
-    "    result = tokenizer(examples[\"text\"])\n",
-    "    if tokenizer.is_fast:\n",
-    "        result[\"word_ids\"] = [result.word_ids(i) for i in range(len(result[\"input_ids\"]))]\n",
-    "    return result\n",
-    "\n",
-    "\n",
-    "# Use batched=True to activate fast multithreading!\n",
-    "tokenized_datasets = imdb_dataset.map(\n",
-    "    tokenize_function, batched=True, remove_columns=[\"text\", \"label\"]\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "512"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.model_max_length"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chunk_size = 128"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review 0 length: 200'\n",
-       "'>>> Review 1 length: 559'\n",
-       "'>>> Review 2 length: 192'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Slicing produces a list of lists for each feature\n",
-    "tokenized_samples = tokenized_datasets[\"train\"][:3]\n",
-    "\n",
-    "for idx, sample in enumerate(tokenized_samples[\"input_ids\"]):\n",
-    "    print(f\"'>>> Review {idx} length: {len(sample)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Concatenated reviews length: 951'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "concatenated_examples = {\n",
-    "    k: sum(tokenized_samples[k], []) for k in tokenized_samples.keys()\n",
-    "}\n",
-    "total_length = len(concatenated_examples[\"input_ids\"])\n",
-    "print(f\"'>>> Concatenated reviews length: {total_length}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 128'\n",
-       "'>>> Chunk length: 55'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chunks = {\n",
-    "    k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "    for k, t in concatenated_examples.items()\n",
-    "}\n",
-    "\n",
-    "for chunk in chunks[\"input_ids\"]:\n",
-    "    print(f\"'>>> Chunk length: {len(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def group_texts(examples):\n",
-    "    # Concatenate all texts\n",
-    "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
-    "    # Compute length of concatenated texts\n",
-    "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
-    "    # We drop the last chunk if it's smaller than chunk_size\n",
-    "    total_length = (total_length // chunk_size) * chunk_size\n",
-    "    # Split by chunks of max_len\n",
-    "    result = {\n",
-    "        k: [t[i : i + chunk_size] for i in range(0, total_length, chunk_size)]\n",
-    "        for k, t in concatenated_examples.items()\n",
-    "    }\n",
-    "    # Create a new labels column\n",
-    "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
-    "    return result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 61289\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 59905\n",
-       "    })\n",
-       "    unsupervised: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 122963\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lm_datasets = tokenized_datasets.map(group_texts, batched=True)\n",
-    "lm_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\".... at.......... high. a classic line : inspector : i'm here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn't! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "for sample in samples:\n",
-    "    _ = sample.pop(\"word_ids\")\n",
-    "\n",
-    "for chunk in data_collator(samples)[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "import numpy as np\n",
-    "\n",
-    "from transformers.data import tf_default_data_collator\n",
-    "\n",
-    "wwm_probability = 0.2\n",
-    "\n",
-    "\n",
-    "def whole_word_masking_data_collator(features):\n",
-    "    for feature in features:\n",
-    "        word_ids = feature.pop(\"word_ids\")\n",
-    "\n",
-    "        # Create a map between words and corresponding token indices\n",
-    "        mapping = collections.defaultdict(list)\n",
-    "        current_word_index = -1\n",
-    "        current_word = None\n",
-    "        for idx, word_id in enumerate(word_ids):\n",
-    "            if word_id is not None:\n",
-    "                if word_id != current_word:\n",
-    "                    current_word = word_id\n",
-    "                    current_word_index += 1\n",
-    "                mapping[current_word_index].append(idx)\n",
-    "\n",
-    "        # Randomly mask words\n",
-    "        mask = np.random.binomial(1, wwm_probability, (len(mapping),))\n",
-    "        input_ids = feature[\"input_ids\"]\n",
-    "        labels = feature[\"labels\"]\n",
-    "        new_labels = [-100] * len(labels)\n",
-    "        for word_id in np.where(mask)[0]:\n",
-    "            word_id = word_id.item()\n",
-    "            for idx in mapping[word_id]:\n",
-    "                new_labels[idx] = labels[idx]\n",
-    "                input_ids[idx] = tokenizer.mask_token_id\n",
-    "        feature[\"labels\"] = new_labels\n",
-    "\n",
-    "    return tf_default_data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> [CLS] bromwell high is a cartoon comedy [MASK] it ran at the same time as some other programs about school life, such as \" teachers \". my 35 years in the teaching profession lead me to believe that bromwell high\\'s satire is much closer to reality than is \" teachers \". the scramble to survive financially, the insightful students who can see right through their pathetic teachers\\'pomp, the pettiness of the whole situation, all remind me of the schools i knew and their students. when i saw the episode in which a student repeatedly tried to burn down the school, i immediately recalled.....'\n",
-       "\n",
-       "'>>> .... [MASK] [MASK] [MASK] [MASK]....... high. a classic line : inspector : i\\'m here to sack one of your teachers. student : welcome to bromwell high. i expect that many adults of my age think that bromwell high is far fetched. what a pity that it isn\\'t! [SEP] [CLS] homelessness ( or houselessness as george carlin stated ) has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school, work, or vote for the matter. most people think of the homeless'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = [lm_datasets[\"train\"][i] for i in range(2)]\n",
-    "batch = whole_word_masking_data_collator(samples)\n",
-    "\n",
-    "for chunk in batch[\"input_ids\"]:\n",
-    "    print(f\"\\n'>>> {tokenizer.decode(chunk)}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 10000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'input_ids', 'labels', 'word_ids'],\n",
-       "        num_rows: 1000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_size = 10_000\n",
-    "test_size = int(0.1 * train_size)\n",
-    "\n",
-    "downsampled_dataset = lm_datasets[\"train\"].train_test_split(\n",
-    "    train_size=train_size, test_size=test_size, seed=42\n",
-    ")\n",
-    "downsampled_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = downsampled_dataset[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "\n",
-    "tf_eval_dataset = downsampled_dataset[\"test\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=32,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "num_train_steps = len(tf_train_dataset)\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=f\"{model_name}-finetuned-imdb\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 21.75"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import math\n",
-    "\n",
-    "eval_loss = model.evaluate(tf_eval_dataset)\n",
-    "print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       ">>> Perplexity: 11.32"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eval_loss = model.evaluate(tf_eval_dataset)\n",
-    "print(f\"Perplexity: {math.exp(eval_loss):.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "mask_filler = pipeline(\n",
-    "    \"fill-mask\", model=\"huggingface-course/distilbert-base-uncased-finetuned-imdb\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> this is a great movie.'\n",
-       "'>>> this is a great film.'\n",
-       "'>>> this is a great story.'\n",
-       "'>>> this is a great movies.'\n",
-       "'>>> this is a great character.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preds = mask_filler(text)\n",
-    "\n",
-    "for pred in preds:\n",
-    "    print(f\">>> {pred['sequence']}\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "微调一个掩码（mask）语言模型 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section4_pt.ipynb b/course/zh-CN/chapter7/section4_pt.ipynb
deleted file mode 100644
index 218fe759..00000000
--- a/course/zh-CN/chapter7/section4_pt.ipynb
+++ /dev/null
@@ -1,963 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 翻译 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "\n",
-    "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 210173\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 189155\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 21018\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
-    "split_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Default to expanded threads',\n",
-       " 'fr': 'Par défaut, développer les fils de discussion'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][1][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut pour les threads élargis'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Unable to import %1 using the OFX importer plugin. This file is not the correct format.',\n",
-       " 'fr': \"Impossible d'importer %1 en utilisant le module d'extension d'importation OFX. Ce fichier n'a pas un format correct.\"}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][172][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le plugin d'importateur OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
-    "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
-    "\n",
-    "inputs = tokenizer(en_sentence)\n",
-    "with tokenizer.as_target_tokenizer():\n",
-    "    targets = tokenizer(fr_sentence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Par', '▁dé', 'f', 'aut', ',', '▁dé', 've', 'lop', 'per', '▁les', '▁fil', 's', '▁de', '▁discussion', '</s>']\n",
-       "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong_targets = tokenizer(fr_sentence)\n",
-    "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
-    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 128\n",
-    "max_target_length = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
-    "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
-    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
-    "\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = split_datasets.map(\n",
-    "    preprocess_function,\n",
-    "    batched=True,\n",
-    "    remove_columns=split_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'input_ids', 'labels', 'decoder_input_ids'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
-    "batch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,  -100,\n",
-       "          -100,  -100,  -100,  -100,  -100,  -100],\n",
-       "        [ 1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,   817,\n",
-       "           550,  7032,  5821,  7907, 12649,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[59513,   577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,\n",
-       "         59513, 59513, 59513, 59513, 59513, 59513],\n",
-       "        [59513,  1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,\n",
-       "           817,   550,  7032,  5821,  7907, 12649]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"decoder_input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[577, 5891, 2, 3184, 16, 2542, 5, 1710, 0]\n",
-       "[1211, 3, 49, 9409, 1211, 3, 29140, 817, 3124, 817, 550, 7032, 5821, 7907, 12649, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(1, 3):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install sacrebleu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"sacrebleu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 46.750469682990165,\n",
-       " 'counts': [11, 6, 4, 3],\n",
-       " 'totals': [12, 11, 10, 9],\n",
-       " 'precisions': [91.67, 54.54, 40.0, 33.33],\n",
-       " 'bp': 0.9200444146293233,\n",
-       " 'sys_len': 12,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\n",
-    "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
-    "]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1.683602693167689,\n",
-       " 'counts': [1, 0, 0, 0],\n",
-       " 'totals': [4, 3, 2, 1],\n",
-       " 'precisions': [25.0, 16.67, 12.5, 12.5],\n",
-       " 'bp': 0.10539922456186433,\n",
-       " 'sys_len': 4,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This This This This\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0,\n",
-       " 'counts': [2, 1, 0, 0],\n",
-       " 'totals': [2, 1, 0, 0],\n",
-       " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
-       " 'bp': 0.004086771438464067,\n",
-       " 'sys_len': 2,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This plugin\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_preds):\n",
-    "    preds, labels = eval_preds\n",
-    "    # In case the model returns more than the prediction logits\n",
-    "    if isinstance(preds, tuple):\n",
-    "        preds = preds[0]\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
-    "\n",
-    "    # Replace -100s in the labels as we can't decode them\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Some simple post-processing\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "\n",
-    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
-    "    return {\"bleu\": result[\"score\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainingArguments\n",
-    "\n",
-    "args = Seq2SeqTrainingArguments(\n",
-    "    f\"marian-finetuned-kde4-en-to-fr\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    per_device_train_batch_size=32,\n",
-    "    per_device_eval_batch_size=64,\n",
-    "    weight_decay=0.01,\n",
-    "    save_total_limit=3,\n",
-    "    num_train_epochs=3,\n",
-    "    predict_with_generate=True,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainer\n",
-    "\n",
-    "trainer = Seq2SeqTrainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 1.6964408159255981,\n",
-       " 'eval_bleu': 39.26865061007616,\n",
-       " 'eval_runtime': 965.8884,\n",
-       " 'eval_samples_per_second': 21.76,\n",
-       " 'eval_steps_per_second': 0.341}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 0.8558505773544312,\n",
-       " 'eval_bleu': 52.94161337775576,\n",
-       " 'eval_runtime': 714.2576,\n",
-       " 'eval_samples_per_second': 29.426,\n",
-       " 'eval_steps_per_second': 0.461,\n",
-       " 'epoch': 3.0}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate(max_length=max_target_length)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/marian-finetuned-kde4-en-to-fr/commit/3601d621e3baae2bc63d3311452535f8f58f6ef3'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(tags=\"translation\", commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "tokenized_datasets.set_format(\"torch\")\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/marian-finetuned-kde4-en-to-fr-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"marian-finetuned-kde4-en-to-fr-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess(predictions, labels):\n",
-    "    predictions = predictions.cpu().numpy()\n",
-    "    labels = labels.cpu().numpy()\n",
-    "\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "\n",
-    "    # Replace -100 in the labels as we can't decode them.\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "    # Some simple post-processing\n",
-    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "    return decoded_preds, decoded_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "epoch 0, BLEU score: 53.47\n",
-       "epoch 1, BLEU score: 54.24\n",
-       "epoch 2, BLEU score: 54.44"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for batch in train_dataloader:\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            generated_tokens = accelerator.unwrap_model(model).generate(\n",
-    "                batch[\"input_ids\"],\n",
-    "                attention_mask=batch[\"attention_mask\"],\n",
-    "                max_length=128,\n",
-    "            )\n",
-    "        labels = batch[\"labels\"]\n",
-    "\n",
-    "        # Necessary to pad predictions and labels for being gathered\n",
-    "        generated_tokens = accelerator.pad_across_processes(\n",
-    "            generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n",
-    "        )\n",
-    "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
-    "\n",
-    "        predictions_gathered = accelerator.gather(generated_tokens)\n",
-    "        labels_gathered = accelerator.gather(labels)\n",
-    "\n",
-    "        decoded_preds, decoded_labels = postprocess(predictions_gathered, labels_gathered)\n",
-    "        metric.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
-    "\n",
-    "    results = metric.compute()\n",
-    "    print(f\"epoch {epoch}, BLEU score: {results['score']:.2f}\")\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut, développer les fils de discussion'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le module externe d'importation OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "翻译 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section4_tf.ipynb b/course/zh-CN/chapter7/section4_tf.ipynb
deleted file mode 100644
index 558d90bf..00000000
--- a/course/zh-CN/chapter7/section4_tf.ipynb
+++ /dev/null
@@ -1,728 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 翻译 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "\n",
-    "raw_datasets = load_dataset(\"kde4\", lang1=\"en\", lang2=\"fr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 210173\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 189155\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['id', 'translation'],\n",
-       "        num_rows: 21018\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets = raw_datasets[\"train\"].train_test_split(train_size=0.9, seed=20)\n",
-    "split_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "split_datasets[\"validation\"] = split_datasets.pop(\"test\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Default to expanded threads',\n",
-       " 'fr': 'Par défaut, développer les fils de discussion'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][1][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut pour les threads élargis'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'en': 'Unable to import %1 using the OFX importer plugin. This file is not the correct format.',\n",
-       " 'fr': \"Impossible d'importer %1 en utilisant le module d'extension d'importation OFX. Ce fichier n'a pas un format correct.\"}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "split_datasets[\"train\"][172][\"translation\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le plugin d'importateur OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"Helsinki-NLP/opus-mt-en-fr\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "en_sentence = split_datasets[\"train\"][1][\"translation\"][\"en\"]\n",
-    "fr_sentence = split_datasets[\"train\"][1][\"translation\"][\"fr\"]\n",
-    "\n",
-    "inputs = tokenizer(en_sentence)\n",
-    "with tokenizer.as_target_tokenizer():\n",
-    "    targets = tokenizer(fr_sentence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁Par', '▁dé', 'f', 'aut', ',', '▁dé', 've', 'lop', 'per', '▁les', '▁fil', 's', '▁de', '▁discussion', '</s>']\n",
-       "['▁Par', '▁défaut', ',', '▁développer', '▁les', '▁fils', '▁de', '▁discussion', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wrong_targets = tokenizer(fr_sentence)\n",
-    "print(tokenizer.convert_ids_to_tokens(wrong_targets[\"input_ids\"]))\n",
-    "print(tokenizer.convert_ids_to_tokens(targets[\"input_ids\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 128\n",
-    "max_target_length = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    inputs = [ex[\"en\"] for ex in examples[\"translation\"]]\n",
-    "    targets = [ex[\"fr\"] for ex in examples[\"translation\"]]\n",
-    "    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)\n",
-    "\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(targets, max_length=max_target_length, truncation=True)\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = split_datasets.map(\n",
-    "    preprocess_function,\n",
-    "    batched=True,\n",
-    "    remove_columns=split_datasets[\"train\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, from_pt=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'input_ids', 'labels', 'decoder_input_ids'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(1, 3)])\n",
-    "batch.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,  -100,\n",
-       "          -100,  -100,  -100,  -100,  -100,  -100],\n",
-       "        [ 1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,   817,\n",
-       "           550,  7032,  5821,  7907, 12649,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"labels\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[59513,   577,  5891,     2,  3184,    16,  2542,     5,  1710,     0,\n",
-       "         59513, 59513, 59513, 59513, 59513, 59513],\n",
-       "        [59513,  1211,     3,    49,  9409,  1211,     3, 29140,   817,  3124,\n",
-       "           817,   550,  7032,  5821,  7907, 12649]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch[\"decoder_input_ids\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[577, 5891, 2, 3184, 16, 2542, 5, 1710, 0]\n",
-       "[1211, 3, 49, 9409, 1211, 3, 29140, 817, 3124, 817, 550, 7032, 5821, 7907, 12649, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for i in range(1, 3):\n",
-    "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install sacrebleu"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"sacrebleu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 46.750469682990165,\n",
-       " 'counts': [11, 6, 4, 3],\n",
-       " 'totals': [12, 11, 10, 9],\n",
-       " 'precisions': [91.67, 54.54, 40.0, 33.33],\n",
-       " 'bp': 0.9200444146293233,\n",
-       " 'sys_len': 12,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\n",
-    "    \"This plugin lets you translate web pages between several languages automatically.\"\n",
-    "]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1.683602693167689,\n",
-       " 'counts': [1, 0, 0, 0],\n",
-       " 'totals': [4, 3, 2, 1],\n",
-       " 'precisions': [25.0, 16.67, 12.5, 12.5],\n",
-       " 'bp': 0.10539922456186433,\n",
-       " 'sys_len': 4,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This This This This\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0,\n",
-       " 'counts': [2, 1, 0, 0],\n",
-       " 'totals': [2, 1, 0, 0],\n",
-       " 'precisions': [100.0, 100.0, 0.0, 0.0],\n",
-       " 'bp': 0.004086771438464067,\n",
-       " 'sys_len': 2,\n",
-       " 'ref_len': 13}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = [\"This plugin\"]\n",
-    "references = [\n",
-    "    [\n",
-    "        \"This plugin allows you to automatically translate web pages between several languages.\"\n",
-    "    ]\n",
-    "]\n",
-    "metric.compute(predictions=predictions, references=references)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics():\n",
-    "    all_preds = []\n",
-    "    all_labels = []\n",
-    "    sampled_dataset = tokenized_datasets[\"validation\"].shuffle().select(range(200))\n",
-    "    tf_generate_dataset = sampled_dataset.to_tf_dataset(\n",
-    "        columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "        collate_fn=data_collator,\n",
-    "        shuffle=False,\n",
-    "        batch_size=4,\n",
-    "    )\n",
-    "    for batch in tf_generate_dataset:\n",
-    "        predictions = model.generate(\n",
-    "            input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n",
-    "        )\n",
-    "        decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "        labels = batch[\"labels\"].numpy()\n",
-    "        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "        decoded_preds = [pred.strip() for pred in decoded_preds]\n",
-    "        decoded_labels = [[label.strip()] for label in decoded_labels]\n",
-    "        all_preds.extend(decoded_preds)\n",
-    "        all_labels.extend(decoded_labels)\n",
-    "\n",
-    "    result = metric.compute(predictions=all_preds, references=all_labels)\n",
-    "    return {\"bleu\": result[\"score\"]}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(compute_metrics())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=\"marian-finetuned-kde4-en-to-fr\", tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_eval_dataset,\n",
-    "    callbacks=[callback],\n",
-    "    epochs=num_epochs,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(compute_metrics())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': 'Par défaut, développer les fils de discussion'}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/marian-finetuned-kde4-en-to-fr\"\n",
-    "translator = pipeline(\"translation\", model=model_checkpoint)\n",
-    "translator(\"Default to expanded threads\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'translation_text': \"Impossible d'importer %1 en utilisant le module externe d'importation OFX. Ce fichier n'est pas le bon format.\"}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "translator(\n",
-    "    \"Unable to import %1 using the OFX importer plugin. This file is not the correct format.\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "翻译 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section5_pt.ipynb b/course/zh-CN/chapter7/section5_pt.ipynb
deleted file mode 100644
index 2729edde..00000000
--- a/course/zh-CN/chapter7/section5_pt.ipynb
+++ /dev/null
@@ -1,1035 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 文本摘要 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 200000\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "spanish_dataset = load_dataset(\"amazon_reviews_multi\", \"es\")\n",
-    "english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n",
-    "english_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Worked in front position, not rear'\n",
-       "'>> Review: 3 stars because these are not rear brakes as stated in the item description. At least the mount adapter only worked on the front fork of the bike that I got it for.'\n",
-       "\n",
-       "'>> Title: meh'\n",
-       "'>> Review: Does it’s job and it’s gorgeous but mine is falling apart, I had to basically put it together again with hot glue'\n",
-       "\n",
-       "'>> Title: Can\\'t beat these for the money'\n",
-       "'>> Review: Bought this for handling miscellaneous aircraft parts and hanger \"stuff\" that I needed to organize; it really fit the bill. The unit arrived quickly, was well packaged and arrived intact (always a good sign). There are five wall mounts-- three on the top and two on the bottom. I wanted to mount it on the wall, so all I had to do was to remove the top two layers of plastic drawers, as well as the bottom corner drawers, place it when I wanted and mark it; I then used some of the new plastic screw in wall anchors (the 50 pound variety) and it easily mounted to the wall. Some have remarked that they wanted dividers for the drawers, and that they made those. Good idea. My application was that I needed something that I can see the contents at about eye level, so I wanted the fuller-sized drawers. I also like that these are the new plastic that doesn\\'t get brittle and split like my older plastic drawers did. I like the all-plastic construction. It\\'s heavy duty enough to hold metal parts, but being made of plastic it\\'s not as heavy as a metal frame, so you can easily mount it to the wall and still load it up with heavy stuff, or light stuff. No problem there. For the money, you can\\'t beat it. Best one of these I\\'ve bought to date-- and I\\'ve been using some version of these for over forty years.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def show_samples(dataset, num_samples=3, seed=42):\n",
-    "    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n",
-    "    for example in sample:\n",
-    "        print(f\"\\n'>> Title: {example['review_title']}'\")\n",
-    "        print(f\"'>> Review: {example['review_body']}'\")\n",
-    "\n",
-    "\n",
-    "show_samples(english_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "home                      17679\n",
-       "apparel                   15951\n",
-       "wireless                  15717\n",
-       "other                     13418\n",
-       "beauty                    12091\n",
-       "drugstore                 11730\n",
-       "kitchen                   10382\n",
-       "toy                        8745\n",
-       "sports                     8277\n",
-       "automotive                 7506\n",
-       "lawn_and_garden            7327\n",
-       "home_improvement           7136\n",
-       "pet_products               7082\n",
-       "digital_ebook_purchase     6749\n",
-       "pc                         6401\n",
-       "electronics                6186\n",
-       "office_product             5521\n",
-       "shoes                      5197\n",
-       "grocery                    4730\n",
-       "book                       3756\n",
-       "Name: product_category, dtype: int64"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "english_dataset.set_format(\"pandas\")\n",
-    "english_df = english_dataset[\"train\"][:]\n",
-    "# Show counts for top 20 products\n",
-    "english_df[\"product_category\"].value_counts()[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_books(example):\n",
-    "    return (\n",
-    "        example[\"product_category\"] == \"book\"\n",
-    "        or example[\"product_category\"] == \"digital_ebook_purchase\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "english_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: I\\'m dissapointed.'\n",
-       "'>> Review: I guess I had higher expectations for this book from the reviews. I really thought I\\'d at least like it. The plot idea was great. I loved Ash but, it just didnt go anywhere. Most of the book was about their radio show and talking to callers. I wanted the author to dig deeper so we could really get to know the characters. All we know about Grace is that she is attractive looking, Latino and is kind of a brat. I\\'m dissapointed.'\n",
-       "\n",
-       "'>> Title: Good art, good price, poor design'\n",
-       "'>> Review: I had gotten the DC Vintage calendar the past two years, but it was on backorder forever this year and I saw they had shrunk the dimensions for no good reason. This one has good art choices but the design has the fold going through the picture, so it\\'s less aesthetically pleasing, especially if you want to keep a picture to hang. For the price, a good calendar'\n",
-       "\n",
-       "'>> Title: Helpful'\n",
-       "'>> Review: Nearly all the tips useful and. I consider myself an intermediate to advanced user of OneNote. I would highly recommend.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spanish_books = spanish_dataset.filter(filter_books)\n",
-    "english_books = english_dataset.filter(filter_books)\n",
-    "show_samples(english_books)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Easy to follow!!!!'\n",
-       "'>> Review: I loved The dash diet weight loss Solution. Never hungry. I would recommend this diet. Also the menus are well rounded. Try it. Has lots of the information need thanks.'\n",
-       "\n",
-       "'>> Title: PARCIALMENTE DAÑADO'\n",
-       "'>> Review: Me llegó el día que tocaba, junto a otros libros que pedí, pero la caja llegó en mal estado lo cual dañó las esquinas de los libros porque venían sin protección (forro).'\n",
-       "\n",
-       "'>> Title: no lo he podido descargar'\n",
-       "'>> Review: igual que el anterior'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import concatenate_datasets, DatasetDict\n",
-    "\n",
-    "books_dataset = DatasetDict()\n",
-    "\n",
-    "for split in english_books.keys():\n",
-    "    books_dataset[split] = concatenate_datasets(\n",
-    "        [english_books[split], spanish_books[split]]\n",
-    "    )\n",
-    "    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n",
-    "\n",
-    "# Peek at a few examples\n",
-    "show_samples(books_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"google/mt5-small\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [336, 259, 28387, 11807, 287, 62893, 295, 12507, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"I loved reading the Hunger Games!\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁I', '▁', 'loved', '▁reading', '▁the', '▁Hung', 'er', '▁Games', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs.input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 512\n",
-    "max_target_length = 30\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    model_inputs = tokenizer(\n",
-    "        examples[\"review_body\"], max_length=max_input_length, truncation=True\n",
-    "    )\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(\n",
-    "            examples[\"review_title\"], max_length=max_target_length, truncation=True\n",
-    "        )\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generated_summary = \"I absolutely loved reading the Hunger Games\"\n",
-    "reference_summary = \"I loved reading the Hunger Games\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install rouge_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "rouge_score = load_metric(\"rouge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rouge2': AggregateScore(low=Score(precision=0.67, recall=0.8, fmeasure=0.73), mid=Score(precision=0.67, recall=0.8, fmeasure=0.73), high=Score(precision=0.67, recall=0.8, fmeasure=0.73)),\n",
-       " 'rougeL': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rougeLsum': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92))}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = rouge_score.compute(\n",
-    "    predictions=[generated_summary], references=[reference_summary]\n",
-    ")\n",
-    "scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Score(precision=0.86, recall=1.0, fmeasure=0.92)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores[\"rouge1\"].mid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install nltk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk\n",
-    "\n",
-    "nltk.download(\"punkt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'I grew up reading Koontz, and years ago, I stopped,convinced i had \"outgrown\" him.'\n",
-       "'Still,when a friend was looking for something suspenseful too read, I suggested Koontz.'\n",
-       "'She found Strangers.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "\n",
-    "\n",
-    "def three_sentence_summary(text):\n",
-    "    return \"\\n\".join(sent_tokenize(text)[:3])\n",
-    "\n",
-    "\n",
-    "print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_baseline(dataset, metric):\n",
-    "    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n",
-    "    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': 16.74, 'rouge2': 8.83, 'rougeL': 15.6, 'rougeLsum': 15.96}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n",
-    "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
-    "rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n",
-    "rouge_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainingArguments\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_train_epochs = 8\n",
-    "# Show the training loss with every epoch\n",
-    "logging_steps = len(tokenized_datasets[\"train\"]) // batch_size\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "args = Seq2SeqTrainingArguments(\n",
-    "    output_dir=f\"{model_name}-finetuned-amazon-en-es\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    learning_rate=5.6e-5,\n",
-    "    per_device_train_batch_size=batch_size,\n",
-    "    per_device_eval_batch_size=batch_size,\n",
-    "    weight_decay=0.01,\n",
-    "    save_total_limit=3,\n",
-    "    num_train_epochs=num_train_epochs,\n",
-    "    predict_with_generate=True,\n",
-    "    logging_steps=logging_steps,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    # Decode generated summaries into text\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "    # Replace -100 in the labels as we can't decode them\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    # Decode reference summaries into text\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "    # ROUGE expects a newline after each sentence\n",
-    "    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
-    "    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n",
-    "    # Compute ROUGE scores\n",
-    "    result = rouge_score.compute(\n",
-    "        predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n",
-    "    )\n",
-    "    # Extract the median scores\n",
-    "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "    return {k: round(v, 4) for k, v in result.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
-    "    books_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[  1494,    259,   8622,    390,    259,    262,   2316,   3435,    955,\n",
-       "            772,    281,    772,   1617,    263,    305,  14701,    260,   1385,\n",
-       "           3031,    259,  24146,    332,   1037,    259,  43906,    305,    336,\n",
-       "            260,      1,      0,      0,      0,      0,      0,      0],\n",
-       "        [   259,  27531,  13483,    259,   7505,    260, 112240,  15192,    305,\n",
-       "          53198,    276,    259,  74060,    263,    260,    459,  25640,    776,\n",
-       "           2119,    336,    259,   2220,    259,  18896,    288,   4906,    288,\n",
-       "           1037,   3931,    260,   7083, 101476,   1143,    260,      1]]), 'labels': tensor([[ 7483,   259,  2364, 15695,     1,  -100],\n",
-       "        [  259, 27531, 13483,   259,  7505,     1]]), 'decoder_input_ids': tensor([[    0,  7483,   259,  2364, 15695,     1],\n",
-       "        [    0,   259, 27531, 13483,   259,  7505]])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n",
-    "data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Seq2SeqTrainer\n",
-    "\n",
-    "trainer = Seq2SeqTrainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'eval_loss': 3.028524398803711,\n",
-       " 'eval_rouge1': 16.9728,\n",
-       " 'eval_rouge2': 8.2969,\n",
-       " 'eval_rougeL': 16.8366,\n",
-       " 'eval_rougeLsum': 16.851,\n",
-       " 'eval_gen_len': 10.1597,\n",
-       " 'eval_runtime': 6.1054,\n",
-       " 'eval_samples_per_second': 38.982,\n",
-       " 'eval_steps_per_second': 4.914}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets.set_format(\"torch\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "batch_size = 8\n",
-    "train_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"train\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=batch_size,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    tokenized_datasets[\"validation\"], collate_fn=data_collator, batch_size=batch_size\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator()\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 10\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def postprocess_text(preds, labels):\n",
-    "    preds = [pred.strip() for pred in preds]\n",
-    "    labels = [label.strip() for label in labels]\n",
-    "\n",
-    "    # ROUGE expects a newline after each sentence\n",
-    "    preds = [\"\\n\".join(nltk.sent_tokenize(pred)) for pred in preds]\n",
-    "    labels = [\"\\n\".join(nltk.sent_tokenize(label)) for label in labels]\n",
-    "\n",
-    "    return preds, labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'lewtun/mt5-finetuned-amazon-en-es-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import get_full_repo_name\n",
-    "\n",
-    "model_name = \"test-bert-finetuned-squad-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "output_dir = \"results-mt5-finetuned-squad-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Epoch 0: {'rouge1': 5.6351, 'rouge2': 1.1625, 'rougeL': 5.4866, 'rougeLsum': 5.5005}\n",
-       "Epoch 1: {'rouge1': 9.8646, 'rouge2': 3.4106, 'rougeL': 9.9439, 'rougeLsum': 9.9306}\n",
-       "Epoch 2: {'rouge1': 11.0872, 'rouge2': 3.3273, 'rougeL': 11.0508, 'rougeLsum': 10.9468}\n",
-       "Epoch 3: {'rouge1': 11.8587, 'rouge2': 4.8167, 'rougeL': 11.7986, 'rougeLsum': 11.7518}\n",
-       "Epoch 4: {'rouge1': 12.9842, 'rouge2': 5.5887, 'rougeL': 12.7546, 'rougeLsum': 12.7029}\n",
-       "Epoch 5: {'rouge1': 13.4628, 'rouge2': 6.4598, 'rougeL': 13.312, 'rougeLsum': 13.2913}\n",
-       "Epoch 6: {'rouge1': 12.9131, 'rouge2': 5.8914, 'rougeL': 12.6896, 'rougeLsum': 12.5701}\n",
-       "Epoch 7: {'rouge1': 13.3079, 'rouge2': 6.2994, 'rougeL': 13.1536, 'rougeLsum': 13.1194}\n",
-       "Epoch 8: {'rouge1': 13.96, 'rouge2': 6.5998, 'rougeL': 13.9123, 'rougeLsum': 13.7744}\n",
-       "Epoch 9: {'rouge1': 14.1192, 'rouge2': 7.0059, 'rougeL': 14.1172, 'rougeLsum': 13.9509}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "import numpy as np\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for step, batch in enumerate(train_dataloader):\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            generated_tokens = accelerator.unwrap_model(model).generate(\n",
-    "                batch[\"input_ids\"],\n",
-    "                attention_mask=batch[\"attention_mask\"],\n",
-    "            )\n",
-    "\n",
-    "            generated_tokens = accelerator.pad_across_processes(\n",
-    "                generated_tokens, dim=1, pad_index=tokenizer.pad_token_id\n",
-    "            )\n",
-    "            labels = batch[\"labels\"]\n",
-    "\n",
-    "            # If we did not pad to max length, we need to pad the labels too\n",
-    "            labels = accelerator.pad_across_processes(\n",
-    "                batch[\"labels\"], dim=1, pad_index=tokenizer.pad_token_id\n",
-    "            )\n",
-    "\n",
-    "            generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()\n",
-    "            labels = accelerator.gather(labels).cpu().numpy()\n",
-    "\n",
-    "            # Replace -100 in the labels as we can't decode them\n",
-    "            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "            if isinstance(generated_tokens, tuple):\n",
-    "                generated_tokens = generated_tokens[0]\n",
-    "            decoded_preds = tokenizer.batch_decode(\n",
-    "                generated_tokens, skip_special_tokens=True\n",
-    "            )\n",
-    "            decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "\n",
-    "            decoded_preds, decoded_labels = postprocess_text(\n",
-    "                decoded_preds, decoded_labels\n",
-    "            )\n",
-    "\n",
-    "            rouge_score.add_batch(predictions=decoded_preds, references=decoded_labels)\n",
-    "\n",
-    "    # Compute metrics\n",
-    "    result = rouge_score.compute()\n",
-    "    # Extract the median ROUGE scores\n",
-    "    result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "    result = {k: round(v, 4) for k, v in result.items()}\n",
-    "    print(f\"Epoch {epoch}:\", result)\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-es\"\n",
-    "summarizer = pipeline(\"summarization\", model=hub_model_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_summary(idx):\n",
-    "    review = books_dataset[\"test\"][idx][\"review_body\"]\n",
-    "    title = books_dataset[\"test\"][idx][\"review_title\"]\n",
-    "    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n",
-    "    print(f\"'>>> Review: {review}'\")\n",
-    "    print(f\"\\n'>>> Title: {title}'\")\n",
-    "    print(f\"\\n'>>> Summary: {summary}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Nothing special at all about this product... the book is too small and stiff and hard to write in. The huge sticker on the back doesn’t come off and looks super tacky. I would not purchase this again. I could have just bought a journal from the dollar store and it would be basically the same thing. It’s also really expensive for what it is.'\n",
-       "\n",
-       "'>>> Title: Not impressed at all... buy something else'\n",
-       "\n",
-       "'>>> Summary: Nothing special at all about this product'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Es una trilogia que se hace muy facil de leer. Me ha gustado, no me esperaba el final para nada'\n",
-       "\n",
-       "'>>> Title: Buena literatura para adolescentes'\n",
-       "\n",
-       "'>>> Summary: Muy facil de leer'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(0)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "文本摘要 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section5_tf.ipynb b/course/zh-CN/chapter7/section5_tf.ipynb
deleted file mode 100644
index 8a8ff51d..00000000
--- a/course/zh-CN/chapter7/section5_tf.ipynb
+++ /dev/null
@@ -1,784 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 文本摘要 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 200000\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category'],\n",
-       "        num_rows: 5000\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "spanish_dataset = load_dataset(\"amazon_reviews_multi\", \"es\")\n",
-    "english_dataset = load_dataset(\"amazon_reviews_multi\", \"en\")\n",
-    "english_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Worked in front position, not rear'\n",
-       "'>> Review: 3 stars because these are not rear brakes as stated in the item description. At least the mount adapter only worked on the front fork of the bike that I got it for.'\n",
-       "\n",
-       "'>> Title: meh'\n",
-       "'>> Review: Does it’s job and it’s gorgeous but mine is falling apart, I had to basically put it together again with hot glue'\n",
-       "\n",
-       "'>> Title: Can\\'t beat these for the money'\n",
-       "'>> Review: Bought this for handling miscellaneous aircraft parts and hanger \"stuff\" that I needed to organize; it really fit the bill. The unit arrived quickly, was well packaged and arrived intact (always a good sign). There are five wall mounts-- three on the top and two on the bottom. I wanted to mount it on the wall, so all I had to do was to remove the top two layers of plastic drawers, as well as the bottom corner drawers, place it when I wanted and mark it; I then used some of the new plastic screw in wall anchors (the 50 pound variety) and it easily mounted to the wall. Some have remarked that they wanted dividers for the drawers, and that they made those. Good idea. My application was that I needed something that I can see the contents at about eye level, so I wanted the fuller-sized drawers. I also like that these are the new plastic that doesn\\'t get brittle and split like my older plastic drawers did. I like the all-plastic construction. It\\'s heavy duty enough to hold metal parts, but being made of plastic it\\'s not as heavy as a metal frame, so you can easily mount it to the wall and still load it up with heavy stuff, or light stuff. No problem there. For the money, you can\\'t beat it. Best one of these I\\'ve bought to date-- and I\\'ve been using some version of these for over forty years.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def show_samples(dataset, num_samples=3, seed=42):\n",
-    "    sample = dataset[\"train\"].shuffle(seed=seed).select(range(num_samples))\n",
-    "    for example in sample:\n",
-    "        print(f\"\\n'>> Title: {example['review_title']}'\")\n",
-    "        print(f\"'>> Review: {example['review_body']}'\")\n",
-    "\n",
-    "\n",
-    "show_samples(english_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "home                      17679\n",
-       "apparel                   15951\n",
-       "wireless                  15717\n",
-       "other                     13418\n",
-       "beauty                    12091\n",
-       "drugstore                 11730\n",
-       "kitchen                   10382\n",
-       "toy                        8745\n",
-       "sports                     8277\n",
-       "automotive                 7506\n",
-       "lawn_and_garden            7327\n",
-       "home_improvement           7136\n",
-       "pet_products               7082\n",
-       "digital_ebook_purchase     6749\n",
-       "pc                         6401\n",
-       "electronics                6186\n",
-       "office_product             5521\n",
-       "shoes                      5197\n",
-       "grocery                    4730\n",
-       "book                       3756\n",
-       "Name: product_category, dtype: int64"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "english_dataset.set_format(\"pandas\")\n",
-    "english_df = english_dataset[\"train\"][:]\n",
-    "# Show counts for top 20 products\n",
-    "english_df[\"product_category\"].value_counts()[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_books(example):\n",
-    "    return (\n",
-    "        example[\"product_category\"] == \"book\"\n",
-    "        or example[\"product_category\"] == \"digital_ebook_purchase\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "english_dataset.reset_format()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: I\\'m dissapointed.'\n",
-       "'>> Review: I guess I had higher expectations for this book from the reviews. I really thought I\\'d at least like it. The plot idea was great. I loved Ash but, it just didnt go anywhere. Most of the book was about their radio show and talking to callers. I wanted the author to dig deeper so we could really get to know the characters. All we know about Grace is that she is attractive looking, Latino and is kind of a brat. I\\'m dissapointed.'\n",
-       "\n",
-       "'>> Title: Good art, good price, poor design'\n",
-       "'>> Review: I had gotten the DC Vintage calendar the past two years, but it was on backorder forever this year and I saw they had shrunk the dimensions for no good reason. This one has good art choices but the design has the fold going through the picture, so it\\'s less aesthetically pleasing, especially if you want to keep a picture to hang. For the price, a good calendar'\n",
-       "\n",
-       "'>> Title: Helpful'\n",
-       "'>> Review: Nearly all the tips useful and. I consider myself an intermediate to advanced user of OneNote. I would highly recommend.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "spanish_books = spanish_dataset.filter(filter_books)\n",
-    "english_books = english_dataset.filter(filter_books)\n",
-    "show_samples(english_books)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>> Title: Easy to follow!!!!'\n",
-       "'>> Review: I loved The dash diet weight loss Solution. Never hungry. I would recommend this diet. Also the menus are well rounded. Try it. Has lots of the information need thanks.'\n",
-       "\n",
-       "'>> Title: PARCIALMENTE DAÑADO'\n",
-       "'>> Review: Me llegó el día que tocaba, junto a otros libros que pedí, pero la caja llegó en mal estado lo cual dañó las esquinas de los libros porque venían sin protección (forro).'\n",
-       "\n",
-       "'>> Title: no lo he podido descargar'\n",
-       "'>> Review: igual que el anterior'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import concatenate_datasets, DatasetDict\n",
-    "\n",
-    "books_dataset = DatasetDict()\n",
-    "\n",
-    "for split in english_books.keys():\n",
-    "    books_dataset[split] = concatenate_datasets(\n",
-    "        [english_books[split], spanish_books[split]]\n",
-    "    )\n",
-    "    books_dataset[split] = books_dataset[split].shuffle(seed=42)\n",
-    "\n",
-    "# Peek at a few examples\n",
-    "show_samples(books_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "books_dataset = books_dataset.filter(lambda x: len(x[\"review_title\"].split()) > 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"google/mt5-small\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [336, 259, 28387, 11807, 287, 62893, 295, 12507, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"I loved reading the Hunger Games!\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['▁I', '▁', 'loved', '▁reading', '▁the', '▁Hung', 'er', '▁Games', '</s>']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs.input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_input_length = 512\n",
-    "max_target_length = 30\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    model_inputs = tokenizer(\n",
-    "        examples[\"review_body\"], max_length=max_input_length, truncation=True\n",
-    "    )\n",
-    "    # Set up the tokenizer for targets\n",
-    "    with tokenizer.as_target_tokenizer():\n",
-    "        labels = tokenizer(\n",
-    "            examples[\"review_title\"], max_length=max_target_length, truncation=True\n",
-    "        )\n",
-    "\n",
-    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-    "    return model_inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = books_dataset.map(preprocess_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generated_summary = \"I absolutely loved reading the Hunger Games\"\n",
-    "reference_summary = \"I loved reading the Hunger Games\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install rouge_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "rouge_score = load_metric(\"rouge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rouge2': AggregateScore(low=Score(precision=0.67, recall=0.8, fmeasure=0.73), mid=Score(precision=0.67, recall=0.8, fmeasure=0.73), high=Score(precision=0.67, recall=0.8, fmeasure=0.73)),\n",
-       " 'rougeL': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92)),\n",
-       " 'rougeLsum': AggregateScore(low=Score(precision=0.86, recall=1.0, fmeasure=0.92), mid=Score(precision=0.86, recall=1.0, fmeasure=0.92), high=Score(precision=0.86, recall=1.0, fmeasure=0.92))}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores = rouge_score.compute(\n",
-    "    predictions=[generated_summary], references=[reference_summary]\n",
-    ")\n",
-    "scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Score(precision=0.86, recall=1.0, fmeasure=0.92)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scores[\"rouge1\"].mid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install nltk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import nltk\n",
-    "\n",
-    "nltk.download(\"punkt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'I grew up reading Koontz, and years ago, I stopped,convinced i had \"outgrown\" him.'\n",
-       "'Still,when a friend was looking for something suspenseful too read, I suggested Koontz.'\n",
-       "'She found Strangers.'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nltk.tokenize import sent_tokenize\n",
-    "\n",
-    "\n",
-    "def three_sentence_summary(text):\n",
-    "    return \"\\n\".join(sent_tokenize(text)[:3])\n",
-    "\n",
-    "\n",
-    "print(three_sentence_summary(books_dataset[\"train\"][1][\"review_body\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_baseline(dataset, metric):\n",
-    "    summaries = [three_sentence_summary(text) for text in dataset[\"review_body\"]]\n",
-    "    return metric.compute(predictions=summaries, references=dataset[\"review_title\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'rouge1': 16.74, 'rouge2': 8.83, 'rougeL': 15.6, 'rougeLsum': 15.96}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "score = evaluate_baseline(books_dataset[\"validation\"], rouge_score)\n",
-    "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
-    "rouge_dict = dict((rn, round(score[rn].mid.fmeasure * 100, 2)) for rn in rouge_names)\n",
-    "rouge_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSeq2SeqLM\n",
-    "\n",
-    "model = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForSeq2Seq\n",
-    "\n",
-    "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
-    "    books_dataset[\"train\"].column_names\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\n",
-       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]), 'input_ids': tensor([[  1494,    259,   8622,    390,    259,    262,   2316,   3435,    955,\n",
-       "            772,    281,    772,   1617,    263,    305,  14701,    260,   1385,\n",
-       "           3031,    259,  24146,    332,   1037,    259,  43906,    305,    336,\n",
-       "            260,      1,      0,      0,      0,      0,      0,      0],\n",
-       "        [   259,  27531,  13483,    259,   7505,    260, 112240,  15192,    305,\n",
-       "          53198,    276,    259,  74060,    263,    260,    459,  25640,    776,\n",
-       "           2119,    336,    259,   2220,    259,  18896,    288,   4906,    288,\n",
-       "           1037,   3931,    260,   7083, 101476,   1143,    260,      1]]), 'labels': tensor([[ 7483,   259,  2364, 15695,     1,  -100],\n",
-       "        [  259, 27531, 13483,   259,  7505,     1]]), 'decoder_input_ids': tensor([[    0,  7483,   259,  2364, 15695,     1],\n",
-       "        [    0,   259, 27531, 13483,   259,  7505]])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "features = [tokenized_datasets[\"train\"][i] for i in range(2)]\n",
-    "data_collator(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_epochs = 8\n",
-    "num_train_steps = len(tf_train_dataset) * num_train_epochs\n",
-    "model_name = model_checkpoint.split(\"/\")[-1]\n",
-    "\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5.6e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    output_dir=f\"{model_name}-finetuned-amazon-en-es\", tokenizer=tokenizer\n",
-    ")\n",
-    "\n",
-    "model.fit(\n",
-    "    tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback], epochs=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm\n",
-    "import numpy as np\n",
-    "\n",
-    "all_preds = []\n",
-    "all_labels = []\n",
-    "for batch in tqdm(tf_eval_dataset):\n",
-    "    predictions = model.generate(**batch)\n",
-    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
-    "    labels = batch[\"labels\"].numpy()\n",
-    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
-    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
-    "    decoded_preds = [\"\\n\".join(sent_tokenize(pred.strip())) for pred in decoded_preds]\n",
-    "    decoded_labels = [\"\\n\".join(sent_tokenize(label.strip())) for label in decoded_labels]\n",
-    "    all_preds.extend(decoded_preds)\n",
-    "    all_labels.extend(decoded_labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = rouge_score.compute(\n",
-    "    predictions=decoded_preds, references=decoded_labels, use_stemmer=True\n",
-    ")\n",
-    "result = {key: value.mid.fmeasure * 100 for key, value in result.items()}\n",
-    "{k: round(v, 4) for k, v in result.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "hub_model_id = \"huggingface-course/mt5-small-finetuned-amazon-en-es\"\n",
-    "summarizer = pipeline(\"summarization\", model=hub_model_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def print_summary(idx):\n",
-    "    review = books_dataset[\"test\"][idx][\"review_body\"]\n",
-    "    title = books_dataset[\"test\"][idx][\"review_title\"]\n",
-    "    summary = summarizer(books_dataset[\"test\"][idx][\"review_body\"])[0][\"summary_text\"]\n",
-    "    print(f\"'>>> Review: {review}'\")\n",
-    "    print(f\"\\n'>>> Title: {title}'\")\n",
-    "    print(f\"\\n'>>> Summary: {summary}'\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Nothing special at all about this product... the book is too small and stiff and hard to write in. The huge sticker on the back doesn’t come off and looks super tacky. I would not purchase this again. I could have just bought a journal from the dollar store and it would be basically the same thing. It’s also really expensive for what it is.'\n",
-       "\n",
-       "'>>> Title: Not impressed at all... buy something else'\n",
-       "\n",
-       "'>>> Summary: Nothing special at all about this product'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'>>> Review: Es una trilogia que se hace muy facil de leer. Me ha gustado, no me esperaba el final para nada'\n",
-       "\n",
-       "'>>> Title: Buena literatura para adolescentes'\n",
-       "\n",
-       "'>>> Summary: Muy facil de leer'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print_summary(0)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "文本摘要 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section6_pt.ipynb b/course/zh-CN/chapter7/section6_pt.ipynb
deleted file mode 100644
index a1737c5a..00000000
--- a/course/zh-CN/chapter7/section6_pt.ipynb
+++ /dev/null
@@ -1,888 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 从头开始训练因果语言模型 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def any_keyword_in_string(string, keywords):\n",
-    "    for keyword in keywords:\n",
-    "        if keyword in string:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "example_1 = \"import numpy as np\"\n",
-    "example_2 = \"import pandas as pd\"\n",
-    "\n",
-    "print(\n",
-    "    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_streaming_dataset(dataset, filters):\n",
-    "    filtered_dict = defaultdict(list)\n",
-    "    total = 0\n",
-    "    for sample in tqdm(iter(dataset)):\n",
-    "        total += 1\n",
-    "        if any_keyword_in_string(sample[\"content\"], filters):\n",
-    "            for k, v in sample.items():\n",
-    "                filtered_dict[k].append(v)\n",
-    "    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n",
-    "    return Dataset.from_dict(filtered_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.26% of data after filtering."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This cell will take a very long time to execute, so you should skip it and go to\n",
-    "# the next one!\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "split = \"train\"  # \"valid\"\n",
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "\n",
-    "data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n",
-    "filtered_data = filter_streaming_dataset(data, filters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 606720\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 3322\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, DatasetDict\n",
-    "\n",
-    "ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n",
-    "ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"train\")\n",
-    "\n",
-    "raw_datasets = DatasetDict(\n",
-    "    {\n",
-    "        \"train\": ds_train,  # .shuffle().select(range(50000)),\n",
-    "        \"valid\": ds_valid,  # .shuffle().select(range(500))\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'REPO_NAME: kmike/scikit-learn'\n",
-       "'PATH: sklearn/utils/__init__.py'\n",
-       "'COPIES: 3'\n",
-       "'SIZE: 10094'\n",
-       "'''CONTENT: \"\"\"\n",
-       "The :mod:`sklearn.utils` module includes various utilites.\n",
-       "\"\"\"\n",
-       "\n",
-       "from collections import Sequence\n",
-       "\n",
-       "import numpy as np\n",
-       "from scipy.sparse import issparse\n",
-       "import warnings\n",
-       "\n",
-       "from .murmurhash import murm\n",
-       "LICENSE: bsd-3-clause'''"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for key in raw_datasets[\"train\"][0]:\n",
-    "    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs length: 34\n",
-       "Input chunk lengths: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 117, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 41]\n",
-       "Chunk mapping: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "context_length = 128\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n",
-    "\n",
-    "outputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][:2][\"content\"],\n",
-    "    truncation=True,\n",
-    "    max_length=context_length,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_length=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n",
-    "print(f\"Input chunk lengths: {(outputs['length'])}\")\n",
-    "print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 16702061\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 93164\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(element):\n",
-    "    outputs = tokenizer(\n",
-    "        element[\"content\"],\n",
-    "        truncation=True,\n",
-    "        max_length=context_length,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_length=True,\n",
-    "    )\n",
-    "    input_batch = []\n",
-    "    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
-    "        if length == context_length:\n",
-    "            input_batch.append(input_ids)\n",
-    "    return {\"input_ids\": input_batch}\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\n",
-    "    \"gpt2\",\n",
-    "    vocab_size=len(tokenizer),\n",
-    "    n_ctx=context_length,\n",
-    "    bos_token_id=tokenizer.bos_token_id,\n",
-    "    eos_token_id=tokenizer.eos_token_id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "GPT-2 size: 124.2M parameters"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = GPT2LMHeadModel(config)\n",
-    "model_size = sum(t.numel() for t in model.parameters())\n",
-    "print(f\"GPT-2 size: {model_size/1000**2:.1f}M parameters\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "input_ids shape: torch.Size([5, 128])\n",
-       "attention_mask shape: torch.Size([5, 128])\n",
-       "labels shape: torch.Size([5, 128])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out = data_collator([tokenized_dataset[\"train\"][i] for i in range(5)])\n",
-    "for key in out:\n",
-    "    print(f\"{key} shape: {out[key].shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer, TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    output_dir=\"codeparrot-ds\",\n",
-    "    per_device_train_batch_size=32,\n",
-    "    per_device_eval_batch_size=32,\n",
-    "    evaluation_strategy=\"steps\",\n",
-    "    eval_steps=5_000,\n",
-    "    logging_steps=5_000,\n",
-    "    gradient_accumulation_steps=8,\n",
-    "    num_train_epochs=1,\n",
-    "    weight_decay=0.1,\n",
-    "    warmup_steps=1_000,\n",
-    "    lr_scheduler_type=\"cosine\",\n",
-    "    learning_rate=5e-4,\n",
-    "    save_steps=5_000,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    tokenizer=tokenizer,\n",
-    "    args=args,\n",
-    "    data_collator=data_collator,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"valid\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import pipeline\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=\"huggingface-course/codeparrot-ds\", device=device\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create scatter plot with x, y\n",
-       "plt.scatter(x, y)\n",
-       "\n",
-       "# create scatter"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create scatter plot with x, y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create dataframe from x and y\n",
-       "df = pd.DataFrame({'x': x, 'y': y})\n",
-       "df.insert(0,'x', x)\n",
-       "for"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create dataframe from x and y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# dataframe with profession, income and name\n",
-       "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-       "\n",
-       "# calculate the mean income per profession\n",
-       "profession = df.groupby(['profession']).mean()\n",
-       "\n",
-       "# compute the"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# dataframe with profession, income and name\n",
-    "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-    "\n",
-    "# calculate the mean income per profession\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# import random forest regressor from scikit-learn\n",
-       "from sklearn.ensemble import RandomForestRegressor\n",
-       "\n",
-       "# fit random forest model with 300 estimators on X, y:\n",
-       "rf = RandomForestRegressor(n_estimators=300, random_state=random_state, max_depth=3)\n",
-       "rf.fit(X, y)\n",
-       "rf"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\n",
-    "# import random forest regressor from scikit-learn\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "# fit random forest model with 300 estimators on X, y:\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Keyword has not single token: testtest'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "keytoken_ids = []\n",
-    "for keyword in [\n",
-    "    \"plt\",\n",
-    "    \"pd\",\n",
-    "    \"sk\",\n",
-    "    \"fit\",\n",
-    "    \"predict\",\n",
-    "    \" plt\",\n",
-    "    \" pd\",\n",
-    "    \" sk\",\n",
-    "    \" fit\",\n",
-    "    \" predict\",\n",
-    "    \"testtest\",\n",
-    "]:\n",
-    "    ids = tokenizer([keyword]).input_ids[0]\n",
-    "    if len(ids) == 1:\n",
-    "        keytoken_ids.append(ids[0])\n",
-    "    else:\n",
-    "        print(f\"Keyword has not single token: {keyword}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.nn import CrossEntropyLoss\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "def keytoken_weighted_loss(inputs, logits, keytoken_ids, alpha=1.0):\n",
-    "    # Shift so that tokens < n predict n\n",
-    "    shift_labels = inputs[..., 1:].contiguous()\n",
-    "    shift_logits = logits[..., :-1, :].contiguous()\n",
-    "    # Calculate per-token loss\n",
-    "    loss_fct = CrossEntropyLoss(reduce=False)\n",
-    "    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))\n",
-    "    # Resize and average loss per sample\n",
-    "    loss_per_sample = loss.view(shift_logits.size(0), shift_logits.size(1)).mean(axis=1)\n",
-    "    # Calculate and scale weighting\n",
-    "    weights = torch.stack([(inputs == kt).float() for kt in keytoken_ids]).sum(\n",
-    "        axis=[0, 2]\n",
-    "    )\n",
-    "    weights = alpha * (1.0 + weights)\n",
-    "    # Calculate weighted average\n",
-    "    weighted_loss = (loss_per_sample * weights).mean()\n",
-    "    return weighted_loss"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data.dataloader import DataLoader\n",
-    "\n",
-    "tokenized_dataset.set_format(\"torch\")\n",
-    "train_dataloader = DataLoader(tokenized_dataset[\"train\"], batch_size=32, shuffle=True)\n",
-    "eval_dataloader = DataLoader(tokenized_dataset[\"valid\"], batch_size=32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weight_decay = 0.1\n",
-    "\n",
-    "\n",
-    "def get_grouped_params(model, no_decay=[\"bias\", \"LayerNorm.weight\"]):\n",
-    "    params_with_wd, params_without_wd = [], []\n",
-    "    for n, p in model.named_parameters():\n",
-    "        if any(nd in n for nd in no_decay):\n",
-    "            params_without_wd.append(p)\n",
-    "        else:\n",
-    "            params_with_wd.append(p)\n",
-    "    return [\n",
-    "        {\"params\": params_with_wd, \"weight_decay\": weight_decay},\n",
-    "        {\"params\": params_without_wd, \"weight_decay\": 0.0},\n",
-    "    ]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate():\n",
-    "    model.eval()\n",
-    "    losses = []\n",
-    "    for step, batch in enumerate(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(batch[\"input_ids\"], labels=batch[\"input_ids\"])\n",
-    "\n",
-    "        losses.append(accelerator.gather(outputs.loss))\n",
-    "    loss = torch.mean(torch.cat(losses))\n",
-    "    try:\n",
-    "        perplexity = torch.exp(loss)\n",
-    "    except OverflowError:\n",
-    "        perplexity = float(\"inf\")\n",
-    "    return loss.item(), perplexity.item()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = GPT2LMHeadModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(get_grouped_params(model), lr=5e-4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator(fp16=True)\n",
-    "\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_train_epochs = 1\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    name=\"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/codeparrot-ds-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"codeparrot-ds-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"codeparrot-ds-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.934126853942871, 56057.14453125)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.notebook import tqdm\n",
-    "\n",
-    "gradient_accumulation_steps = 8\n",
-    "eval_steps = 5_000\n",
-    "\n",
-    "model.train()\n",
-    "completed_steps = 0\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    for step, batch in tqdm(\n",
-    "        enumerate(train_dataloader, start=1), total=len(train_dataloader)\n",
-    "    ):\n",
-    "        logits = model(batch[\"input_ids\"]).logits\n",
-    "        loss = keytoken_weighted_loss(batch[\"input_ids\"], logits, keytoken_ids)\n",
-    "        if step % 100 == 0:\n",
-    "            accelerator.print(\n",
-    "                {\n",
-    "                    \"lr\": get_lr(),\n",
-    "                    \"samples\": step * samples_per_step,\n",
-    "                    \"steps\": completed_steps,\n",
-    "                    \"loss/train\": loss.item() * gradient_accumulation_steps,\n",
-    "                }\n",
-    "            )\n",
-    "        loss = loss / gradient_accumulation_steps\n",
-    "        accelerator.backward(loss)\n",
-    "        if step % gradient_accumulation_steps == 0:\n",
-    "            accelerator.clip_grad_norm_(model.parameters(), 1.0)\n",
-    "            optimizer.step()\n",
-    "            lr_scheduler.step()\n",
-    "            optimizer.zero_grad()\n",
-    "            completed_steps += 1\n",
-    "        if (step % (eval_steps * gradient_accumulation_steps)) == 0:\n",
-    "            eval_loss, perplexity = evaluate()\n",
-    "            accelerator.print({\"loss/eval\": eval_loss, \"perplexity\": perplexity})\n",
-    "            model.train()\n",
-    "            accelerator.wait_for_everyone()\n",
-    "            unwrapped_model = accelerator.unwrap_model(model)\n",
-    "            unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "            if accelerator.is_main_process:\n",
-    "                tokenizer.save_pretrained(output_dir)\n",
-    "                repo.push_to_hub(\n",
-    "                    commit_message=f\"Training in progress step {step}\", blocking=False\n",
-    "                )"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "从头开始训练因果语言模型 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section6_tf.ipynb b/course/zh-CN/chapter7/section6_tf.ipynb
deleted file mode 100644
index 61cfa34a..00000000
--- a/course/zh-CN/chapter7/section6_tf.ipynb
+++ /dev/null
@@ -1,613 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 从头开始训练因果语言模型 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def any_keyword_in_string(string, keywords):\n",
-    "    for keyword in keywords:\n",
-    "        if keyword in string:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "example_1 = \"import numpy as np\"\n",
-    "example_2 = \"import pandas as pd\"\n",
-    "\n",
-    "print(\n",
-    "    any_keyword_in_string(example_1, filters), any_keyword_in_string(example_2, filters)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def filter_streaming_dataset(dataset, filters):\n",
-    "    filtered_dict = defaultdict(list)\n",
-    "    total = 0\n",
-    "    for sample in tqdm(iter(dataset)):\n",
-    "        total += 1\n",
-    "        if any_keyword_in_string(sample[\"content\"], filters):\n",
-    "            for k, v in sample.items():\n",
-    "                filtered_dict[k].append(v)\n",
-    "    print(f\"{len(filtered_dict['content'])/total:.2%} of data after filtering.\")\n",
-    "    return Dataset.from_dict(filtered_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.26% of data after filtering."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This cell will take a very long time to execute, so you should skip it and go to\n",
-    "# the next one!\n",
-    "from datasets import load_dataset\n",
-    "\n",
-    "split = \"train\"  # \"valid\"\n",
-    "filters = [\"pandas\", \"sklearn\", \"matplotlib\", \"seaborn\"]\n",
-    "\n",
-    "data = load_dataset(f\"transformersbook/codeparrot-{split}\", split=split, streaming=True)\n",
-    "filtered_data = filter_streaming_dataset(data, filters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 606720\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['repo_name', 'path', 'copies', 'size', 'content', 'license'],\n",
-       "        num_rows: 3322\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, DatasetDict\n",
-    "\n",
-    "ds_train = load_dataset(\"huggingface-course/codeparrot-ds-train\", split=\"train\")\n",
-    "ds_valid = load_dataset(\"huggingface-course/codeparrot-ds-valid\", split=\"train\")\n",
-    "\n",
-    "raw_datasets = DatasetDict(\n",
-    "    {\n",
-    "        \"train\": ds_train,  # .shuffle().select(range(50000)),\n",
-    "        \"valid\": ds_valid,  # .shuffle().select(range(500))\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'REPO_NAME: kmike/scikit-learn'\n",
-       "'PATH: sklearn/utils/__init__.py'\n",
-       "'COPIES: 3'\n",
-       "'SIZE: 10094'\n",
-       "'''CONTENT: \"\"\"\n",
-       "The :mod:`sklearn.utils` module includes various utilites.\n",
-       "\"\"\"\n",
-       "\n",
-       "from collections import Sequence\n",
-       "\n",
-       "import numpy as np\n",
-       "from scipy.sparse import issparse\n",
-       "import warnings\n",
-       "\n",
-       "from .murmurhash import murm\n",
-       "LICENSE: bsd-3-clause'''"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for key in raw_datasets[\"train\"][0]:\n",
-    "    print(f\"{key.upper()}: {raw_datasets['train'][0][key][:200]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs length: 34\n",
-       "Input chunk lengths: [128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 117, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 41]\n",
-       "Chunk mapping: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "context_length = 128\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/code-search-net-tokenizer\")\n",
-    "\n",
-    "outputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][:2][\"content\"],\n",
-    "    truncation=True,\n",
-    "    max_length=context_length,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_length=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"Input IDs length: {len(outputs['input_ids'])}\")\n",
-    "print(f\"Input chunk lengths: {(outputs['length'])}\")\n",
-    "print(f\"Chunk mapping: {outputs['overflow_to_sample_mapping']}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 16702061\n",
-       "    })\n",
-       "    valid: Dataset({\n",
-       "        features: ['input_ids'],\n",
-       "        num_rows: 93164\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def tokenize(element):\n",
-    "    outputs = tokenizer(\n",
-    "        element[\"content\"],\n",
-    "        truncation=True,\n",
-    "        max_length=context_length,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_length=True,\n",
-    "    )\n",
-    "    input_batch = []\n",
-    "    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n",
-    "        if length == context_length:\n",
-    "            input_batch.append(input_ids)\n",
-    "    return {\"input_ids\": input_batch}\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(\n",
-    "    tokenize, batched=True, remove_columns=raw_datasets[\"train\"].column_names\n",
-    ")\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFGPT2LMHeadModel, AutoConfig\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\n",
-    "    \"gpt2\",\n",
-    "    vocab_size=len(tokenizer),\n",
-    "    n_ctx=context_length,\n",
-    "    bos_token_id=tokenizer.bos_token_id,\n",
-    "    eos_token_id=tokenizer.eos_token_id,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "_________________________________________________________________\n",
-       "Layer (type)                 Output Shape              Param #   \n",
-       "=================================================================\n",
-       "transformer (TFGPT2MainLayer multiple                  124242432 \n",
-       "=================================================================\n",
-       "Total params: 124,242,432\n",
-       "Trainable params: 124,242,432\n",
-       "Non-trainable params: 0\n",
-       "_________________________________________________________________"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFGPT2LMHeadModel(config)\n",
-    "model(model.dummy_inputs)  # Builds the model\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorForLanguageModeling\n",
-    "\n",
-    "tokenizer.pad_token = tokenizer.eos_token\n",
-    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "input_ids shape: (5, 128)\n",
-       "attention_mask shape: (5, 128)\n",
-       "labels shape: (5, 128)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out = data_collator([tokenized_dataset[\"train\"][i] for i in range(5)])\n",
-    "for key in out:\n",
-    "    print(f\"{key} shape: {out[key].shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=32,\n",
-    ")\n",
-    "tf_eval_dataset = tokenized_dataset[\"valid\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"labels\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=32,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "num_train_steps = len(tf_train_dataset)\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=5e-5,\n",
-    "    num_warmup_steps=1_000,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"codeparrot-ds\", tokenizer=tokenizer)\n",
-    "\n",
-    "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, callbacks=[callback])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "course_model = TFGPT2LMHeadModel.from_pretrained(\"huggingface-course/codeparrot-ds\")\n",
-    "course_tokenizer = AutoTokenizer.from_pretrained(\"huggingface-course/codeparrot-ds\")\n",
-    "pipe = pipeline(\n",
-    "    \"text-generation\", model=course_model, tokenizer=course_tokenizer, device=0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create scatter plot with x, y\n",
-       "plt.scatter(x, y)\n",
-       "\n",
-       "# create scatter"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create scatter plot with x, y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# create some data\n",
-       "x = np.random.randn(100)\n",
-       "y = np.random.randn(100)\n",
-       "\n",
-       "# create dataframe from x and y\n",
-       "df = pd.DataFrame({'x': x, 'y': y})\n",
-       "df.insert(0,'x', x)\n",
-       "for"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# create some data\n",
-    "x = np.random.randn(100)\n",
-    "y = np.random.randn(100)\n",
-    "\n",
-    "# create dataframe from x and y\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# dataframe with profession, income and name\n",
-       "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-       "\n",
-       "# calculate the mean income per profession\n",
-       "profession = df.groupby(['profession']).mean()\n",
-       "\n",
-       "# compute the"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\\\n",
-    "# dataframe with profession, income and name\n",
-    "df = pd.DataFrame({'profession': x, 'income':y, 'name': z})\n",
-    "\n",
-    "# calculate the mean income per profession\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "# import random forest regressor from scikit-learn\n",
-       "from sklearn.ensemble import RandomForestRegressor\n",
-       "\n",
-       "# fit random forest model with 300 estimators on X, y:\n",
-       "rf = RandomForestRegressor(n_estimators=300, random_state=random_state, max_depth=3)\n",
-       "rf.fit(X, y)\n",
-       "rf"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "txt = \"\"\"\n",
-    "# import random forest regressor from scikit-learn\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "# fit random forest model with 300 estimators on X, y:\n",
-    "\"\"\"\n",
-    "print(pipe(txt, num_return_sequences=1)[0][\"generated_text\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "从头开始训练因果语言模型 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section7_pt.ipynb b/course/zh-CN/chapter7/section7_pt.ipynb
deleted file mode 100644
index 29dcc662..00000000
--- a/course/zh-CN/chapter7/section7_pt.ipynb
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 问答系统 (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
-    "# To run the training on TPU, you will need to uncomment the following line:\n",
-    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 87599\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 10570\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Context: 'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'\n",
-       "Question: 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'\n",
-       "Answer: {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n",
-    "print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n",
-    "print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "    num_rows: 0\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}\n",
-       "{'text': ['Santa Clara, California', \"Levi's Stadium\", \"Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.\"], 'answer_start': [403, 355, 355]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][0][\"answers\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \"Super Bowl L\"), so that the logo could prominently feature the Arabic numerals 50.'\n",
-       "'Where did Super Bowl 50 take place?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][2][\"context\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, '\n",
-       "'the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin '\n",
-       "'Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms '\n",
-       "'upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred '\n",
-       "'Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a '\n",
-       "'replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette '\n",
-       "'Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues '\n",
-       "'and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context = raw_datasets[\"train\"][0][\"context\"]\n",
-    "question = raw_datasets[\"train\"][0][\"question\"]\n",
-    "\n",
-    "inputs = tokenizer(question, context)\n",
-    "tokenizer.decode(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basi [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP]. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "inputs.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"overflow_to_sample_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The 4 examples gave 19 features.'\n",
-       "'Here is where each comes from: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3].'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][2:6][\"question\"],\n",
-    "    raw_datasets[\"train\"][2:6][\"context\"],\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n",
-    "print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([83, 51, 19, 0, 0, 64, 27, 0, 34, 0, 0, 0, 67, 34, 0, 0, 0, 0, 0],\n",
-       " [85, 53, 21, 0, 0, 70, 33, 0, 40, 0, 0, 0, 68, 35, 0, 0, 0, 0, 0])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "answers = raw_datasets[\"train\"][2:6][\"answers\"]\n",
-    "start_positions = []\n",
-    "end_positions = []\n",
-    "\n",
-    "for i, offset in enumerate(inputs[\"offset_mapping\"]):\n",
-    "    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n",
-    "    answer = answers[sample_idx]\n",
-    "    start_char = answer[\"answer_start\"][0]\n",
-    "    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "    sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "    # Find the start and end of the context\n",
-    "    idx = 0\n",
-    "    while sequence_ids[idx] != 1:\n",
-    "        idx += 1\n",
-    "    context_start = idx\n",
-    "    while sequence_ids[idx] == 1:\n",
-    "        idx += 1\n",
-    "    context_end = idx - 1\n",
-    "\n",
-    "    # If the answer is not fully inside the context, label is (0, 0)\n",
-    "    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "        start_positions.append(0)\n",
-    "        end_positions.append(0)\n",
-    "    else:\n",
-    "        # Otherwise it's the start and end token positions\n",
-    "        idx = context_start\n",
-    "        while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "            idx += 1\n",
-    "        start_positions.append(idx - 1)\n",
-    "\n",
-    "        idx = context_end\n",
-    "        while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "            idx -= 1\n",
-    "        end_positions.append(idx + 1)\n",
-    "\n",
-    "start_positions, end_positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: the Main Building, labels give: the Main Building'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "start = start_positions[idx]\n",
-    "end = end_positions[idx]\n",
-    "labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n",
-    "\n",
-    "print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: a Marian place of prayer and reflection, decoded example: [CLS] What is the Grotto at Notre Dame? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grot [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 4\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n",
-    "print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_length = 384\n",
-    "stride = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_training_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    answers = examples[\"answers\"]\n",
-    "    start_positions = []\n",
-    "    end_positions = []\n",
-    "\n",
-    "    for i, offset in enumerate(offset_mapping):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        answer = answers[sample_idx]\n",
-    "        start_char = answer[\"answer_start\"][0]\n",
-    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "        # Find the start and end of the context\n",
-    "        idx = 0\n",
-    "        while sequence_ids[idx] != 1:\n",
-    "            idx += 1\n",
-    "        context_start = idx\n",
-    "        while sequence_ids[idx] == 1:\n",
-    "            idx += 1\n",
-    "        context_end = idx - 1\n",
-    "\n",
-    "        # If the answer is not fully inside the context, label is (0, 0)\n",
-    "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "            start_positions.append(0)\n",
-    "            end_positions.append(0)\n",
-    "        else:\n",
-    "            # Otherwise it's the start and end token positions\n",
-    "            idx = context_start\n",
-    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "                idx += 1\n",
-    "            start_positions.append(idx - 1)\n",
-    "\n",
-    "            idx = context_end\n",
-    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "                idx -= 1\n",
-    "            end_positions.append(idx + 1)\n",
-    "\n",
-    "    inputs[\"start_positions\"] = start_positions\n",
-    "    inputs[\"end_positions\"] = end_positions\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(87599, 88729)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_dataset = raw_datasets[\"train\"].map(\n",
-    "    preprocess_training_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"train\"]), len(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess_validation_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    example_ids = []\n",
-    "\n",
-    "    for i in range(len(inputs[\"input_ids\"])):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        example_ids.append(examples[\"id\"][sample_idx])\n",
-    "\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "        offset = inputs[\"offset_mapping\"][i]\n",
-    "        inputs[\"offset_mapping\"][i] = [\n",
-    "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
-    "        ]\n",
-    "\n",
-    "    inputs[\"example_id\"] = example_ids\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10570, 10822)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation_dataset = raw_datasets[\"validation\"].map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"validation\"]), len(validation_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "small_eval_set = raw_datasets[\"validation\"].select(range(100))\n",
-    "trained_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n",
-    "eval_set = small_eval_set.map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoModelForQuestionAnswering\n",
-    "\n",
-    "eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "eval_set_for_model.set_format(\"torch\")\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: eval_set_for_model[k].to(device) for k in eval_set_for_model.column_names}\n",
-    "trained_model = AutoModelForQuestionAnswering.from_pretrained(trained_checkpoint).to(\n",
-    "    device\n",
-    ")\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trained_model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_logits = outputs.start_logits.cpu().numpy()\n",
-    "end_logits = outputs.end_logits.cpu().numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "\n",
-    "example_to_features = collections.defaultdict(list)\n",
-    "for idx, feature in enumerate(eval_set):\n",
-    "    example_to_features[feature[\"example_id\"]].append(idx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "n_best = 20\n",
-    "max_answer_length = 30\n",
-    "predicted_answers = []\n",
-    "\n",
-    "for example in small_eval_set:\n",
-    "    example_id = example[\"id\"]\n",
-    "    context = example[\"context\"]\n",
-    "    answers = []\n",
-    "\n",
-    "    for feature_index in example_to_features[example_id]:\n",
-    "        start_logit = start_logits[feature_index]\n",
-    "        end_logit = end_logits[feature_index]\n",
-    "        offsets = eval_set[\"offset_mapping\"][feature_index]\n",
-    "\n",
-    "        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        for start_index in start_indexes:\n",
-    "            for end_index in end_indexes:\n",
-    "                # Skip answers that are not fully in the context\n",
-    "                if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                    continue\n",
-    "                # Skip answers with a length that is either < 0 or > max_answer_length.\n",
-    "                if (\n",
-    "                    end_index < start_index\n",
-    "                    or end_index - start_index + 1 > max_answer_length\n",
-    "                ):\n",
-    "                    continue\n",
-    "\n",
-    "                answers.append(\n",
-    "                    {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                )\n",
-    "\n",
-    "    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theoretical_answers = [\n",
-    "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\n",
-       "{'id': '56be4db0acb8001400a502ec', 'answers': {'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(predicted_answers[0])\n",
-    "print(theoretical_answers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "\n",
-    "def compute_metrics(start_logits, end_logits, features, examples):\n",
-    "    example_to_features = collections.defaultdict(list)\n",
-    "    for idx, feature in enumerate(features):\n",
-    "        example_to_features[feature[\"example_id\"]].append(idx)\n",
-    "\n",
-    "    predicted_answers = []\n",
-    "    for example in tqdm(examples):\n",
-    "        example_id = example[\"id\"]\n",
-    "        context = example[\"context\"]\n",
-    "        answers = []\n",
-    "\n",
-    "        # Loop through all features associated with that example\n",
-    "        for feature_index in example_to_features[example_id]:\n",
-    "            start_logit = start_logits[feature_index]\n",
-    "            end_logit = end_logits[feature_index]\n",
-    "            offsets = features[feature_index][\"offset_mapping\"]\n",
-    "\n",
-    "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            for start_index in start_indexes:\n",
-    "                for end_index in end_indexes:\n",
-    "                    # Skip answers that are not fully in the context\n",
-    "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                        continue\n",
-    "                    # Skip answers with a length that is either < 0 or > max_answer_length\n",
-    "                    if (\n",
-    "                        end_index < start_index\n",
-    "                        or end_index - start_index + 1 > max_answer_length\n",
-    "                    ):\n",
-    "                        continue\n",
-    "\n",
-    "                    answer = {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                    answers.append(answer)\n",
-    "\n",
-    "        # Select the answer with the best score\n",
-    "        if len(answers) > 0:\n",
-    "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "            predicted_answers.append(\n",
-    "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
-    "            )\n",
-    "        else:\n",
-    "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
-    "\n",
-    "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
-    "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    \"bert-finetuned-squad\",\n",
-    "    evaluation_strategy=\"no\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    "    fp16=True,\n",
-    "    push_to_hub=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    args=args,\n",
-    "    train_dataset=train_dataset,\n",
-    "    eval_dataset=validation_dataset,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 81.18259224219489, 'f1': 88.67381321905516}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions, _ = trainer.predict(validation_dataset)\n",
-    "start_logits, end_logits = predictions\n",
-    "compute_metrics(start_logits, end_logits, validation_dataset, raw_datasets[\"validation\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://huggingface.co/sgugger/bert-finetuned-squad/commit/9dcee1fbc25946a6ed4bb32efb1bd71d5fa90b68'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.push_to_hub(commit_message=\"Training complete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import default_data_collator\n",
-    "\n",
-    "train_dataset.set_format(\"torch\")\n",
-    "validation_set = validation_dataset.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "validation_set.set_format(\"torch\")\n",
-    "\n",
-    "train_dataloader = DataLoader(\n",
-    "    train_dataset,\n",
-    "    shuffle=True,\n",
-    "    collate_fn=default_data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "eval_dataloader = DataLoader(\n",
-    "    validation_set, collate_fn=default_data_collator, batch_size=8\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizer = AdamW(model.parameters(), lr=2e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from accelerate import Accelerator\n",
-    "\n",
-    "accelerator = Accelerator(fp16=True)\n",
-    "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n",
-    "    model, optimizer, train_dataloader, eval_dataloader\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import get_scheduler\n",
-    "\n",
-    "num_train_epochs = 3\n",
-    "num_update_steps_per_epoch = len(train_dataloader)\n",
-    "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
-    "\n",
-    "lr_scheduler = get_scheduler(\n",
-    "    \"linear\",\n",
-    "    optimizer=optimizer,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_training_steps=num_training_steps,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'sgugger/bert-finetuned-squad-accelerate'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import Repository, get_full_repo_name\n",
-    "\n",
-    "model_name = \"bert-finetuned-squad-accelerate\"\n",
-    "repo_name = get_full_repo_name(model_name)\n",
-    "repo_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_dir = \"bert-finetuned-squad-accelerate\"\n",
-    "repo = Repository(output_dir, clone_from=repo_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import torch\n",
-    "\n",
-    "progress_bar = tqdm(range(num_training_steps))\n",
-    "\n",
-    "for epoch in range(num_train_epochs):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    for step, batch in enumerate(train_dataloader):\n",
-    "        outputs = model(**batch)\n",
-    "        loss = outputs.loss\n",
-    "        accelerator.backward(loss)\n",
-    "\n",
-    "        optimizer.step()\n",
-    "        lr_scheduler.step()\n",
-    "        optimizer.zero_grad()\n",
-    "        progress_bar.update(1)\n",
-    "\n",
-    "    # Evaluation\n",
-    "    model.eval()\n",
-    "    start_logits = []\n",
-    "    end_logits = []\n",
-    "    accelerator.print(\"Evaluation!\")\n",
-    "    for batch in tqdm(eval_dataloader):\n",
-    "        with torch.no_grad():\n",
-    "            outputs = model(**batch)\n",
-    "\n",
-    "        start_logits.append(accelerator.gather(outputs.start_logits).cpu().numpy())\n",
-    "        end_logits.append(accelerator.gather(outputs.end_logits).cpu().numpy())\n",
-    "\n",
-    "    start_logits = np.concatenate(start_logits)\n",
-    "    end_logits = np.concatenate(end_logits)\n",
-    "    start_logits = start_logits[: len(validation_dataset)]\n",
-    "    end_logits = end_logits[: len(validation_dataset)]\n",
-    "\n",
-    "    metrics = compute_metrics(\n",
-    "        start_logits, end_logits, validation_dataset, raw_datasets[\"validation\"]\n",
-    "    )\n",
-    "    print(f\"epoch {epoch}:\", metrics)\n",
-    "\n",
-    "    # Save and upload\n",
-    "    accelerator.wait_for_everyone()\n",
-    "    unwrapped_model = accelerator.unwrap_model(model)\n",
-    "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
-    "    if accelerator.is_main_process:\n",
-    "        tokenizer.save_pretrained(output_dir)\n",
-    "        repo.push_to_hub(\n",
-    "            commit_message=f\"Training in progress epoch {epoch}\", blocking=False\n",
-    "        )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accelerator.wait_for_everyone()\n",
-    "unwrapped_model = accelerator.unwrap_model(model)\n",
-    "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.9979003071784973,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-squad\"\n",
-    "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
-    "\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "问答系统 (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter7/section7_tf.ipynb b/course/zh-CN/chapter7/section7_tf.ipynb
deleted file mode 100644
index 5df36653..00000000
--- a/course/zh-CN/chapter7/section7_tf.ipynb
+++ /dev/null
@@ -1,1056 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 问答系统 (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 87599\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "        num_rows: 10570\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Context: 'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'\n",
-       "Question: 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'\n",
-       "Answer: {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(\"Context: \", raw_datasets[\"train\"][0][\"context\"])\n",
-    "print(\"Question: \", raw_datasets[\"train\"][0][\"question\"])\n",
-    "print(\"Answer: \", raw_datasets[\"train\"][0][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
-       "    num_rows: 0\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_datasets[\"train\"].filter(lambda x: len(x[\"answers\"][\"text\"]) != 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}\n",
-       "{'text': ['Santa Clara, California', \"Levi's Stadium\", \"Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.\"], 'answer_start': [403, 355, 355]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][0][\"answers\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"answers\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \"Super Bowl L\"), so that the logo could prominently feature the Arabic numerals 50.'\n",
-       "'Where did Super Bowl 50 take place?'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(raw_datasets[\"validation\"][2][\"context\"])\n",
-    "print(raw_datasets[\"validation\"][2][\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "model_checkpoint = \"bert-base-cased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.is_fast"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, '\n",
-       "'the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin '\n",
-       "'Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms '\n",
-       "'upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred '\n",
-       "'Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a '\n",
-       "'replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette '\n",
-       "'Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues '\n",
-       "'and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context = raw_datasets[\"train\"][0][\"context\"]\n",
-    "question = raw_datasets[\"train\"][0][\"question\"]\n",
-    "\n",
-    "inputs = tokenizer(question, context)\n",
-    "tokenizer.decode(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basi [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP] Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 [SEP]'\n",
-       "'[CLS] To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France? [SEP]. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ), is a simple, modern stone statue of Mary. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    ")\n",
-    "\n",
-    "for ids in inputs[\"input_ids\"]:\n",
-    "    print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'offset_mapping', 'overflow_to_sample_mapping'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    question,\n",
-    "    context,\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "inputs.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 0, 0, 0]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"overflow_to_sample_mapping\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The 4 examples gave 19 features.'\n",
-       "'Here is where each comes from: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3].'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\n",
-    "    raw_datasets[\"train\"][2:6][\"question\"],\n",
-    "    raw_datasets[\"train\"][2:6][\"context\"],\n",
-    "    max_length=100,\n",
-    "    truncation=\"only_second\",\n",
-    "    stride=50,\n",
-    "    return_overflowing_tokens=True,\n",
-    "    return_offsets_mapping=True,\n",
-    ")\n",
-    "\n",
-    "print(f\"The 4 examples gave {len(inputs['input_ids'])} features.\")\n",
-    "print(f\"Here is where each comes from: {inputs['overflow_to_sample_mapping']}.\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "([83, 51, 19, 0, 0, 64, 27, 0, 34, 0, 0, 0, 67, 34, 0, 0, 0, 0, 0],\n",
-       " [85, 53, 21, 0, 0, 70, 33, 0, 40, 0, 0, 0, 68, 35, 0, 0, 0, 0, 0])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "answers = raw_datasets[\"train\"][2:6][\"answers\"]\n",
-    "start_positions = []\n",
-    "end_positions = []\n",
-    "\n",
-    "for i, offset in enumerate(inputs[\"offset_mapping\"]):\n",
-    "    sample_idx = inputs[\"overflow_to_sample_mapping\"][i]\n",
-    "    answer = answers[sample_idx]\n",
-    "    start_char = answer[\"answer_start\"][0]\n",
-    "    end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "    sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "    # Find the start and end of the context\n",
-    "    idx = 0\n",
-    "    while sequence_ids[idx] != 1:\n",
-    "        idx += 1\n",
-    "    context_start = idx\n",
-    "    while sequence_ids[idx] == 1:\n",
-    "        idx += 1\n",
-    "    context_end = idx - 1\n",
-    "\n",
-    "    # If the answer is not fully inside the context, label is (0, 0)\n",
-    "    if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "        start_positions.append(0)\n",
-    "        end_positions.append(0)\n",
-    "    else:\n",
-    "        # Otherwise it's the start and end token positions\n",
-    "        idx = context_start\n",
-    "        while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "            idx += 1\n",
-    "        start_positions.append(idx - 1)\n",
-    "\n",
-    "        idx = context_end\n",
-    "        while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "            idx -= 1\n",
-    "        end_positions.append(idx + 1)\n",
-    "\n",
-    "start_positions, end_positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: the Main Building, labels give: the Main Building'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "start = start_positions[idx]\n",
-    "end = end_positions[idx]\n",
-    "labeled_answer = tokenizer.decode(inputs[\"input_ids\"][idx][start : end + 1])\n",
-    "\n",
-    "print(f\"Theoretical answer: {answer}, labels give: {labeled_answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Theoretical answer: a Marian place of prayer and reflection, decoded example: [CLS] What is the Grotto at Notre Dame? [SEP] Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \" Venite Ad Me Omnes \". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grot [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 4\n",
-    "sample_idx = inputs[\"overflow_to_sample_mapping\"][idx]\n",
-    "answer = answers[sample_idx][\"text\"][0]\n",
-    "\n",
-    "decoded_example = tokenizer.decode(inputs[\"input_ids\"][idx])\n",
-    "print(f\"Theoretical answer: {answer}, decoded example: {decoded_example}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_length = 384\n",
-    "stride = 128\n",
-    "\n",
-    "\n",
-    "def preprocess_training_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    answers = examples[\"answers\"]\n",
-    "    start_positions = []\n",
-    "    end_positions = []\n",
-    "\n",
-    "    for i, offset in enumerate(offset_mapping):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        answer = answers[sample_idx]\n",
-    "        start_char = answer[\"answer_start\"][0]\n",
-    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "\n",
-    "        # Find the start and end of the context\n",
-    "        idx = 0\n",
-    "        while sequence_ids[idx] != 1:\n",
-    "            idx += 1\n",
-    "        context_start = idx\n",
-    "        while sequence_ids[idx] == 1:\n",
-    "            idx += 1\n",
-    "        context_end = idx - 1\n",
-    "\n",
-    "        # If the answer is not fully inside the context, label is (0, 0)\n",
-    "        if offset[context_start][0] > start_char or offset[context_end][1] < end_char:\n",
-    "            start_positions.append(0)\n",
-    "            end_positions.append(0)\n",
-    "        else:\n",
-    "            # Otherwise it's the start and end token positions\n",
-    "            idx = context_start\n",
-    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
-    "                idx += 1\n",
-    "            start_positions.append(idx - 1)\n",
-    "\n",
-    "            idx = context_end\n",
-    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
-    "                idx -= 1\n",
-    "            end_positions.append(idx + 1)\n",
-    "\n",
-    "    inputs[\"start_positions\"] = start_positions\n",
-    "    inputs[\"end_positions\"] = end_positions\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(87599, 88729)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_dataset = raw_datasets[\"train\"].map(\n",
-    "    preprocess_training_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"train\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"train\"]), len(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess_validation_examples(examples):\n",
-    "    questions = [q.strip() for q in examples[\"question\"]]\n",
-    "    inputs = tokenizer(\n",
-    "        questions,\n",
-    "        examples[\"context\"],\n",
-    "        max_length=max_length,\n",
-    "        truncation=\"only_second\",\n",
-    "        stride=stride,\n",
-    "        return_overflowing_tokens=True,\n",
-    "        return_offsets_mapping=True,\n",
-    "        padding=\"max_length\",\n",
-    "    )\n",
-    "\n",
-    "    sample_map = inputs.pop(\"overflow_to_sample_mapping\")\n",
-    "    example_ids = []\n",
-    "\n",
-    "    for i in range(len(inputs[\"input_ids\"])):\n",
-    "        sample_idx = sample_map[i]\n",
-    "        example_ids.append(examples[\"id\"][sample_idx])\n",
-    "\n",
-    "        sequence_ids = inputs.sequence_ids(i)\n",
-    "        offset = inputs[\"offset_mapping\"][i]\n",
-    "        inputs[\"offset_mapping\"][i] = [\n",
-    "            o if sequence_ids[k] == 1 else None for k, o in enumerate(offset)\n",
-    "        ]\n",
-    "\n",
-    "    inputs[\"example_id\"] = example_ids\n",
-    "    return inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10570, 10822)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation_dataset = raw_datasets[\"validation\"].map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")\n",
-    "len(raw_datasets[\"validation\"]), len(validation_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "small_eval_set = raw_datasets[\"validation\"].select(range(100))\n",
-    "trained_checkpoint = \"distilbert-base-cased-distilled-squad\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(trained_checkpoint)\n",
-    "eval_set = small_eval_set.map(\n",
-    "    preprocess_validation_examples,\n",
-    "    batched=True,\n",
-    "    remove_columns=raw_datasets[\"validation\"].column_names,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import TFAutoModelForQuestionAnswering\n",
-    "\n",
-    "eval_set_for_model = eval_set.remove_columns([\"example_id\", \"offset_mapping\"])\n",
-    "eval_set_for_model.set_format(\"numpy\")\n",
-    "\n",
-    "batch = {k: eval_set_for_model[k] for k in eval_set_for_model.column_names}\n",
-    "trained_model = TFAutoModelForQuestionAnswering.from_pretrained(trained_checkpoint)\n",
-    "\n",
-    "outputs = trained_model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "start_logits = outputs.start_logits.numpy()\n",
-    "end_logits = outputs.end_logits.numpy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections\n",
-    "\n",
-    "example_to_features = collections.defaultdict(list)\n",
-    "for idx, feature in enumerate(eval_set):\n",
-    "    example_to_features[feature[\"example_id\"]].append(idx)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "n_best = 20\n",
-    "max_answer_length = 30\n",
-    "predicted_answers = []\n",
-    "\n",
-    "for example in small_eval_set:\n",
-    "    example_id = example[\"id\"]\n",
-    "    context = example[\"context\"]\n",
-    "    answers = []\n",
-    "\n",
-    "    for feature_index in example_to_features[example_id]:\n",
-    "        start_logit = start_logits[feature_index]\n",
-    "        end_logit = end_logits[feature_index]\n",
-    "        offsets = eval_set[\"offset_mapping\"][feature_index]\n",
-    "\n",
-    "        start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "        for start_index in start_indexes:\n",
-    "            for end_index in end_indexes:\n",
-    "                # Skip answers that are not fully in the context\n",
-    "                if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                    continue\n",
-    "                # Skip answers with a length that is either < 0 or > max_answer_length.\n",
-    "                if (\n",
-    "                    end_index < start_index\n",
-    "                    or end_index - start_index + 1 > max_answer_length\n",
-    "                ):\n",
-    "                    continue\n",
-    "\n",
-    "                answers.append(\n",
-    "                    {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                )\n",
-    "\n",
-    "    best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "    predicted_answers.append({\"id\": example_id, \"prediction_text\": best_answer[\"text\"]})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_metric\n",
-    "\n",
-    "metric = load_metric(\"squad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theoretical_answers = [\n",
-    "    {\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in small_eval_set\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'}\n",
-       "{'id': '56be4db0acb8001400a502ec', 'answers': {'text': ['Denver Broncos', 'Denver Broncos', 'Denver Broncos'], 'answer_start': [177, 177, 177]}}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(predicted_answers[0])\n",
-    "print(theoretical_answers[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "\n",
-    "def compute_metrics(start_logits, end_logits, features, examples):\n",
-    "    example_to_features = collections.defaultdict(list)\n",
-    "    for idx, feature in enumerate(features):\n",
-    "        example_to_features[feature[\"example_id\"]].append(idx)\n",
-    "\n",
-    "    predicted_answers = []\n",
-    "    for example in tqdm(examples):\n",
-    "        example_id = example[\"id\"]\n",
-    "        context = example[\"context\"]\n",
-    "        answers = []\n",
-    "\n",
-    "        # Loop through all features associated with that example\n",
-    "        for feature_index in example_to_features[example_id]:\n",
-    "            start_logit = start_logits[feature_index]\n",
-    "            end_logit = end_logits[feature_index]\n",
-    "            offsets = features[feature_index][\"offset_mapping\"]\n",
-    "\n",
-    "            start_indexes = np.argsort(start_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            end_indexes = np.argsort(end_logit)[-1 : -n_best - 1 : -1].tolist()\n",
-    "            for start_index in start_indexes:\n",
-    "                for end_index in end_indexes:\n",
-    "                    # Skip answers that are not fully in the context\n",
-    "                    if offsets[start_index] is None or offsets[end_index] is None:\n",
-    "                        continue\n",
-    "                    # Skip answers with a length that is either < 0 or > max_answer_length\n",
-    "                    if (\n",
-    "                        end_index < start_index\n",
-    "                        or end_index - start_index + 1 > max_answer_length\n",
-    "                    ):\n",
-    "                        continue\n",
-    "\n",
-    "                    answer = {\n",
-    "                        \"text\": context[offsets[start_index][0] : offsets[end_index][1]],\n",
-    "                        \"logit_score\": start_logit[start_index] + end_logit[end_index],\n",
-    "                    }\n",
-    "                    answers.append(answer)\n",
-    "\n",
-    "        # Select the answer with the best score\n",
-    "        if len(answers) > 0:\n",
-    "            best_answer = max(answers, key=lambda x: x[\"logit_score\"])\n",
-    "            predicted_answers.append(\n",
-    "                {\"id\": example_id, \"prediction_text\": best_answer[\"text\"]}\n",
-    "            )\n",
-    "        else:\n",
-    "            predicted_answers.append({\"id\": example_id, \"prediction_text\": \"\"})\n",
-    "\n",
-    "    theoretical_answers = [{\"id\": ex[\"id\"], \"answers\": ex[\"answers\"]} for ex in examples]\n",
-    "    return metric.compute(predictions=predicted_answers, references=theoretical_answers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 83.0, 'f1': 88.25}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "compute_metrics(start_logits, end_logits, eval_set, small_eval_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = TFAutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DefaultDataCollator\n",
-    "\n",
-    "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = train_dataset.to_tf_dataset(\n",
-    "    columns=[\n",
-    "        \"input_ids\",\n",
-    "        \"start_positions\",\n",
-    "        \"end_positions\",\n",
-    "        \"attention_mask\",\n",
-    "        \"token_type_ids\",\n",
-    "    ],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=True,\n",
-    "    batch_size=16,\n",
-    ")\n",
-    "tf_eval_dataset = validation_dataset.to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"attention_mask\", \"token_type_ids\"],\n",
-    "    collate_fn=data_collator,\n",
-    "    shuffle=False,\n",
-    "    batch_size=16,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import create_optimizer\n",
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_epochs = 3\n",
-    "num_train_steps = len(tf_train_dataset) * num_train_epochs\n",
-    "optimizer, schedule = create_optimizer(\n",
-    "    init_lr=2e-5,\n",
-    "    num_warmup_steps=0,\n",
-    "    num_train_steps=num_train_steps,\n",
-    "    weight_decay_rate=0.01,\n",
-    ")\n",
-    "model.compile(optimizer=optimizer)\n",
-    "\n",
-    "# Train in mixed-precision float16\n",
-    "tf.keras.mixed_precision.set_global_policy(\"mixed_float16\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers.keras_callbacks import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(output_dir=\"bert-finetuned-squad\", tokenizer=tokenizer)\n",
-    "\n",
-    "# We're going to do validation afterwards, so no validation mid-training\n",
-    "model.fit(tf_train_dataset, callbacks=[callback], epochs=num_train_epochs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'exact_match': 81.18259224219489, 'f1': 88.67381321905516}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = model.predict(tf_eval_dataset)\n",
-    "compute_metrics(\n",
-    "    predictions[\"start_logits\"],\n",
-    "    predictions[\"end_logits\"],\n",
-    "    validation_dataset,\n",
-    "    raw_datasets[\"validation\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.9979003071784973,\n",
-       " 'start': 78,\n",
-       " 'end': 105,\n",
-       " 'answer': 'Jax, PyTorch and TensorFlow'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "# Replace this with your own checkpoint\n",
-    "model_checkpoint = \"huggingface-course/bert-finetuned-squad\"\n",
-    "question_answerer = pipeline(\"question-answering\", model=model_checkpoint)\n",
-    "\n",
-    "context = \"\"\"\n",
-    "🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration\n",
-    "between them. It's straightforward to train your models with one before loading them for inference with the other.\n",
-    "\"\"\"\n",
-    "question = \"Which deep learning libraries back 🤗 Transformers?\"\n",
-    "question_answerer(question=question, context=context)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "问答系统 (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter8/section2.ipynb b/course/zh-CN/chapter8/section2.ipynb
deleted file mode 100644
index 24ce22dc..00000000
--- a/course/zh-CN/chapter8/section2.ipynb
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 出现错误时该怎么办"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from distutils.dir_util import copy_tree\n",
-    "from huggingface_hub import Repository, snapshot_download, create_repo, get_full_repo_name\n",
-    "\n",
-    "\n",
-    "def copy_repository_template():\n",
-    "    # Clone the repo and extract the local path\n",
-    "    template_repo_id = \"lewtun/distilbert-base-uncased-finetuned-squad-d5716d28\"\n",
-    "    commit_hash = \"be3eaffc28669d7932492681cd5f3e8905e358b4\"\n",
-    "    template_repo_dir = snapshot_download(template_repo_id, revision=commit_hash)\n",
-    "    # Create an empty repo on the Hub\n",
-    "    model_name = template_repo_id.split(\"/\")[1]\n",
-    "    create_repo(model_name, exist_ok=True)\n",
-    "    # Clone the empty repo\n",
-    "    new_repo_id = get_full_repo_name(model_name)\n",
-    "    new_repo_dir = model_name\n",
-    "    repo = Repository(local_dir=new_repo_dir, clone_from=new_repo_id)\n",
-    "    # Copy files\n",
-    "    copy_tree(template_repo_dir, new_repo_dir)\n",
-    "    # Push to Hub\n",
-    "    repo.push_to_hub()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distillbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model_checkpoint = get_full_repo_name(\"distillbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "OSError: Can't load config for 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28'. Make sure that:\n",
-       "\n",
-       "- 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is a correct model identifier listed on 'https://huggingface.co/models'\n",
-       "\n",
-       "- or 'lewtun/distilbert-base-uncased-finetuned-squad-d5716d28' is the correct path to a directory containing a config.json file\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_checkpoint = get_full_repo_name(\"distilbert-base-uncased-finetuned-squad-d5716d28\")\n",
-    "reader = pipeline(\"question-answering\", model=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['.gitattributes', 'README.md', 'pytorch_model.bin', 'special_tokens_map.json', 'tokenizer_config.json', 'training_args.bin', 'vocab.txt']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from huggingface_hub import list_repo_files\n",
-    "\n",
-    "list_repo_files(repo_id=model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoConfig\n",
-    "\n",
-    "pretrained_checkpoint = \"distilbert-base-uncased\"\n",
-    "config = AutoConfig.from_pretrained(pretrained_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config.push_to_hub(model_checkpoint, commit_message=\"Add config.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.38669535517692566,\n",
-       " 'start': 34,\n",
-       " 'end': 95,\n",
-       " 'answer': 'the task of extracting an answer from a text given a question'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reader = pipeline(\"question-answering\", model=model_checkpoint, revision=\"main\")\n",
-    "\n",
-    "context = r\"\"\"\n",
-    "Extractive Question Answering is the task of extracting an answer from a text\n",
-    "given a question. An example of a question answering dataset is the SQuAD\n",
-    "dataset, which is entirely based on that task. If you would like to fine-tune a\n",
-    "model on a SQuAD task, you may leverage the\n",
-    "examples/pytorch/question-answering/run_squad.py script.\n",
-    "\n",
-    "🤗 Transformers is interoperable with the PyTorch, TensorFlow, and JAX\n",
-    "frameworks, so you can use your favourite tools for a wide variety of tasks!\n",
-    "\"\"\"\n",
-    "\n",
-    "question = \"What is extractive question answering?\"\n",
-    "reader(question=question, context=context)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer = reader.tokenizer\n",
-    "model = reader.model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Which frameworks can I use?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"\"\"\n",
-       "---------------------------------------------------------------------------\n",
-       "AttributeError                            Traceback (most recent call last)\n",
-       "/var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_75743/2725838073.py in <module>\n",
-       "      1 inputs = tokenizer(question, text, add_special_tokens=True)\n",
-       "      2 input_ids = inputs[\"input_ids\"]\n",
-       "----> 3 outputs = model(**inputs)\n",
-       "      4 answer_start_scores = outputs.start_logits\n",
-       "      5 answer_end_scores = outputs.end_logits\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\n",
-       "    723         return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n",
-       "    724\n",
-       "--> 725         distilbert_output = self.distilbert(\n",
-       "    726             input_ids=input_ids,\n",
-       "    727             attention_mask=attention_mask,\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)\n",
-       "   1049         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n",
-       "   1050                 or _global_forward_hooks or _global_forward_pre_hooks):\n",
-       "-> 1051             return forward_call(*input, **kwargs)\n",
-       "   1052         # Do not call functions when jit is used\n",
-       "   1053         full_backward_hooks, non_full_backward_hooks = [], []\n",
-       "\n",
-       "~/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\n",
-       "    471             raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n",
-       "    472         elif input_ids is not None:\n",
-       "--> 473             input_shape = input_ids.size()\n",
-       "    474         elif inputs_embeds is not None:\n",
-       "    475             input_shape = inputs_embeds.size()[:-1]\n",
-       "\n",
-       "AttributeError: 'list' object has no attribute 'size'\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "inputs = tokenizer(question, context, add_special_tokens=True)\n",
-    "input_ids = inputs[\"input_ids\"][0]\n",
-    "outputs = model(**inputs)\n",
-    "answer_start_scores = outputs.start_logits\n",
-    "answer_end_scores = outputs.end_logits\n",
-    "# Get the most likely beginning of answer with the argmax of the score\n",
-    "answer_start = torch.argmax(answer_start_scores)\n",
-    "# Get the most likely end of answer with the argmax of the score\n",
-    "answer_end = torch.argmax(answer_end_scores) + 1\n",
-    "answer = tokenizer.convert_tokens_to_string(\n",
-    "    tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])\n",
-    ")\n",
-    "print(f\"Question: {question}\")\n",
-    "print(f\"Answer: {answer}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 2029, 7705, 2015, 2064]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs[\"input_ids\"][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(inputs[\"input_ids\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "出现错误时该怎么办",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter8/section3.ipynb b/course/zh-CN/chapter8/section3.ipynb
deleted file mode 100644
index bcecf775..00000000
--- a/course/zh-CN/chapter8/section3.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 在论坛上寻求帮助"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, AutoModel\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "model = AutoModel.from_pretrained(model_checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"\"\"\n",
-    "Generation One is a retroactive term for the Transformers characters that\n",
-    "appeared between 1984 and 1993. The Transformers began with the 1980s Japanese\n",
-    "toy lines Micro Change and Diaclone. They presented robots able to transform\n",
-    "into everyday vehicles, electronic items or weapons. Hasbro bought the Micro\n",
-    "Change and Diaclone toys, and partnered with Takara. Marvel Comics was hired by\n",
-    "Hasbro to create the backstory; editor-in-chief Jim Shooter wrote an overall\n",
-    "story, and gave the task of creating the characthers to writer Dennis O'Neil.\n",
-    "Unhappy with O'Neil's work (although O'Neil created the name \"Optimus Prime\"),\n",
-    "Shooter chose Bob Budiansky to create the characters.\n",
-    "\n",
-    "The Transformers mecha were largely designed by Shōji Kawamori, the creator of\n",
-    "the Japanese mecha anime franchise Macross (which was adapted into the Robotech\n",
-    "franchise in North America). Kawamori came up with the idea of transforming\n",
-    "mechs while working on the Diaclone and Macross franchises in the early 1980s\n",
-    "(such as the VF-1 Valkyrie in Macross and Robotech), with his Diaclone mechs\n",
-    "later providing the basis for Transformers.\n",
-    "\n",
-    "The primary concept of Generation One is that the heroic Optimus Prime, the\n",
-    "villainous Megatron, and their finest soldiers crash land on pre-historic Earth\n",
-    "in the Ark and the Nemesis before awakening in 1985, Cybertron hurtling through\n",
-    "the Neutral zone as an effect of the war. The Marvel comic was originally part\n",
-    "of the main Marvel Universe, with appearances from Spider-Man and Nick Fury,\n",
-    "plus some cameos, as well as a visit to the Savage Land.\n",
-    "\n",
-    "The Transformers TV series began around the same time. Produced by Sunbow\n",
-    "Productions and Marvel Productions, later Hasbro Productions, from the start it\n",
-    "contradicted Budiansky's backstories. The TV series shows the Autobots looking\n",
-    "for new energy sources, and crash landing as the Decepticons attack. Marvel\n",
-    "interpreted the Autobots as destroying a rogue asteroid approaching Cybertron.\n",
-    "Shockwave is loyal to Megatron in the TV series, keeping Cybertron in a\n",
-    "stalemate during his absence, but in the comic book he attempts to take command\n",
-    "of the Decepticons. The TV series would also differ wildly from the origins\n",
-    "Budiansky had created for the Dinobots, the Decepticon turned Autobot Jetfire\n",
-    "(known as Skyfire on TV), the Constructicons (who combine to form\n",
-    "Devastator),[19][20] and Omega Supreme. The Marvel comic establishes early on\n",
-    "that Prime wields the Creation Matrix, which gives life to machines. In the\n",
-    "second season, the two-part episode The Key to Vector Sigma introduced the\n",
-    "ancient Vector Sigma computer, which served the same original purpose as the\n",
-    "Creation Matrix (giving life to Transformers), and its guardian Alpha Trion.\n",
-    "\"\"\"\n",
-    "\n",
-    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
-    "logits = model(**inputs).logits"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "在论坛上寻求帮助",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter8/section4.ipynb b/course/zh-CN/chapter8/section4.ipynb
deleted file mode 100644
index b5909976..00000000
--- a/course/zh-CN/chapter8/section4.ipynb
+++ /dev/null
@@ -1,865 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 调试训练管道"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: You have to specify either input_ids or inputs_embeds'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=raw_datasets[\"train\"],\n",
-    "    eval_dataset=raw_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'hypothesis': 'Product and geography are what make cream skimming work. ',\n",
-       " 'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'premise': 'Conceptually cream skimming has two basic dimensions - product and geography.'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'ValueError: expected sequence of length 43 at dim 1 (got 37)'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[CLS] conceptually cream skimming has two basic dimensions - product and geography. [SEP] product and geography are what make cream skimming work. [SEP]'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(trainer.train_dataset[0][\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['attention_mask', 'hypothesis', 'idx', 'input_ids', 'label', 'premise'])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0].keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "transformers.models.distilbert.modeling_distilbert.DistilBertForSequenceClassification"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(trainer.model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.decode(trainer.train_dataset[0][\"attention_mask\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(trainer.train_dataset[0][\"attention_mask\"]) == len(\n",
-    "    trainer.train_dataset[0][\"input_ids\"]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset[0][\"label\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['entailment', 'neutral', 'contradiction']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.train_dataset.features[\"label\"].names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/git/transformers/src/transformers/data/data_collator.py in torch_default_data_collator(features)\n",
-       "    105                 batch[k] = torch.stack([f[k] for f in features])\n",
-       "    106             else:\n",
-       "--> 107                 batch[k] = torch.tensor([f[k] for f in features])\n",
-       "    108 \n",
-       "    109     return batch\n",
-       "\n",
-       "ValueError: expected sequence of length 45 at dim 1 (got 76)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function transformers.data.data_collator.default_data_collator(features: List[InputDataClass], return_tensors='pt') -> Dict[str, Any]>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "data_collator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "batch = data_collator([trainer.train_dataset[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_collator = trainer.get_train_dataloader().collate_fn\n",
-    "actual_train_set = trainer._remove_unused_columns(trainer.train_dataset)\n",
-    "batch = data_collator([actual_train_set[i] for i in range(4)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "~/.pyenv/versions/3.7.9/envs/base/lib/python3.7/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)\n",
-       "   2386         )\n",
-       "   2387     if dim == 2:\n",
-       "-> 2388         ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "   2389     elif dim == 4:\n",
-       "   2390         ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)\n",
-       "\n",
-       "IndexError: Target 2 is out of bounds."
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "outputs = trainer.model.cpu()(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "outputs = trainer.model.to(device)(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = outputs.loss\n",
-    "loss.backward()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.create_optimizer()\n",
-    "trainer.optimizer.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This will take a long time and error out, so you shouldn't run this cell\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trainer.evaluate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_eval_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TypeError: only size-1 arrays can be converted to Python scalars"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = outputs.logits.cpu().numpy()\n",
-    "labels = batch[\"labels\"].cpu().numpy()\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((8, 3), (8,))"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions.shape, labels.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.625}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "compute_metrics((predictions, labels))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    DataCollatorWithPadding,\n",
-    "    TrainingArguments,\n",
-    "    Trainer,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=3)\n",
-    "\n",
-    "args = TrainingArguments(\n",
-    "    f\"distilbert-finetuned-mnli\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
-    "    save_strategy=\"epoch\",\n",
-    "    learning_rate=2e-5,\n",
-    "    num_train_epochs=3,\n",
-    "    weight_decay=0.01,\n",
-    ")\n",
-    "\n",
-    "metric = load_metric(\"glue\", \"mnli\")\n",
-    "\n",
-    "\n",
-    "def compute_metrics(eval_pred):\n",
-    "    predictions, labels = eval_pred\n",
-    "    predictions = np.argmax(predictions, axis=1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)\n",
-    "\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation_matched\"],\n",
-    "    compute_metrics=compute_metrics,\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")\n",
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in trainer.get_train_dataloader():\n",
-    "    break\n",
-    "\n",
-    "batch = {k: v.to(device) for k, v in batch.items()}\n",
-    "trainer.create_optimizer()\n",
-    "\n",
-    "for _ in range(20):\n",
-    "    outputs = trainer.model(**batch)\n",
-    "    loss = outputs.loss\n",
-    "    loss.backward()\n",
-    "    trainer.optimizer.step()\n",
-    "    trainer.optimizer.zero_grad()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 1.0}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with torch.no_grad():\n",
-    "    outputs = trainer.model(**batch)\n",
-    "preds = outputs.logits\n",
-    "labels = batch[\"labels\"]\n",
-    "\n",
-    "compute_metrics((preds.cpu().numpy(), labels.cpu().numpy()))"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "调试训练管道",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter8/section4_tf.ipynb b/course/zh-CN/chapter8/section4_tf.ipynb
deleted file mode 100644
index a453bfb7..00000000
--- a/course/zh-CN/chapter8/section4_tf.ipynb
+++ /dev/null
@@ -1,442 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 调试训练管道"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ValueError: No gradients provided for any variable: ['tf_distil_bert_for_sequence_classification/distilbert/embeddings/word_embeddings/weight:0', '...']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset, load_metric\n",
-    "from transformers import (\n",
-    "    AutoTokenizer,\n",
-    "    TFAutoModelForSequenceClassification,\n",
-    ")\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mnli\")\n",
-    "\n",
-    "model_checkpoint = \"distilbert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "\n",
-    "def preprocess_function(examples):\n",
-    "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(preprocess_function, batched=True)\n",
-    "\n",
-    "train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "validation_dataset = tokenized_datasets[\"validation_matched\"].to_tf_dataset(\n",
-    "    columns=[\"input_ids\", \"labels\"], batch_size=16, shuffle=True\n",
-    ")\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "\n",
-    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
-    "\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        ...,\n",
-       "        [1, 1, 1, ..., 1, 1, 1],\n",
-       "        [1, 1, 1, ..., 0, 0, 0],\n",
-       "        [1, 1, 1, ..., 0, 0, 0]])>,\n",
-       " 'label': <tf.Tensor: shape=(16,), dtype=int64, numpy=array([0, 2, 1, 2, 1, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2, 1])>,\n",
-       " 'input_ids': <tf.Tensor: shape=(16, 76), dtype=int64, numpy=\n",
-       " array([[ 101, 2174, 1010, ...,    0,    0,    0],\n",
-       "        [ 101, 3174, 2420, ...,    0,    0,    0],\n",
-       "        [ 101, 2044, 2048, ...,    0,    0,    0],\n",
-       "        ...,\n",
-       "        [ 101, 3398, 3398, ..., 2051, 2894,  102],\n",
-       "        [ 101, 1996, 4124, ...,    0,    0,    0],\n",
-       "        [ 101, 1999, 2070, ...,    0,    0,    0]])>}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "  246/24543 [..............................] - ETA: 15:52 - loss: nan"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.compile(optimizer=\"adam\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
-       "       nan, nan, nan], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan],\n",
-       "       [nan, nan]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(16,), dtype=float32, numpy=\n",
-       "array([0.6844486 ,        nan,        nan, 0.67127866, 0.7068601 ,\n",
-       "              nan, 0.69309855,        nan, 0.65531296,        nan,\n",
-       "              nan,        nan, 0.675402  ,        nan,        nan,\n",
-       "       0.69831556], dtype=float32)>, logits=<tf.Tensor: shape=(16, 2), dtype=float32, numpy=\n",
-       "array([[-0.04761693, -0.06509043],\n",
-       "       [-0.0481936 , -0.04556257],\n",
-       "       [-0.0040929 , -0.05848458],\n",
-       "       [-0.02417453, -0.0684005 ],\n",
-       "       [-0.02517801, -0.05241832],\n",
-       "       [-0.04514256, -0.0757378 ],\n",
-       "       [-0.02656011, -0.02646275],\n",
-       "       [ 0.00766164, -0.04350497],\n",
-       "       [ 0.02060014, -0.05655622],\n",
-       "       [-0.02615328, -0.0447021 ],\n",
-       "       [-0.05119278, -0.06928903],\n",
-       "       [-0.02859691, -0.04879177],\n",
-       "       [-0.02210129, -0.05791225],\n",
-       "       [-0.02363213, -0.05962167],\n",
-       "       [-0.05352269, -0.0481673 ],\n",
-       "       [-0.08141848, -0.07110836]], dtype=float32)>, hidden_states=None, attentions=None)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model(batch)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  2,  5,  7,  9, 10, 11, 13, 14])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "loss = model(batch).loss.numpy()\n",
-    "indices = np.flatnonzero(np.isnan(loss))\n",
-    "indices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  101,  2007,  2032,  2001,  1037, 16480,  3917,  2594,  4135,\n",
-       "        23212,  3070,  2214, 10170,  1010,  2012,  4356,  1997,  3183,\n",
-       "         6838, 12953,  2039,  2000,  1996,  6147,  1997,  2010,  2606,\n",
-       "         1012,   102,  6838,  2001,  3294,  6625,  3773,  1996,  2214,\n",
-       "         2158,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  6814,  2016,  2234,  2461,  2153,  1998, 13322,\n",
-       "         2009,  1012,   102,  2045,  1005,  1055,  2053,  3382,  2008,\n",
-       "         2016,  1005,  2222,  3046,  8103,  2075,  2009,  2153,  1012,\n",
-       "          102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1998,  2007,  1996,  3712,  4634,  1010,  2057,  8108,\n",
-       "         2025,  3404,  2028,  1012,  1996,  2616, 18449,  2125,  1999,\n",
-       "         1037,  9666,  1997,  4100,  8663, 11020,  6313,  2791,  1998,\n",
-       "         2431,  1011,  4301,  1012,   102,  2028,  1005,  1055,  5177,\n",
-       "         2110,  1998,  3977,  2000,  2832,  2106,  2025,  2689,  2104,\n",
-       "         2122,  6214,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1045,  2001,  1999,  1037, 13090,  5948,  2007,  2048,\n",
-       "         2308,  2006,  2026,  5001,  2043,  2026,  2171,  2001,  2170,\n",
-       "         1012,   102,  1045,  2001,  3564,  1999,  2277,  1012,   102,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2195,  4279,  2191,  2039,  1996,  2181,  2124,  2004,\n",
-       "         1996,  2225,  7363,  1012,   102,  2045,  2003,  2069,  2028,\n",
-       "         2451,  1999,  1996,  2225,  7363,  1012,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2061,  2008,  1045,  2123,  1005,  1056,  2113,  2065,\n",
-       "         2009,  2428, 10654,  7347,  2030,  2009,  7126,  2256,  2495,\n",
-       "         2291,   102,  2009,  2003,  5094,  2256,  2495,  2291,  2035,\n",
-       "         2105,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  2051,  1010,  2029,  3216,  2019,  2503,  3444,  1010,\n",
-       "         6732,  1996,  2265,  2038, 19840,  2098,  2125,  9906,  1998,\n",
-       "         2003,  2770,  2041,  1997,  4784,  1012,   102,  2051,  6732,\n",
-       "         1996,  2265,  2003,  9525,  1998,  4569,  1012,   102,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101,  1996, 10556,  2140, 11515,  2058,  1010,  2010,  2162,\n",
-       "         2252,  5689,  2013,  2010,  7223,  1012,   102,  2043,  1996,\n",
-       "        10556,  2140, 11515,  2058,  1010,  2010,  2252,  3062,  2000,\n",
-       "         1996,  2598,  1012,   102,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0],\n",
-       "       [  101, 13543,  1999,  2049,  6143,  2933,  2443,   102,  2025,\n",
-       "        13543,  1999,  6143,  2933,  2003,  2443,   102,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "            0,     0,     0,     0]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "input_ids[indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.num_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)\n",
-    "model.compile(optimizer=Adam(5e-5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "319/24543 [..............................] - ETA: 16:07 - loss: 0.9718"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "input_ids = batch[\"input_ids\"].numpy()\n",
-    "tokenizer.decode(input_ids[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = batch[\"labels\"].numpy()\n",
-    "label = labels[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for batch in train_dataset:\n",
-    "    break\n",
-    "\n",
-    "# Make sure you have run model.compile() and set your optimizer,\n",
-    "# and your loss/metrics if you're using them\n",
-    "\n",
-    "model.fit(batch, epochs=20)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "调试训练管道",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter8/section5.ipynb b/course/zh-CN/chapter8/section5.ipynb
deleted file mode 100644
index 7ba6f79a..00000000
--- a/course/zh-CN/chapter8/section5.ipynb
+++ /dev/null
@@ -1,42 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 如何提出一个好的问题"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "如何提出一个好的问题",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter8/section7.ipynb b/course/zh-CN/chapter8/section7.ipynb
deleted file mode 100644
index af24f22f..00000000
--- a/course/zh-CN/chapter8/section7.ipynb
+++ /dev/null
@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 章节测验"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)\n",
-    "# ---------------------------------------------------------------------------\n",
-    "# ImportError                               Traceback (most recent call last)\n",
-    "# /var/folders/28/k4cy5q7s2hs92xq7_h89_vgm0000gn/T/ipykernel_30848/333858878.py in <module>\n",
-    "# ----> 1 from transformers import GPT3ForSequenceClassification\n",
-    "\n",
-    "# ImportError: cannot import name 'GPT3ForSequenceClassification' from 'transformers' (/Users/lewtun/miniconda3/envs/huggingface/lib/python3.8/site-packages/transformers/__init__.py)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "章节测验",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter9/section2.ipynb b/course/zh-CN/chapter9/section2.ipynb
deleted file mode 100644
index 0e5f7efd..00000000
--- a/course/zh-CN/chapter9/section2.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 构建你的第一个演示"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def greet(name):\n",
-    "    return \"Hello \" + name\n",
-    "\n",
-    "\n",
-    "demo = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\")\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def greet(name):\n",
-    "    return \"Hello \" + name\n",
-    "\n",
-    "\n",
-    "# We instantiate the Textbox class\n",
-    "textbox = gr.Textbox(label=\"Type your name here:\", placeholder=\"John Doe\", lines=2)\n",
-    "\n",
-    "gr.Interface(fn=greet, inputs=textbox, outputs=\"text\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "model = pipeline(\"text-generation\")\n",
-    "\n",
-    "\n",
-    "def predict(prompt):\n",
-    "    completion = model(prompt)[0][\"generated_text\"]\n",
-    "    return completion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "gr.Interface(fn=predict, inputs=\"text\", outputs=\"text\").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "构建你的第一个演示",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter9/section3.ipynb b/course/zh-CN/chapter9/section3.ipynb
deleted file mode 100644
index 1af722d8..00000000
--- a/course/zh-CN/chapter9/section3.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 了解接口类"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def reverse_audio(audio):\n",
-    "    sr, data = audio\n",
-    "    reversed_audio = (sr, np.flipud(data))\n",
-    "    return reversed_audio\n",
-    "\n",
-    "\n",
-    "mic = gr.Audio(source=\"microphone\", type=\"numpy\", label=\"Speak here...\")\n",
-    "gr.Interface(reverse_audio, mic, \"audio\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "notes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n",
-    "\n",
-    "\n",
-    "def generate_tone(note, octave, duration):\n",
-    "    sr = 48000\n",
-    "    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n",
-    "    frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n",
-    "    duration = int(duration)\n",
-    "    audio = np.linspace(0, duration, duration * sr)\n",
-    "    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n",
-    "    return (sr, audio)\n",
-    "\n",
-    "\n",
-    "gr.Interface(\n",
-    "    generate_tone,\n",
-    "    [\n",
-    "        gr.Dropdown(notes, type=\"index\"),\n",
-    "        gr.Slider(minimum=4, maximum=6, step=1),\n",
-    "        gr.Textbox(type=\"number\", value=1, label=\"Duration in seconds\"),\n",
-    "    ],\n",
-    "    \"audio\",\n",
-    ").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "import gradio as gr\n",
-    "\n",
-    "model = pipeline(\"automatic-speech-recognition\")\n",
-    "\n",
-    "\n",
-    "def transcribe_audio(mic=None, file=None):\n",
-    "    if mic is not None:\n",
-    "        audio = mic\n",
-    "    elif file is not None:\n",
-    "        audio = file\n",
-    "    else:\n",
-    "        return \"You must either provide a mic recording or a file\"\n",
-    "    transcription = model(audio)[\"text\"]\n",
-    "    return transcription\n",
-    "\n",
-    "\n",
-    "gr.Interface(\n",
-    "    fn=transcribe_audio,\n",
-    "    inputs=[\n",
-    "        gr.Audio(source=\"microphone\", type=\"filepath\", optional=True),\n",
-    "        gr.Audio(source=\"upload\", type=\"filepath\", optional=True),\n",
-    "    ],\n",
-    "    outputs=\"text\",\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "了解接口类",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter9/section4.ipynb b/course/zh-CN/chapter9/section4.ipynb
deleted file mode 100644
index 2726f88c..00000000
--- a/course/zh-CN/chapter9/section4.ipynb
+++ /dev/null
@@ -1,131 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 与他人分享演示"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "title = \"Ask Rick a Question\"\n",
-    "description = \"\"\"\n",
-    "The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything!\n",
-    "<img src=\"https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png\" width=200px>\n",
-    "\"\"\"\n",
-    "\n",
-    "article = \"Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of.\"\n",
-    "\n",
-    "gr.Interface(\n",
-    "    fn=predict,\n",
-    "    inputs=\"textbox\",\n",
-    "    outputs=\"text\",\n",
-    "    title=title,\n",
-    "    description=description,\n",
-    "    article=article,\n",
-    "    examples=[[\"What are you doing?\"], [\"Where should we time travel to?\"]],\n",
-    ").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gr.Interface(classify_image, \"image\", \"label\").launch(share=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "import torch\n",
-    "import gradio as gr\n",
-    "from torch import nn\n",
-    "\n",
-    "LABELS = Path(\"class_names.txt\").read_text().splitlines()\n",
-    "\n",
-    "model = nn.Sequential(\n",
-    "    nn.Conv2d(1, 32, 3, padding=\"same\"),\n",
-    "    nn.ReLU(),\n",
-    "    nn.MaxPool2d(2),\n",
-    "    nn.Conv2d(32, 64, 3, padding=\"same\"),\n",
-    "    nn.ReLU(),\n",
-    "    nn.MaxPool2d(2),\n",
-    "    nn.Conv2d(64, 128, 3, padding=\"same\"),\n",
-    "    nn.ReLU(),\n",
-    "    nn.MaxPool2d(2),\n",
-    "    nn.Flatten(),\n",
-    "    nn.Linear(1152, 256),\n",
-    "    nn.ReLU(),\n",
-    "    nn.Linear(256, len(LABELS)),\n",
-    ")\n",
-    "state_dict = torch.load(\"pytorch_model.bin\", map_location=\"cpu\")\n",
-    "model.load_state_dict(state_dict, strict=False)\n",
-    "model.eval()\n",
-    "\n",
-    "\n",
-    "def predict(im):\n",
-    "    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0\n",
-    "    with torch.no_grad():\n",
-    "        out = model(x)\n",
-    "    probabilities = torch.nn.functional.softmax(out[0], dim=0)\n",
-    "    values, indices = torch.topk(probabilities, 5)\n",
-    "    return {LABELS[i]: v.item() for i, v in zip(indices, values)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "interface = gr.Interface(\n",
-    "    predict,\n",
-    "    inputs=\"sketchpad\",\n",
-    "    outputs=\"label\",\n",
-    "    theme=\"huggingface\",\n",
-    "    title=\"Sketch Recognition\",\n",
-    "    description=\"Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!\",\n",
-    "    article=\"<p style='text-align: center'>Sketch Recognition | Demo Model</p>\",\n",
-    "    live=True,\n",
-    ")\n",
-    "interface.launch(share=True)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "与他人分享演示",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter9/section5.ipynb b/course/zh-CN/chapter9/section5.ipynb
deleted file mode 100644
index c378b7b8..00000000
--- a/course/zh-CN/chapter9/section5.ipynb
+++ /dev/null
@@ -1,83 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 与 Hugging Face Hub 整合"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "title = \"GPT-J-6B\"\n",
-    "description = \"Gradio Demo for GPT-J 6B, a transformer model trained using Ben Wang's Mesh Transformer JAX. 'GPT-J' refers to the class of model, while '6B' represents the number of trainable parameters. To use it, simply add your text, or click one of the examples to load them. Read more at the links below.\"\n",
-    "article = \"<p style='text-align: center'><a href='https://github.com/kingoflolz/mesh-transformer-jax' target='_blank'>GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model</a></p>\"\n",
-    "examples = [\n",
-    "    [\"The tower is 324 metres (1,063 ft) tall,\"],\n",
-    "    [\"The Moon's orbit around Earth has\"],\n",
-    "    [\"The smooth Borealis basin in the Northern Hemisphere covers 40%\"],\n",
-    "]\n",
-    "gr.Interface.load(\n",
-    "    \"huggingface/EleutherAI/gpt-j-6B\",\n",
-    "    inputs=gr.Textbox(lines=5, label=\"Input Text\"),\n",
-    "    title=title,\n",
-    "    description=description,\n",
-    "    article=article,\n",
-    "    examples=examples,\n",
-    "    enable_queue=True,\n",
-    ").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gr.Interface.load(\"spaces/abidlabs/remove-bg\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gr.Interface.load(\n",
-    "    \"spaces/abidlabs/remove-bg\", inputs=\"webcam\", title=\"Remove your webcam background!\"\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "与 Hugging Face Hub 整合",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter9/section6.ipynb b/course/zh-CN/chapter9/section6.ipynb
deleted file mode 100644
index 5cbdd4cf..00000000
--- a/course/zh-CN/chapter9/section6.ipynb
+++ /dev/null
@@ -1,105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 高级界面功能"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def chat(message, history):\n",
-    "    history = history or []\n",
-    "    if message.startswith(\"How many\"):\n",
-    "        response = random.randint(1, 10)\n",
-    "    elif message.startswith(\"How\"):\n",
-    "        response = random.choice([\"Great\", \"Good\", \"Okay\", \"Bad\"])\n",
-    "    elif message.startswith(\"Where\"):\n",
-    "        response = random.choice([\"Here\", \"There\", \"Somewhere\"])\n",
-    "    else:\n",
-    "        response = \"I don't know\"\n",
-    "    history.append((message, response))\n",
-    "    return history, history\n",
-    "\n",
-    "\n",
-    "iface = gr.Interface(\n",
-    "    chat,\n",
-    "    [\"text\", \"state\"],\n",
-    "    [\"chatbot\", \"state\"],\n",
-    "    allow_screenshot=False,\n",
-    "    allow_flagging=\"never\",\n",
-    ")\n",
-    "iface.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "inception_net = tf.keras.applications.MobileNetV2()  # load the model\n",
-    "\n",
-    "# Download human-readable labels for ImageNet.\n",
-    "response = requests.get(\"https://git.io/JJkYN\")\n",
-    "labels = response.text.split(\"\\n\")\n",
-    "\n",
-    "\n",
-    "def classify_image(inp):\n",
-    "    inp = inp.reshape((-1, 224, 224, 3))\n",
-    "    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n",
-    "    prediction = inception_net.predict(inp).flatten()\n",
-    "    return {labels[i]: float(prediction[i]) for i in range(1000)}\n",
-    "\n",
-    "\n",
-    "image = gr.Image(shape=(224, 224))\n",
-    "label = gr.Label(num_top_classes=3)\n",
-    "\n",
-    "title = \"Gradio Image Classifiction + Interpretation Example\"\n",
-    "gr.Interface(\n",
-    "    fn=classify_image, inputs=image, outputs=label, interpretation=\"default\", title=title\n",
-    ").launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "高级界面功能",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/zh-CN/chapter9/section7.ipynb b/course/zh-CN/chapter9/section7.ipynb
deleted file mode 100644
index f1cd1d9e..00000000
--- a/course/zh-CN/chapter9/section7.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gradio 块简介"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install gradio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def flip_text(x):\n",
-    "    return x[::-1]\n",
-    "\n",
-    "\n",
-    "demo = gr.Blocks()\n",
-    "\n",
-    "with demo:\n",
-    "    gr.Markdown(\n",
-    "        \"\"\"\n",
-    "    # Flip Text!\n",
-    "    Start typing below to see the output.\n",
-    "    \"\"\"\n",
-    "    )\n",
-    "    input = gr.Textbox(placeholder=\"Flip this text\")\n",
-    "    output = gr.Textbox()\n",
-    "\n",
-    "    input.change(fn=flip_text, inputs=input, outputs=output)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "\n",
-    "demo = gr.Blocks()\n",
-    "\n",
-    "\n",
-    "def flip_text(x):\n",
-    "    return x[::-1]\n",
-    "\n",
-    "\n",
-    "def flip_image(x):\n",
-    "    return np.fliplr(x)\n",
-    "\n",
-    "\n",
-    "with demo:\n",
-    "    gr.Markdown(\"Flip text or image files using this demo.\")\n",
-    "    with gr.Tabs():\n",
-    "        with gr.TabItem(\"Flip Text\"):\n",
-    "            with gr.Row():\n",
-    "                text_input = gr.Textbox()\n",
-    "                text_output = gr.Textbox()\n",
-    "            text_button = gr.Button(\"Flip\")\n",
-    "        with gr.TabItem(\"Flip Image\"):\n",
-    "            with gr.Row():\n",
-    "                image_input = gr.Image()\n",
-    "                image_output = gr.Image()\n",
-    "            image_button = gr.Button(\"Flip\")\n",
-    "\n",
-    "    text_button.click(flip_text, inputs=text_input, outputs=text_output)\n",
-    "    image_button.click(flip_image, inputs=image_input, outputs=image_output)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "api = gr.Interface.load(\"huggingface/EleutherAI/gpt-j-6B\")\n",
-    "\n",
-    "\n",
-    "def complete_with_gpt(text):\n",
-    "    # Use the last 50 characters of the text as context\n",
-    "    return text[:-50] + api(text[-50:])\n",
-    "\n",
-    "\n",
-    "with gr.Blocks() as demo:\n",
-    "    textbox = gr.Textbox(placeholder=\"Type here and press enter...\", lines=4)\n",
-    "    btn = gr.Button(\"Generate\")\n",
-    "\n",
-    "    btn.click(complete_with_gpt, textbox, textbox)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "import gradio as gr\n",
-    "\n",
-    "asr = pipeline(\"automatic-speech-recognition\", \"facebook/wav2vec2-base-960h\")\n",
-    "classifier = pipeline(\"text-classification\")\n",
-    "\n",
-    "\n",
-    "def speech_to_text(speech):\n",
-    "    text = asr(speech)[\"text\"]\n",
-    "    return text\n",
-    "\n",
-    "\n",
-    "def text_to_sentiment(text):\n",
-    "    return classifier(text)[0][\"label\"]\n",
-    "\n",
-    "\n",
-    "demo = gr.Blocks()\n",
-    "\n",
-    "with demo:\n",
-    "    audio_file = gr.Audio(type=\"filepath\")\n",
-    "    text = gr.Textbox()\n",
-    "    label = gr.Label()\n",
-    "\n",
-    "    b1 = gr.Button(\"Recognize Speech\")\n",
-    "    b2 = gr.Button(\"Classify Sentiment\")\n",
-    "\n",
-    "    b1.click(speech_to_text, inputs=audio_file, outputs=text)\n",
-    "    b2.click(text_to_sentiment, inputs=text, outputs=label)\n",
-    "\n",
-    "demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "\n",
-    "\n",
-    "def change_textbox(choice):\n",
-    "    if choice == \"short\":\n",
-    "        return gr.Textbox.update(lines=2, visible=True)\n",
-    "    elif choice == \"long\":\n",
-    "        return gr.Textbox.update(lines=8, visible=True)\n",
-    "    else:\n",
-    "        return gr.Textbox.update(visible=False)\n",
-    "\n",
-    "\n",
-    "with gr.Blocks() as block:\n",
-    "    radio = gr.Radio(\n",
-    "        [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n",
-    "    )\n",
-    "    text = gr.Textbox(lines=2, interactive=True)\n",
-    "\n",
-    "    radio.change(fn=change_textbox, inputs=radio, outputs=text)\n",
-    "    block.launch()"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Gradio 块简介",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From b3180dbc94a5881e6bc412db563bd01fac2076ab Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Tue, 3 Sep 2024 12:21:07 +0000
Subject: [PATCH 05/20] =?UTF-8?q?ch2=20sec3=20=E2=9C=85?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .devcontainer/devcontainer.json               |   2 +-
 .gitignore                                    |   3 +-
 .../section1_pt_behind_pipeline.ipynb         | 569 ++++++++++++++++++
 course/en/chapter2/section2_pt.ipynb          | 360 ++++++++++-
 course/en/chapter2/section2_tf.ipynb          | 245 --------
 course/en/chapter2/section3_pt.ipynb          | 154 -----
 course/en/chapter2/section3_pt_models.ipynb   | 294 +++++++++
 course/en/chapter2/section3_tf.ipynb          | 154 -----
 course/en/chapter2/section4_tf.ipynb          | 179 ------
 course/en/chapter2/section5_tf.ipynb          | 233 -------
 course/en/chapter2/section6_tf.ipynb          | 192 ------
 11 files changed, 1221 insertions(+), 1164 deletions(-)
 create mode 100644 course/en/chapter2/section1_pt_behind_pipeline.ipynb
 delete mode 100644 course/en/chapter2/section2_tf.ipynb
 delete mode 100644 course/en/chapter2/section3_pt.ipynb
 create mode 100644 course/en/chapter2/section3_pt_models.ipynb
 delete mode 100644 course/en/chapter2/section3_tf.ipynb
 delete mode 100644 course/en/chapter2/section4_tf.ipynb
 delete mode 100644 course/en/chapter2/section5_tf.ipynb
 delete mode 100644 course/en/chapter2/section6_tf.ipynb

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 3b183ca1..0a7cecec 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -1,6 +1,6 @@
 //devcontainer.json
 {
-    "name": "hf", 
+    "name": "🤗", 
     // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
     "image": "mcr.microsoft.com/vscode/devcontainers/python:3.10",
     // pip install needed python packages on creation
diff --git a/.gitignore b/.gitignore
index cc68a6d7..7452193a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 # Jupyter Notebook
 **/.ipynb_checkpoints
-**/.DS_Store
\ No newline at end of file
+**/.DS_Store
+**/course/en/chapter2/models/*
diff --git a/course/en/chapter2/section1_pt_behind_pipeline.ipynb b/course/en/chapter2/section1_pt_behind_pipeline.ipynb
new file mode 100644
index 00000000..47f99f06
--- /dev/null
+++ b/course/en/chapter2/section1_pt_behind_pipeline.ipynb
@@ -0,0 +1,569 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "[![Video Title](https://img.youtube.com/vi/1pedAIvTWXk/0.jpg)](https://www.youtube.com/watch?v=1pedAIvTWXk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "^C\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Behind the pipeline (PyTorch)"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
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"
+    },
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "> # Stage 1: Tokenization\n",
+    "> ---\n",
+    "![image-3.png](attachment:image-3.png)\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* notice how padding happens since sentences in batch are not same size!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
+       "           2607,  2026,  2878,  2166,  1012,   102],\n",
+       "         [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
+       "              0,     0,     0,     0,     0,     0]]),\n",
+       " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "         [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoTokenizer\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "# most important method of class is `.from_pretrained`\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "# download & cache config & vocab associated with checkpoint\n",
+    "# checkpoint used by default is sentiment analysis checkpoint\n",
+    "raw_inputs = [\n",
+    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
+    "    \"I hate this so much!\",\n",
+    "]\n",
+    "inputs = tokenizer(raw_inputs, padding=True,\n",
+    "                   truncation=True, return_tensors=\"pt\")\n",
+    "\n",
+    "# padding=True --> will pad shorter sentence in batch\n",
+    "# truncation=True --> any sentence longer than what the model can handle is truncated\n",
+    "# return_tensor --> pt option selected ; PyTorch, TensorFlow, or plain NumPy\n",
+    "#   * if return_tensor not specified get list of lists\n",
+    "{\n",
+    "    'input_ids': torch.tensor([\n",
+    "        [101, 1045, 1005, 2310, 2042, 3403, 2005, 1037,\n",
+    "            17662, 12172, 2607, 2026, 2878, 2166, 1012, 102],\n",
+    "        [101, 1045, 5223, 2023, 2061, 2172, 999, 102, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+    "    ]),\n",
+    "    'attention_mask': torch.tensor([\n",
+    "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+    "    ])\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
+       "          2607,  2026,  2878,  2166,  1012,   102],\n",
+       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
+       "             0,     0,     0,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `inputs` keys\n",
+    "* `input_ids` vocab dictionary lookups of `raw_inputs` \n",
+    "* `attention_mask` indicates where padding has been applied with 0s\n",
+    "  \n",
+    "> # Stage 2: Model\n",
+    "> ---\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "API used will determine what portion of of model is instantiated\n",
+    "> ### `AutoModel` outputs model body only → without classification head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModel\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "model = AutoModel.from_pretrained(checkpoint)\n",
+    "# download & cache config of model & pretrained weights\n",
+    "# AutoModel API only instatiates body of model\n",
+    "# --> part of model that is left once pre-training head is removed\n",
+    "# --> Output will be high-dimensional representation of sentences passed that is not directly usable for downstream tasks\n",
+    "outputs = model(**inputs)\n",
+    "print(outputs.last_hidden_state.shape)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `AutoModelForSequenceClassification` outputs model with classification head\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "* There is an `AutoClass` for each common NLP task\n",
+    "\n",
+    "* outputs are not Probabilities yet, don't sum to 1\n",
+    "  * model outputs Logits! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[-1.5607,  1.6123],\n",
+      "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoModelForSequenceClassification\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
+    "outputs = model(**inputs)\n",
+    "print(outputs.logits)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> # Stage 3: Postprocessing\n",
+    "> ---\n",
+    "* To convert Logits into Probabilities a SoftMax layers must be applied\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[4.0195e-02, 9.5981e-01],\n",
+      "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Last step is to know which positions correspond to which labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.config.id2label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
+       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "classifier = pipeline(\"sentiment-analysis\")\n",
+    "classifier(\n",
+    "    [\n",
+    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
+    "        \"I hate this so much!\",\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{\n",
+       "    'input_ids': tensor([\n",
+       "        [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172, 2607,  2026,  2878,  2166,  1012,   102],\n",
+       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
+       "    ]), \n",
+       "    'attention_mask': tensor([\n",
+       "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+       "    ])\n",
+       "}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_inputs = [\n",
+    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
+    "    \"I hate this so much!\",\n",
+    "]\n",
+    "inputs = tokenizer(raw_inputs, padding=True,\n",
+    "                   truncation=True, return_tensors=\"pt\")\n",
+    "print(inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModel\n",
+    "\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "model = AutoModel.from_pretrained(checkpoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 16, 768])"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outputs = model(**inputs)\n",
+    "print(outputs.last_hidden_state.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForSequenceClassification\n",
+    "\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
+    "outputs = model(**inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 2])"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(outputs.logits.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[-1.5607,  1.6123],\n",
+       "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward>)"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(outputs.logits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[4.0195e-02, 9.5980e-01],\n",
+       "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward>)"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.config.id2label"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Behind the pipeline (PyTorch)",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/course/en/chapter2/section2_pt.ipynb b/course/en/chapter2/section2_pt.ipynb
index 9cdf82bc..eb9eb79f 100644
--- a/course/en/chapter2/section2_pt.ipynb
+++ b/course/en/chapter2/section2_pt.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Behind the pipeline (PyTorch)"
+    "\n",
+    "[![Video Title](https://img.youtube.com/vi/1pedAIvTWXk/0.jpg)](https://www.youtube.com/watch?v=1pedAIvTWXk)"
    ]
   },
   {
@@ -16,11 +17,342 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "^C\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Behind the pipeline (PyTorch)"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
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"
+    },
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "> # Stage 1: Tokenization\n",
+    "> ---\n",
+    "![image-3.png](attachment:image-3.png)\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* notice how padding happens since sentences in batch are not same size!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
+       "           2607,  2026,  2878,  2166,  1012,   102],\n",
+       "         [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
+       "              0,     0,     0,     0,     0,     0]]),\n",
+       " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "         [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoTokenizer\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "# most important method of class is `.from_pretrained`\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "# download & cache config & vocab associated with checkpoint\n",
+    "# checkpoint used by default is sentiment analysis checkpoint\n",
+    "raw_inputs = [\n",
+    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
+    "    \"I hate this so much!\",\n",
+    "]\n",
+    "inputs = tokenizer(raw_inputs, padding=True,\n",
+    "                   truncation=True, return_tensors=\"pt\")\n",
+    "\n",
+    "# padding=True --> will pad shorter sentence in batch\n",
+    "# truncation=True --> any sentence longer than what the model can handle is truncated\n",
+    "# return_tensor --> pt option selected ; PyTorch, TensorFlow, or plain NumPy\n",
+    "#   * if return_tensor not specified get list of lists\n",
+    "{\n",
+    "    'input_ids': torch.tensor([\n",
+    "        [101, 1045, 1005, 2310, 2042, 3403, 2005, 1037,\n",
+    "            17662, 12172, 2607, 2026, 2878, 2166, 1012, 102],\n",
+    "        [101, 1045, 5223, 2023, 2061, 2172, 999, 102, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+    "    ]),\n",
+    "    'attention_mask': torch.tensor([\n",
+    "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+    "        [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+    "    ])\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
+       "          2607,  2026,  2878,  2166,  1012,   102],\n",
+       "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
+       "             0,     0,     0,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+       "        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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SUjoREAEREIHhCZwawxyHR20iIAIiIAKnh0Dt9KgqTUVABERABESgQmBUxjmOhhoRK3C1KwIiIALHTqBfm9JJ9NgnbpjrF06ndCcB7NhbjwoUAREQgVNIQP3zKTxoUlkEREAERKBCYBTGOY6IGhUrYLUrAiIgAsdGIGxJ4eYFd+udu4XneUfpr21vb3fSb5RldJUVBYfLhLmfMPL9XBDj4peHnzZ/t/oNW4/jakinXf9hOSu/CIiACIiACIiACIjAWSYwgqvdveO6Mj/Lx0F1EwEROLcEhuhC2YNHLx5ucOxmTxqiuBB9aPdEDHMBJHfpz/dzGBGeg6sjfbkfCQ5d/RPOUFRy1OqX7EYtuIrrtOtfrY/2RUAEjpZA2TkNVkx0aUOKGaxw5RIBERABEXhtCMR4MAqF9o0poxQ+CgUlQwREQAREoJ3Avo67PTr22J03i53o2ulGdrrhZ7LqPsOOazt2w1wOpOrnfoQRQA4pB8LwMMzR9S3PGGGvs4tKhMrhjkrdnJv7R10AFT3t+o8KtuSIgAgcTCDvkA5O2TU2ujC6VXHV/a5CFCECIiACInCmCMTY0E+leqWNsSTc8iK9H+FKIwIiIAIicHwEyo46FXlQ/864+FUVDDG5G/5q2uPYP3HDXIAKoOGy8t3AMDx+pWHuKGjlyoxCflahqDfF5v5RFRN8yiJHVZdCYK5z7h9W/9C3o/4ROWwhyi8CInA0BPJ+pnK+5lHdCq9kKZNFXrrRN5SRRVi+L78IiIAIiMD5JRBjRhDgfjUs4nI3H1/K8aifjLkQ+UVABERABI6OQNk59+7Xe/X9maiO84ujq0RnySdqmOOywiow7ucDY652p7ExB5qnHZW/U5mjkN2p3qOQSxnBJOcYYaMqI/TP+eT+QcsJPUP32M/rNahs5RMBETg6Ap3O/2pYdb+qTfV8j37goHSM65aumk/7IiACIiACZ4TAHkYUDio+cOSjxx6Ci304TBJjT/hjP0hE7twNP9Pk/sgjVwREQARE4HgJVPvuw5Ze7cur+4eVN+r0r4VhLp77jcpVIcVB6ORGWOQdxu0kKw/L/cOUw/pRFq8pjmqrFRCrLEdRXuhPWW1VwE7b/iELC52ZLfQON0RV9yNcrgiIwMkSqJ77+X7u76Ulz/H8F68tiHM/XMqppuslW/EiIAIiIAJngAAHlepFNC4i+X2FPYTTMOfjDveRNBYCxHyj05jUaWw5A6RUBREQARE41QTyvrnfiuR56I8nLHN/m6wYFPKMbQmOZ+fYDXOsFuteHSiDR17tCIv01f0mAiIu5Ob5B/FHGZE39nP3hI9ZqNbTPSo9KTd49FRiwARHpfuA6iibCIjAIQhE/1B1KSLCuonjuR/GOLr8NRCYh0f/EGGRLsK7yVa4CIiACIjAGSCAgWRvL5nZYkzZC8McqpcMcTV36W9i5NllHvjjl1NoHzvSXntYuQYvz3YIf5IWuh4io5KKgAiIwLkmwN6zvT/mfntv6vFFouTUPA/9MUegG/t0Uzp4uOXi2iJS9HH9PVHDHBlwwNzFj/5gkgZR7CMgwulGOLxleO6P/AwbdKvK4H23CAt32OPVLX8K7xY7WI1a2g+Wv1Ou1NQ7xShMBERABFKvw/4yfmRCP/vw6EcZ1mljD5gPnnUMndxv4DdW/OpIFOkijvH0VzeWN9petVqC9kVABERABI6DAFfDWfF4BQ1zu9jfxagSYwuNc+zxm3BpiKNBbgch/DEdx4NyNR38+dYaJ6pXuZGLqVup8ry9/SkfJWkTAREQARHon0De69Jf7U3DSJfC05yB/jR/qPn8gTf4OUeIOUP42+YN0UEnQf0rOMKUJ2qYC0MbDXMxqKaBNO2znts+qPJOV9N28Nve3bXVtTVb39iwrZ0dq9fqaek60jrPIWH68Is/7uIvB/0Uhr9DymZ9uNUoMNtCbLhZ1FDeVIeWCC7xH2bz7IXuISpcys39w5QTeav6R7hcERCB15UAegH8jy4ud9PjRWmiVNWe0yDvPzCZauBX4w9973i9bpONMZudnLSZCfzGx226PoZBtjXwjiFjabCDYMpxWXCjfJYXYfRrEwEREAEROJ0E2K/7/KEwyqU5BK/V0wq5Xbg72NtGqp0m5g1w17e37fHLFz5v4GgQxjkfF/LBIR80kCrt8m9bxADgilEuL2sAKcoiAiIgAueRALtO2k/cpb/thx4d84Z6vWET42M2jnnDWKOOOQT8mEdMNBo2PTZl47AZ+U18ZE438/fcZe9MA13IhPfEttrW1tawo82hlWeBuVEuDHM0ym0hzu9+0RAHI9z67o6tNndtq7mNAXbXNra27PmLF/ZyZdnWNzesDti8e0aZwxqeWBGXg79JP7g0zOHHfd941IbZWAC2EOMuwmI/xQ7/14uB0KK45A5bSAfdcyPjsOLzWrOoqAPD2+qRJ5RfBETgtSHgfQCNal00auL9A77iIYtHcmzIA9cHVg6cWBK3h7RulBuftAszszY/NYPflC2OT9kEBlsOuhNj4zaBjPyNQ0qsmmu7A1aU1U2nIlqOCIiACIjAKSDA63HOG/jz1XB+jZ5upG/DEEcj3GZzxzb3duFi/gB3dWvTbt2/a+uYQ3CAomHOPRx33I9dbH4DyS9s01iVTHP8i6vQuBBNSQ/3F2OU33BiLg1Gh2On1CIgAuebAPpedpv1wnV/0XOHnwu1xsYaNjU9bZOcG4zRKIe5Qo1GuXFbnJ63GYbDbjQGw904MnIhAG/s82a//xAWBrqTAn7shjmOa/zFwJoG1TS4biN8C7HrMMItb63b01ev7AUMcC/XV20LBrptrJCjcY7GMq6g8wkeLKExWlKubwA9yMZsabCGSz8mhxxIfWl8IXJY418YskLDsP4Oom+3PMEhdK3ud8vXK9x1LoTRH/tRl175DxPPYkJ/5os6HEaG0oqACJwMgdQncLoDH/pQ33eXfWq7TujOy837dAYUPx+EYaBrbu1YbbcJg1zdFmfm3FC3ODtvNy5ftgUY6mYwIHOQpXEuDbKlSO87WGSl2FaCqi/06TtDVYD2RUAEREAEjooA5w28ie8u+utNzAfWdjZscwdzh411u/Pgvq3gxv02wt2Ah2v5HVxsb2Ae4TfZOQhhzOD1fXVrN8SlwSBuzedjVTVfr30vMv3plVTxIiACIiACOQF0xeytw2ZCP60/bkSDscBd9K80ztVx0x6GImvSZoSbNFjlZWNIw5v7S3PztjS/YJcXFvwpnHGusOP8AXMLrqbj/CHk8kb/SWzHZpiLuQ5dDowcUH3ARAANcpuYPq1i0Hy6tmzPV1fs2dord9e3N/0R1hosny4DR4Or5GpuNAM+uG0bDkyERPq2+J47NMQhEX7JIAcP/QhyeT3z905A8dzchVC6EeYRA/7J9XM/hNLNwwcU3ZYtdHUXwulGWFvCAXZCV3chNPYpKvcPIFpZREAEjoFA6gvYD6d/7BwY5nvsnzk5ybY0EUIATvAwzIVbw+DKFwXtYYDdw+DKfd7VGsdvujFuly4s2NW5C3YNv3k86jrFARbiJyAuL4V+DrZ9bdHR5AL6yqhEIiACIiACgxKIrjfy5/vh5/yBRrkNBHDusIGZxPL2hj159cKWcRN/BYa5l5hD8LU3fM8clkZgOUTD9sbq1uR8gWH48fqe/9o2xtH6VgZj7HE/3CK8jGrL2O/OcLn7LUXpREAEROAsEYieM18x12akKwaI6NP3sHK6yTEAcwfDTX3/7TSLV+NM2cLsrM1NztiFqWm7DEMdV9iNY5bA+UO8GieMdMfN8VgMczGg0g2jHAdUPrrKx1Y5yL7a2bInGFTvPH9sj189x6OqK74Uvc5nhacmbXJmxhoYYDmp4zPENMzRMto2yeOg6kcvHRovN44mygg94O25MS0HZP/RX/wOJ6VLMRBGtVy1wt8l5aGCqWNs7qf+RUC4ET+o6zojc+lCcPgHlVnNV9U94iM89uUeEYG8sfCcOsoNF7t5caMqqtS69IxK8uskZ9jKHQ15auU9cJrRpHlQ1ksc1KTSigToRdWww/fMcfClMY4uB9rN9XXbXt+w5sYW7oKZXVtYsjeXLsM4N+/GuRksUZ9CIWP4URfe9Uo69dlXsWxuw+JNUvRXBERABESgBwF2u9H1Mmns5y7nD7yhv4FxYQ2/FTxB82pn3V6sL9uDp4/t1Wp6uoZzhjrGgRpWTtTxXlLe2KefhrpURrgsqbW1zycQHmMABx/PiTElwlrZ+vZB5UJO31mUUAREQAREAH1neR1f8ROOd9H0pE6WPvcznPOIPRjn1nHDZmcTFicY6PgI6yxu5l+cvWBvXrpqC9Nz/g5rzhcmURLnEJPw84b+EF0+ch9+O3LDHMeh/MeVcr5CDqDiBa2rWD/32eNHduvRfXuJ5ei7oFCfoEFuyhoT47jZhdVyfkQSHv6lTD8AxeTPdyMcCVKawsMdxnmm5G/95Z2wlN6TlQM30iMgz9LThFC2jJb0rr6izFSx/anycvfHdgtJdcljMzx58NB+suK0N7lDi2sXUOEekYMxidxy+yYQoHEcaPzm1rPt9y28lZDFNHFX46g2XmSzfZ61zWs1qorhIAx0bMH2wA1ye6Q4MDsjozulHB9c4XLVAu+E1bmSbnvHNpZXrIZBdho3axYnp+3G0iUY6S76e+imsDQ97nzF4FqummPjq27DKlyVp30REIHjJxDnts7n42dfLTGORTW8ss/r1DC65VEM41dV+WU9zhv4Rmwa5lYR+mx3y+6vv7D7L57Y0+fPbAvjwTg+EjSN9ws1YIzj40w1XL/s4f1CvJHPp2uoTvyqI1SaH+QKw49saXxshQ/brFqSoIw2ERABERCBngTY73rfW3Sgab9lg9g/2ysSwqFhjh1/Ezdy8Aer6HCDfwPPaW5tWwMrxGbHJ+zm1ev25pUrmDPUbK42ZvPwzaAQv7GPvBxCqhuC27YOSdri+905UsNcKJ0G1zTwxuC6AUr8UtIyHlW98+wJVso9sScrL82mJmwMd7t4x2tsYgJ3uYAFtaWskJcMcimAINKAyviUwv/GxLHISyBlfvgDYAzGaT9CkbblZdaeW5RdziZ75YAy3pBypYo8Sf9eAjrEV2RVdjtkGCAo45JPnAeQdGCWvhkUlczUapPbt5y2XOd4p9Jo2u4gjxBLFFOeNyOUHaK6tYmIP83uSIxzcRAOCaLMdhDgMtEhhWfJq+J9H3LZZhpwaZDbxnuE8Jk9q8M/hs78It4hcXVmwd7AI65LWKI+hwGXj7XSIBe/Um4nHcvITBF5RUAETgeB/JzWuXzyxyw/HgdoE4Y53rjPs/CJmh0E0Di3jStm/vi11Uebq3Z37aV9vvLEH13dxeNKNbzkewKGuUmsgmhwhVwxB+Ajq6VM+sudpFDbbha5Fxe4Ze62lAfURlEiIAIiIAKjJFAO5+iGw88bL+Fv+Vqlph4bf+mJvp27MM7t4H2kTdzMwR0df/pmER+Yu3JhEe+eu2CXJ2dtqT5p8xgv+N5qlhE390N6kt0ar5gmdIk0g7q1zc3NkD+ojI75cqG8w8UBly7vePEDD5hO+eq4x8sv7ae3PraX+GLSHlbJTS3MuVGuged9eccr7phRXllpAsZI7v+8IEzV3E2JwssM/JhDTP7L8FwW/KPavJxyMO8htdC/U6pcz07xncJKNp0iRxTWUa8S/IgKGUDMQXXvqPMAZSjLEREoLp6PSDo6gDPaAsDtoHZ/ZDwLwSdJlf0su9n0YQgsScfqORrpdtbXrLG1a7NYIfHm4mV7a/Giv39uEqy4co53vvzuVw6nU0VOEmyum/wiIAL9E6ieyzqP+2d3VCmrx6RLOUzWxPHijfs8C41wWxjDOWeA6Q0fczB8HG7Xbi8/tY9f3LcHK6/8qZoZTKrGxyb8NTd1rJSuF9cVlOWrrMty2SiKhgEnlYW/yVOmSh7PnYVV97MoeUVABERABI6UQNFzF2VwDpSegfFevcNc0rt1jB/ec1fmgsxTZ9j2rj9509zctEk8efPOtTfszQtLdn1qzhYgfwJyWQrnEHn5Lhth4boO2B/FduSGOSodj6/yztcm9rlaDk/62h28E+LWw7vuTs7jS3uXl2wP75TjAM18vOtFNwxr8GLDwWBg4TqoDLhHpWhPk/ZbErifw2XSUW1eSj+GOSiRptXdNSnr0Ydy3aX0kfmQSUKvKDP2Dymmr+RRhjeCA3KU6bqkOUoduxQ5umBUrpv+Xu9elR9EExTYrUyKK4vslugAnQdRZ5A8oWOoGPsHViwKOkH9e+pZ6FamC51PwA22J1C0t8FYnt5A/1/nC0tx92t3dc1sbdOmYZz78I237P3rN2wWg20Y5/a9zLXfSrwOwE8CtMoUgdNCoHou65w9+SNXPSZdNGIyXvfnK+YYto0BexP9Ow1z6QNx2/ZsfcU+x+Or9149s82xmk1glfQ0Vkdz+lSDUc5Xynm5xYSsHPRRQPEoTOG0aZOr2nrolUnymNzfll07IiACIiACx0Qg2VDSOjkf6mFAy4f8vKd2P8aRXT7KSkMbX3GAf3xstYHVX02smtvZwHurYZyjIe79y9fsw8vX7WJ9HE/ccGTZw0chfITxMlhOyA+XYXn5w2A4UsMcFebPH1+Fy9VyNMyt7G7bw9UX9unjB3bv6RN8Qq9hs4sXbGpuzjaamFwxFyHwMVZsLbNaqnbNV5t5lK+a8HgWhK1wCkKBqSUhpTqav15KL8Mcq+bF82/odzT6nAepvXCTQaeLsPPAZtA6prOlvZMbVJbyicCREsBgy8daffUcPgyxhw9CNFfXbXdl3d5YXLL3rrxhNxYW/R0SNM7FY61tPW85aBygaVuGA9IpSgRE4GQIVM9jnbMncxzyUqvHJI+r+GmY41M1fEqGG13OGXgjH1MmfOxhx55vrdmtJw/tOT4Ut7a3a/XZaavz6RrciOH1tF+1VI47hojWvCC/5ka6buolERFLN/zwahMBERABEThRAmmGGvNUuN5pVzr/TEPOa3f5fmq8LI43cNyPbp3zB/6aW1t46mbd1vHe6muz8/bBxav2wdIVm8I3Dhp7TRvnO6tRiH+IDnJjRAiXJXcvPVOkD++RGubSY6h8dBUGOWjPO15rAPNsc90+eXzX7mLF3PLWhl25cc3G8dVVw6Ora3ikle+T4BeU0otbkRkja6o8D0KqOg0yDsIjEJ8SeJXDEBOQsqgSZi82kbdXujzetTzIUlTonPK49nn2kfkPUoHQch4jK/SEBB1Y10KnaA8npOKRFevHMW/4oywJ7STOtWp7Kc+NakSUP2AbK1nxBCUAAEAASURBVOVSTlV2W+T+6Cg6dytZ8qjX0t9PWz5r5+8wByKaCI1yXJLOFXRjWJZdw1eXVp48x4tb63YNn0H/4o2bdnl2zmYxgeNXljiFSwvgO5QeQqtRp60xVfXXvgicdQLVc1fn7Mkf8eoxOUAjXqfRMMcsnDvQ3cAfGuaW97btBb6++mj1lX1097ZtY3I1gadsOG9g2l28zBuzLfiwMWN27LlbzBbcV/5BmhRXhuzzdF85ty+pAkRABERABI6DAAaLeIeod/UwmKUuP+v4K3rQPuNDA7/igPS7uJFPw1EDssYwdtSxmo6r5l48e2YziLoxPW9ff+s9W8THhMaRZxKGOTfOYfbAkSbGjnBZcvfSK8r02D0yw1wY5ej646sAsIYh9Nnmmj149cJ+cfdzW8cdr7HpSbtwEY+wwhC3DaPdLmenBTh+eC+qj+kW/AGf4dhDvIOA7KB0kBHGxaWsff09LOTXwTDXz+T+IEZ9gXnNEh1YZxzEwx7316x6XdXxerHtH8UGbnG2ebs+omI6qV6ed3mZhz2Omf6dynjdwg5swxVlz9r5W6leX7uVpuEr5jhYjgNOA59F38Zdr62XKzYBQ92Xbr5jb128ZJdm5vGIKxZoI11ai50VlQvMgt1bNshqhPZFQAReGwLVc1jn7ckfmuoxOUgjHK983kD/OvKvYJ7wZHfVbj97jA/FPbbnmDyNzUzZ9Py81fH1Vc4N0mUQr1jSQW9/rxwE+XKKSoPAbi/1ZJg76IApTgREQAROgkCrr/de3fv31py1k0apr0/GOf7lPqVw3tCAYa4G+9PezpZtrK1ZDU/czOGFpu8uXLI3OXdYWLApvhKHxrnCMNepjMoI0ylJX2FHapjj3S8Orlwxt4aRcwX3w27jC6yfPXngxrn61LjNXJi3Sdz12kWNdrBckMvZfRCFmwbXVFUOvsmH+GLzyewhDBPlIFwVFAJzF4n7SdaehZnKUvKo5G+TSemHLWG/yDzkoKLzdO5H0Qdoui+5AroT6HQUh2GbyztIjscdov13r0GHmPIORIpL3ViHdMMEdahczzZ8mHZbgOwps0sdXL38YLSl298ftUX3uTOobuw6OuDrs9TTn6zt2GCHgytZjgPMGNwa3je3+eIlHmndsDeXLtk7l6/igxCXbA4D6xTaNt8117a5wLaQtNP1+HdIqyAREIGTI1A9h3XuntyxiJKrxyTCO7nF8eK8IX40zL1sbtudjWf22dOHdu/Zc9sZb9jE9BR+eK8cJktppRwzY0wuJmicO3BrFZ/iPTD+IKgVH4Htrgxz7Ty0JwIiIAInT4DzrzRg+N8+DHMpNcYW2pnwq2PsiPGC8wejYQ6vUsNSOtwNWrdxzB0uNibtnSuYO1xJj7TSOIdPDJUfkeP4EXLDHQWbYzHMccXcGkA8xwD783u37Rf379jWWB13vPDSVixHr+EjD+lOGZ4BxlCZD5b81Hmr6u1V9kntIQwTudx2SZ33DgvaNT9opg0FWjL318uzIrhfPbsV1SqjVa9OMmPlTZ6e6fL9loTk6ySnmuY87bdY8aIQx84BjYISJbefC1WuqahRlFWRjIpEvdhxuRZHUAxLjQvoKI9h3dq1q1AkPKw6uXyW0WvrKR8CW5R6Sescn9ezk369dIjzt7P0sx1aZUN+5Ml3RdA4N4E7YNsr+MTQ8pp/COLdS1ftXRjnlsYnbcbvemV8cmH05wcj92dZ5BUBEXjNCOTnMVXTuXvyB6h6TA7SqDheYZTjhyDWkP/Zzqb94sU9u/3isT3Faobx+Vkbm8D0qIGeHn15OtC8/io+/ICQuA5rFU/hlQaB3VY8ojtsMsx1gKIgERABEThRApx9pf7c/5Zz1kofDx0jhC5/O3wyE/YoflyUc3bfYIDa44chsDqbj6vW1jetvrxuk+tb9jbmDTevXbO5SazSro/hdTh41xxGDp+/IX9Lk0LWCJwjN8xxkOV7IpZhiby3/tJ+du+Off7kkc1cXLAJvLi1gQEWI2xpmONAmQ+WnSefcUiYOE/dm0i/qeN49ZbYSjGMYS6fpHeuc6sc+vL0eUw3vTvVm+W00tOXp2rFtOQfbChqpTs/vkQJ7THDdcgm2RlWIS8MV50S+dEaSWGQ7uVFbVJpcTfhKA1zeZPLEHaqroflLZQ6t+13yeVyKwk7lVVJ4kyqYZ3ydSn2wOD8/O0ms1p2VWA//UQ1z1naDz45v3iRq78zAqvm9tY3bOfVil2fx4cgLl2xt/AZ9HkMyOMAUeYLQYRDfxlR8TNemwiIwOtJID+PqWF+Hr+eGkurDgQ4Z+DXWPnROBrmHuP1N39z52f2dGPVthp418/CBUwZfGqEQ4yD7BdfPNjJMEcfL4var1YZyl+2YbfaZLJY98owVyWifREQARE4YQKY/Pi/skuPOXgZ4ArGHudb9PPHPp/fMeCir5i3+9OZMNixvx/Dq9TGt5s2trZpW4+f2xW8MuHm1Wv4mNxFvKd6wr/aGvMHysOog7+j3Y7MMJff9aJh7gWe3f3k+UP7+NF9e7j8yhavX7ZxLEenUY6AuGKOwDiY0o0t90dYwPT9Tgki4TG7rnk+4+5UPvRNh5F/Wwe05euUKYVFVbsVcRgZLrHMkBposvGwlFaDzbVpxfe+oMnznWU/EYJW7yr2kSQXEsfa21Ts5Ang9+CRGeYqtYC+EXKQDhWVBt7t1qb3CYReXXDsS9otwA9FByFHbejqVq6Hd1C2g4qtVCPg0BJ2dnzpQxB8hxwfaQVBfKV17elzW8RKuXcXL9uXrt2wxclJf89cyT0HTX8ZUfGfHUyqiQicTQL5uZyfx2eztmeyVlwpt4VRnl9kXYf/wfqK/dVnP7KXu1tWwzupJ+cvYN5Qz+7J80DHarnWQW+/qcnwVhx2fDdvLh5W+SPDXAWIdkVABETghAnUfLLWslP4XNW79/Y+Pvb4YTga0LjP+WzYm8Iyx0db2dczHvd+bHwXBrqNbdt4/MzmGg27DqPcB/iI3IWJKZuEAYrvqfbHX92NUrAzoq22sUGz2eg3GuY4wPLH90Q83Vq3Hz24ZXefP7VlGOkuXL1sY1P4Rh4G2ATJp/9Ak7Zwi91DO/sm+mA3rMxeSiT5/NtPSTyY6YDmf/MyUniEtMvM6xcx7ekj33430qfiw/yS0kVcVVaER91a+/vln5cQHoNkJG5nmNc/OIWbx3XyB/dIn86KTimLI3FUhjkUGbU6SIfOmqXQqMtBaaKeeXvuln5Yw5mXAaWizE7l9KOH5ztATjcZh+ERug1b55BzHlweVzfMwa3hy0F8F0R9a8eWnzy1KXwE4g18ZelbX/jQLk3PdDfM5aD6OWB5evlFQAROnkB08Dp/T/5YDKABV8ptYpSmYe4VnrS5v/bK/v2tn+LjcXjMaA5999wcYmGYK0dyHmj+0tqFOPz9GOZ6q0dppcTeyZVCBERABETgSAnQMOfz02KMj7lqGgdaRcclQBoh0uOnqUdPxjk+FcZ/fLSVc3ka22iYG8P8obG5bVvPX1l9c8sWJybt61/4si3NzOLVOHWbgJDSMJeMAK1CR+A7csMcDXSrqMSD9VX7649/4ga65gSe012Y9/dE1GCN5ABbDn1Okn9aYYetZ7eJ8XFMclM9ojadNPcKeoSnioOKYN+HoYX6d1oeGTnDTdLBqSiuPbxT2ZEji/NMqVknMelvq6HHZUlRCLK2fJmcc+aNNhbM0wlewMEBcUaIDFax3xtT61j0OgNcZhz83oL3pyiOPSOiHuFr7adsUY9Oeyms/W8rf8vXnoJ7SWrIDqb70yEEYiJdx/gegbnsbv1ApAmN6UaZ4ebFdJJTlcH0Li8XUKkLo/KymKeTbIZr60wg8JJjnecEBtZJrJsbw/L09RevbBdfaF3CmyH+wde+blcxsSvveEXGqlg/aNVA7YuACIiACIyKQN79xhjIj8VtoA/nqrn7ay/ts1dP7BeP7vpHHybnZm0ckyOubWiyn/d+mn/468MwF4X0XQFqWGgZg3vfeZVQBERABERg1AT6NcyxXB8i3EU/7l15mlm71w1z/O5DYZjjPvp5fkCusb1rzbV1W3/6wqbwQYjf/uqv4pU4F2ymNmZTkBpyY9Vdv3Us8x2Q4dgMc/fWVuwvf/5D3AHbtgbeLccXuNbH+fK9tFicOhJU671WKcTh0YutOi52m7xW06XcJz/ZzetS+mmYK46U15/1xL+wxhJKGGiiXu0HFrGFsPbwqHV3t6XD/jRZs0ttue3v/vRliAsNya26lfGn1NPGFtXjMWFYCk+0yjRF9d1BIF3/FQnKdBkLxlfDPX+Wpur1+Dj41ch+9otOKU+aH/c8vOqPNlkN536rHgdLS/XrVctOJaSwyNkqr3vaSBN5ws1zME2k47kW/khb7W8inDLCH/kiL+NyP/e5MX3kSQnYD3ajul8Cuw0e+miHLvQc/wmWJMXVcvzC0kQNX1DCQdteW7U1vCtidnPX/vE3vuWDK16ikPpYZuQvR5z7EaVNBERABETgcASiTz4oVzUN92mQW8fgRvf28jP79MVDu/XiqTUnGjY5C8McVjyzw/b3BLlwdti8Yk7XGyGz44o5Jo0EnrfXHyYuMsQFeK8sihcBERABETgyAh0fZfXSOl+8p9Cs48f4wr3S3sTJFBL5KIJ+nu+qru/iIxF4R/Xqoyc2gVVzvwPD3I0FvKO6MeGGOaZ1iZyMHXLrleNYDHMr0P4uDHPf/tkPbBXL0ScuzLlhzhpYjo4JVJoUFaoWlYwpqle8qHR1XKxOlINNNV2Ed0sf8Uft5nVhWemghgcuEPDSou4Gk9ah4wUIZ+FRr1ZMIaUqmMHY8nSdkrSFIXHsp3yt3OHjMfH2WwS0p09l8m/bBVGPRhuyI7fL5J9qRCQ4IZfqhJmJ9eOPVUtqIiZgFKqn9gtjS1EPRoefVQhZLeoMbW2ZuFZg5kvx+JslZFHZbpa6szc6pTy2pVceut+f6rc/nCHUo185TB86F6gYVIbRn4dzn1vkoT/iIyz2GddpY7pIG/HME/nCH8fU00dkZChlJBIhL/L4qi0/jzuzSDogr8vFH6Rlm/JzvSjDGZbx+wpGJVh2lNzSP0t5brxBgbhomGviDhcNc5NYdt7c3LTl+49tAi9z/aNf+w27cWERd71q/snzDF9i5bxb2HK5rVD5REAEjoJAnG+dZFdOzU5JFPaaEDjoOIaKTJOnS/t7/hgrV8xtIPbOq6f2yYsHdm/lpTVxI39iZsbGp9JtlXgFThjl8hHQ5UJGa/ORISXJg1sJuviSVh4Zg3uXlAoWAREQARE4BgKYOPm/8qIgZpxlQEclUmxr1hSp2cvHEjGumOP8rUbDHOYONMyNY+7wH3zla3Zz4ZItjE/ZFMr3xVMQwG8kDLIdlOsYDXPL9v/8/fdtHS8Bmlyc93dFsELJMAcV/T//UN0Erjp+VsfF3NBRBVNNS/lVedU8x7EfOrCW3NzIA+Mkl1Jyq+Ode/w1MKF0PkxDzYuLjNQcPen+P9mFiMvHn+DAqLI5Fg2p3CccbMGz1A1hRfNnbPoHQXXqVoTA8dzpJEHOohxPzUQhjAk7bGU002JLDv5CR8YVwR53kn+oCzVyZnEsCo6uF1WmxxVOZN2LNME1GVhTOhrFou5Ml/wR4hL9T5LU2g9fyzjo5OO0KfN42ZG4i+saVIpsadUlUxbcTTeKHExOypV0b9UghYaieWzyR1ktfSIkUzbzMlekzVPSn0op5CanPH6ZCPdShh8H7uXtFe2D511oTEN7a8+zljK9CI/n05cuMSXAX8Z5Xsbj5zqz7XkmJmilTyVEiaWIc+UhFudQGObGuWKO3HC3i4a5sbUN+4Nf/gYG1yW7MD7hd8WcWPBMENuY5VFtEdoRAREYCYE4x7w3ww73000jhGDHu7+ibz7fPdxIcB+LkNYxPbi4lM5HtjSW4gBvInADt6jW0BI+f/HYPn3+wL/MugfD3Pg0nraZnLI9vpu6KIRtpOj5y8JCbhnAkSEuxFqBffgoqSgoLqb7yKUkIiACIiACR0QAfXksLEnXBDF36+cKoejPC9WYkyNQypn6e1o4arTLbG3ZyoMnmDus229/+Zcwd7hsSxPTNo3yT71hju+Yu7O6bP/XT/7WNsbMppcWbGx2xsfJMMzx4suvwEo8CV6OsDouDjTOHlE7OaxYHlS+EaN8l5xPuNE80lWoxzRRYZrrYsJORGX6DgWyefF/MvpEQ0UQJ/DFL+FNDZFsvUEm+ElipV1Tip8AfvWTIpncd1mYxzMr41hOkkm5sbV8KSQvItOSGYtc8KCQFJckVWWE7MO6XnYurFAmOemvlxhpGJTUgafY8TicuBk35on2GcYats/oPJgll9u+KpKxqWx4fPMiCl/407FPx45fkdnDlSnl1PF555wVs5U6uAwGeKjvpbSOGPtU0oOzPykgDw4dskSpPnlA4We+KKND9L6gdIRTcCsfSmShEAZTddLRg1qpWcfgy9wR05LRoWqpGF+ZVqbztlZEuKCitmzMDiEn0ZLJ8v09N8yDY8AY1wHHJXJ4KOV7W2HCtBUlYKdICcfleTSlpNrQCF7DJIT5vR+gAR/lpvz4Sw/ysi6FpFTAOfsbPPwc5HHDMRgHO7wswfYwuC7fx12v1Q37/V/6FbuJLywt4GWufI+EM0uZ004FYh51zpCquiJwLAR4jvGm1Y73p/DBrdd55nLju184AqQL4Tg9w/Uk+vPaEYh+k274uyuZxrN0ncsPPyTD3CqO/WfPHtonzx7YC3yR1fBuahrmGui7MSiinRTjLfv4bDLQKq/l8849S9Ndl2pMVoO4wKsm0b4IiIAIiMDxEUBfHnOqdC0Qf4e5Mij6+lozfUQOc4i9bcwdHj6xxsqa/daXvmpvL162ixMzZ8cwd3v1lf3Fj//GNscaNndpyeqzWI4OhjQu+JZNXmM4zaaf5QHPx8aBxtlS0sl6xqA8bJTWwDy7wVVyWN3BuTsvTnb8IhXWWu4jzQ4e/+Vck02u4RN8+tq3CKHLi1j+Syt1KJNGnHTB61KQiBfCvAB21qVhB5ldflV2mvTzRKARiJmY1138YbgbDmhhRlmU7TkKWXEcmR4R/J/ii2K4H7JcH+57Oo9BVDJQMniYLcqhjCQ5eVLtQieWBi0InonwC/bREXjdEUVWTFLqzDAyDa6ITHmYKjF3YchANM6tpYmn4Z9cnuvC9Ah3Yx4uSJ0xWePnbYeGm0IT6kpv6FDUJgn1SJYQZbvHdWFYGe3qUqJ7CnkUkWu2f58yihytvAzE1p4zhaW/iPH/qc0wvzebLIMbpxgIBct2S2URxEkbOab6JlkeXmgS+uQl0h/ivZaeKKV0Xh4JXwBxhVgcdIyMhRB+zYcK+7GkHvzHfEU6So04SigjGO4SKai1eX5kbnJQwL9GHf0Cjy9+cdz3eD5neqR2lGREcKpNS+558AV3P05gy6k9jW/lXS83zP2qvc0Vc5jcjSPOOeXQKuDyqPPAUHUUgeMmwKEWVzi22dy1TVwI7+zs2iQeV+RTA/4iZlwboRfk7Rk/X+MUDfe49VV5vQlEv0k3/N1zcaRDP41BjW4Y5lbQKj56fM8+fnLf1vDSn/rkhI2hXdSx2tkNc2gNMQ7y6iEK6lreQBMGSisktg3+3WujGBEQAREQgSMkcBSGOe/f2dcXhjkOLtvbbpirr6zab3z4FXsHhrlLk/gyK8o/9Svm+I65Oyuv7H//4V/bzuSYzV+5bPWZSR/uOMnk5kaAYgJcDIPlxLg6njq/1jjs+U/bnzFUewxf/dhZXrPxMXyldhIrOMbHrYmjvQ2Dy8rWBu4MjuM3YavwcxvDF2xpguH/4g89aZceNCRGMY2vpMKFLWXv4kJ3ewcfoWd8McnfxUXwLvaddbQwyqCRgW7bX8pkCP4hnjIpawfvcdrc2vRHb+vQjTJ5ccWsdRoU8I/yUynwMQpxLodFMLLYwgji6T0NIgpdGJZaSciKXIdz47oq1a+V1w1e0Iou9aDBq7WSs5Wadc9UbgmAz9towTPqwgRuyIRs8kqGA0gohCSDDatJuanSVfmRh7o13HjLTzvj2IF9E8/A87FntgsacJLBpjimLJJ6UYlcrzCEUzfI9APCNNlWnm+I5z9uSVYh2/dD0xTPFOFLR9iz+Z9IGSGlFET4+e8J0iPSlOG/LBPrSEbceGy87nDreEfl2Ng4XLQ98KBc/4e8LsP192xFBZJQMk3skaqQ6/V0P44/ktEE6G25yE7HNaBs5C83ehkBg7UfA3hprPP2w0Qh33VhwsQpyEZbCc5ectFWXE/U0c8l5N9l3Snb/1FU0sPZIN73GMZqJW2pwfnZWHXWnS5+7IH4EtcmB1c+yrq6jq+yfiMZ5tDXyjB3fpqGavr6EtiFatvovTYwrj1+9sxe4lrx4uVLNoNHFicb+AIavoJGIzt7z+rv9a3V+dYM3a5vdMNfBHVwOKKhn+a4h3/JMGe2jFbx4zuf2U/vf271+Rkbm5mCYW66MMxxBXkS5WXwoiUrKPN6omg9Md52UKJLkEtPcXEB2SWlgkVABERABI6BAPr7mBPymiBdGfBv2htIA+/f2d+3DHM1zCuXHzy22vKqfeuDL9m7S1fs8uQc3jGHKV9R7Kl7xxxMQVjtZbaGStzBy1v/t+//le1OTdji9StmcDlF5cTcUbb+tMZXH6hZe2yIJ7Kzsk3ganT72Uv7wbe/g5cLPsYXQHb8bmBtvGHNMXxJEBP9t7/2NXv7q1/ByjlyQu35IzXs017Li5hohnTdYIAkPjHFvhtzYLChMYMvQ/f8XPEGgx3zckWeMw2XMjizhVT+5Zb2kuv5kYF3spmTcrd3cISpD4wnNBzw+oh+GlMYzo1leUlwqFNc37hbFEQjUyoMdQcLf88eLspdz1IGjQ+uMcUeegsuXmQpBgwLP103buGKj2lqMIpwRRbr6fojtAZ+YQihy+PC7PwV1U0GHT9eKQw5vAzK5koocmzAmJTkpLzM7PJcFtPAQz/SUi9yi0dW/XjSQIPwMXCmQYi8aJyi4SYMSklX7z6QlmUzT7QZatW+Rb1oYGXxUVf63TCKDJE71TqXwVTc8rAUEjEpNlWtVTZimaDQy48F9qMtU0muCnTdkIztxA3AyMQ619FG6Lq5CjL8PEnFprbGg+IKJI5FVDoeLSWotteN9eK/OOJMz2SslbuhK+HHxjKKH1e18TzgMWAQ/1B36pgkQG/4eFTooqiy7mTgUtFGYpLihnRvhzwviuPLVJDpWQt/KqxQiGUW3vPmsN6Ja/FlJULCjYlXNMwtr+Mdc1/3d8wtwDCnR1nPW+tQfV9HArxG5Bc4+cL/u08e2oMnj6yB1VGX8cj51YVFfMClAeNcA4+mpz4vneM8z7W9rgR8HINyaYzqpWUa99KYx7bAR1n37BUMcz/8/GP7yd3PbHzxgo3PThcr5vgoazLMeTn449clUWjH4thq8IsxtmOaToFZDeJCsVMyhYmACIiACBwPgWM0zK3gUVYa5r75hS9ixdwVuzLVMszR3tE29zpE7Zm121ZbX18/cDjrlrFXOC+2csPcXdwF/V/+9jtmeIR16fo125scL8bJ/cW7EYAFVAfRg2rSS6HXLH4CgJbv3Lf/4b/6r+3Tv/6ubT19bmMXcFdwesImwGgad4x/+8//lf3mn/yJTeF9fDS4bG9x1Vt65wqNFX45A0YtYwaQ+n5mhHBjjMNE1mLiD+NSHY8U78HlBY03roJPGEC4S9y8+PVpPg1KNDjAOOBGNOT0GCTyj1TQRQisIW6Yo9vWYIvD7PIKP1e0cKNWOzDw0QhBWZvbWIWHVXlcQciVR7yj7kmZvMhLJ+VmZB8bMkA0VtAUQgo5Lg9MYU2xPfzCqMVHhqkPM9Go5Rv8vnqpqBtZ0RBEoxA3N6JAqyYNWwhnMEvD907gRy1ZFxgJWMYkVkPSqERO3KcRzMvypEjL/NwK1w2a2KU8f8yReeDn6kXXE/5dMKQs/viPK8nCAOjGIsRTapiI3ODItIWe1IF4dt1IivrDIMRCqAtzejmZnhDV2ijEN+SjYtkWMQxCbDmhimTuUg+Wi3aW2jDKZXsF+3iUNfFOtNlOnHemD+vsFUwFeVmUHWWSYYoHIz/WkIVyazSSFnJ2eL4UQrwMVw46szyIL9sv64tfYk2x1DW5ae2eF4zCudIRxNGWQgYTso5s/6ltIJlnZjiV51ZoQZ39vMCEBcdvB23Lj8VY0TYRz2OWzlt6kJW6uozz+cf7FWcMdODJryutwDDXeLVmf/A1GOYW+fEHGebOZ+tQrV8XAtHV8TrRjTEIuPPkgX127449w43cN65cs/feeNPm8fjifGPCpniTEWnZtzWKX8d+LgR3jERGbUdOIA5BuAcVGGNnjLzJMMcVc1v2g1sf2Y/v3rKJJXwwbnbWGlOT6R1zaAWUzfGXLQIjJ4fMAzbG83dgog75mb7I0xqcO6RTkAiIgAiIwLEQSJMeL8qHec6TRrHh/XLcGhgn6ph/1jEf5FdZa69W7dfe+8DeWbpaGubKIktP/wr00vZYDHP8+AMNc//z975ttblpu/jG9XbDXGWsLMwgPoi2RfWqTf9cTjYlKjUFa9Py53ftv/8v/ku79Xd/a7vrazaFd+/VMJNswGh58YMv2Lf+9E/tW3/4h34xklYKYUoOOGwHnOSTk3/WFx43GNCowcYEQwBXUtEAs40Xn9OYRmPLFj79i0h/RHYMd6RpeOFqPK6cI1pO7h0xoaNh0jgVRhzKbcKoRAPa5np6tLaB/DQuNfC1rDp+btTBRQwfkS0EurIQ49c2vK6hl5Nlyp2AUYnGIcr0RxFpHIHeMF25y4rC1IQc2FxI8lK92Fzf2OnmFhl4MV+aLHjiYX+Mj4DSSIO67YETDTVuEIIBZHcnGW7GwWochsxGGMGgCy8I/bFC1pUb9eN/7LJO1IvHyL+uC/k0mu3g65BbOB6s7yQe05nGHeBJvMw4Lkjp0qhD9m6YK0Rz34vwqKRrrBwbx6OcLIwpvDzoSGOc56A+0IFGITdEoX7Uyx99RRthe/DHQqEf67yLlYo8dmwb43yMGnWmoYo/6kVjbhjnKN91pWLcggNKIIp8S1GukZdPhb29IhGPR7RdMgrjHNuvP4INXnRpiKY+rA+Ntt7mMn1orORqNefmch2Lg4njwHKQyI8Py9qkbOxPTeNYzPBrb5O2iUe+t8BhBz/XuPjD4+hyoO0YmVIUuG2C4Rbei8TzawzMxvDoOXXzYwlu1ImCeExYB5bPdrDNcxFRWByLH+IKDnRbRuF0XHkGkPUW2uQ6Hsvc3N22GurOVSUNvBDbzxBCp1LUrfhh71xuZMiN52IaXPds+R5W4byEYQ4r5t4uDHN8zNVT+jFmhuLHzMWWR0WYXBEQgeEJ8Nzij4Y5folzDe7txw/s43u37O7DBzaNMfLNy1ftw3fetUszczbTwJiENOn6ATelcL6mMz3JQVTy+EmLayE/nyOFx+rPMRHwQ9BHWSkdx24Oh+lmVMswt10Y5rBirjDMjckw1wdVJREBERCBM0ygaphjVX3AH6bOHI3SiETDnC+egMsnGmsvV+wb731o7y7CMDedVsxFcVzAwa16pVHd90R9/jkWwxzfMeeGub+BYW5+2i61GeagaTmpT4NzqTvyJUwIGaaWpcDXwIMKsSpTXDF36679d//2P7dHd277e/d++Te+ZTMLc1wGZeOXl+ytL35oN957z1e2+UVLySkZBlib0jAHuZxociJKIxosBZj8b9mrFy/s/mef2f3Pb8EgsGlX33/f3vzSF21+cdHG8TgxVwvRwOQGDzc+gPluapTjMFqxcdKQ8PTxE3v8AJ+tv3/f1vEeGFhLYDDEnezrb9gi3hm4eOmizc7N41GDCTeccDWeVxSNlrJ55eUNHWX4xTX2m1s79ujxI7t35469uHsXUCbtAlZTvoN6z8yDAwx/vFSDOtjwJ86EtMfA3s3C83pSL9ebEcLi4p4Glx2sRNxcXbNnT5/ac9TzxdPHto2XPe7yvXwoYRzvNZlZuGAXLl6y62i78wsLNglDzg64+eO7YOSrt1gW/DQM0BhK48urly/t2ZNn+D22tSdPwBJGTXCduYRn1a/hJMfvAuRNQR4NOvwynV+esrpkCHeH78h6+cqePcf7d54+s9Xnz21zedl2IYsGn/rUjE1emLOli5ft8tUrtrh00SZ4HHj8IMMNczQSwRjF+u/B4LiFvM8h6ynrjOO5tbxi2xvrMNDt2gSO48zigs2ijSxevJj0m5tJxwPHhMZdNzw5VlYaW+GQV3aYUlz8Rd0iHfm7oRZhpWFue8dWUC/q9fzxY9TzBer50nbW18EGL37GHfOJuTm7dPWaXUU7WYJu42gzLJCGq2TMTIrQ8OuGOBaJY7GFY7zy6iXq+9xePHtiGy9ewsC8hmMMI+n0jC3gOCzh2F68fBnHli+YxmpNyCWPGhqgG7pdFlaubuC8gqxnT57akwd3bQ3s2FbGMZGcu4wvfl7FccWEksa+ZKTjKd2ADBjRoMs9tPXbn3xiy3D3NreTIY7cID/pjaPE44Z/rJN/9AFtjV9p3qFBDitoF2++ZdfefsuPdw26cmN75GpNz9n1IMTBOLtu1TCXVsw9srFX6/aPvv4te+vCBZujoRS8oz9wGtzxgBYbJPGtEtxKIJ8IiMBABHhu8cdRdh393Ar2Podh7qN7n9ttGOY4li7Nztm7N960m1eu29X5RZvGCco+ktcQEzgp205ZCEOW9Cfi2A8ykbZjJ8BD0c/G6x1eshZXPlgnVzNciaA97NgPPv8FVszBMLfYY8UcJ2oHboj3tnFgokpk1KDI2KuISm7tioAIiIAIHBUBdsic7Yxoc0Gpz6ddhT8ucVnlo6wvl/cZ5ji/5LDTyTA3rE5HbpjbRj1XUYFkmPt3MMzN+Io5w6owH0t9zEswuPIjVry0oR62lm3Cjm+HtWpTHQGxPw3D3KvP7th/+2/+rS2vrdrNb/yq/el//GfOpjmBlUoNNDi8bw6WF398LRmImRs/OgmZL7nk6svSKAfjxvbapq3BkPMchoPHd+/Zj777l/bT7/w7GAW27Vv/4Z/Zt/75P7OrN96wCRgOuPImDHNjNMxROIwHnMxO4g71GgxUTx4+tI9/+jP76Kc/sbs//6mtPrgPo9CmjeMu9tUvf8WuvvOO3YCh4I333rMr12G4urhYvr/OGdDAhCsvGkj4KGkTRpItfH54Gfr9/Kc/tb///t/a7e/9e18x+Navf9P+4X/0L11OHSuQWG//SAWMDt0tPl2OacGIsUTGjZzILhkHoRYMM8vPXrjR8fann9qtj35hjz7+CMbH5/7SeBouuQpq7spVu/j2O/aVX/ol+wIMm1ffesv28Dihv/cLxpAxGNVoQazBADYFbqsw1jzG5OLTn//CPoMR5uFnn9qru3fcMDc2OW0LN2/alZtv203IfOcL79v1Gzds4eKS7cD44mpjQkGj2+bGhhv3bn8C3T79BEbWT+0FDJlrMKZtr66gMjWbwgqg+auYuEDWux9iue0XvmDXIG8CRit/Tx7kkH0TbWMXv1eo2yMYWO99/rl9fuuWPYa81ccP3Ti3hxdwU94cDEsLaCPvvPe+vYf63njnJlZoQR5WaO3BOMc2U6wpK89jZ1wYhII3mXubooezJhpk2eHByzQ0JO9hRdwujL80yj28c9duffKxffqTv7dXaGc0Am/hsaYxfEVz4sKizUKv6x98YB9+5cv2JRyL+aVFXznm70ukfPxYHtuyr2xDs3kB4/QjHIu7rC+M1A9hpF5G/TdXVsBkyx+NWXr7bbsOg/CHkHnj3XdsCYY6N87xXIB+kzCq7aGtrC+v2f3bt+02ZN2+9ak9+Ohntg7jK1dcctXowvUbduW9d+2DL33Z3sR5cRFGOp5jE3x0Eiv9uIL1Rz/4oX3v29+223/1HTcQss/bQ0PnasG0WpMtIBH0VZxoX3tNvhwAq/vQRi5C/oe/93v2xW983d5F25nAij/2nVyJ6R95gb/XuZIfHyY/SxuHajf2AqPfqABXrpibWNmwf/Lrv2lvzl8wmJndOO8ciJsbdypgWkfCU+iPCIjAiAjw3OKPK6RomHuF2yufuWHutn2O98xto2/mNcgSDOnvX3/T3r16Ay9exipz3HDiF+0nOUbCjdOW3T87wmIERReIngCRPiTpRAac49+Ivdfm4x+OGsew1B5qeMccH2XFijm+Y+7epzaGG9bjMzPWwFjHD6LxqsIPN8dOFsALxWrnzfDYQhFPF4EHucxQZMI12fFurMtxl3m8Ney7tKPEQMxHvQ2q/1Hq1q9OR6nDUXM/zfJ5fMS+zyPoyy/6TNsrWZwYNMhxfoAFGeiHaZgzGOZ+7d0P06OsxYo5LxnHKQxzufRhD9+RG+biEYXcMHfpBh5lhdHFGx9Z+BWVX1MBAwKCD2s6bA1zWsfo71gFBLI6XC00CSPOq0/v2H/zn/0b28DE+wu//Vv2z//sX2K1zWU3ym3zqNOghcffkgGkuADxq8xUEa5Ao1GOF6l4Y5kbu7jK6MXDp/bRj39sP/+779uLW7cxKb1r6wivweD3D//T/8R+71/9uS3C8GDjMCzR4IUyeO1BvdyYga/F1rCqahIy//Z737Nv/8Vf2MMf/dC2YCTCc5A4Plhhhzw8bDs0DmG10CxWk335H/+R/frv/5596atftbWdTf9qK5L4I6A0MND4sgaD3L3PbtlnMLx8/nc/tCcwDK08gpEEL1ece/tNe+c3v2l/9K//tV29+aavhuLFWrkaKlW7v78suNiiCfFEi2usMRpCaJR7/tJ+9Nd/ZT/8y7/E11ce2gZXjkFPPraJSnrbbMKYwvfDNfDYKOv523/8x/bNf/SHNnNxwVcp8eu0vnIL3OowfM2NT9ov/v6n9u+/8137+Xe+bS9h9GrCsDJxYcGNfVzpRuYNfG1uGpOOt772K/at3/kd++qv/ort4tlGPqrJL47SgPU5DEA/++GP7KO/+v/sGQxKazAyUS9+nayBFVrOFeVSxyaMpYswKr33a79u/+Sf/YldwCrGPZRDAycNhjvrWMEHo9z3v/td+97//Rf2/JNP/fFViwtdrIKkobKO1WlkxonNOPxf/d3ftV/+/d+3d95/z6a4ohPGuU20WRoRmZDHiJvn8b/J74FIwqbsqybZYAoDLU3A44XxbO3Vsj26d9/+7m++Z3d+8mN7CgMpVzDWYYwbm570uu3ysVO8e5BtiI8WX333XfudP/tz+8IvfRUr6K76I898tJSNcgITN37NbwLyeX58F8f3//0//w/U92PIxUpIN3QxKVcRsl2uQ2s+wjtp8zgvvv5Hf+T1ncWj5fxAQAN8Z3HsN1fW7S50+1//p//RHsDdwgpDng8+YQDDTbad5VdYArKFFZGX7Zv/9I/tG3/wB77qkKvn+M6/FZT/0Ucf2U++/3d2B4+wb8AY6aviqAfaTnMbxjka6FAuN65epbG0iS8zbzx/atOXFu0azq9f/xf/wt6DcfLStSu2C/BF8tJNuQ/+y+N15jaASF0kJu30AyT7shUY5sbx8Yd/+s3f6mmYI8szyeYIDnYxKo1EspiPBONrLqR1lHme8ReGuZd72/YJPv7ws/u37bOnGIvR5/P1AdNY0XwB49D1uUX7+vsf2sXpWf8YxAz6d45UfhHNWqOvx/DSdvL+/+y995cdx5XneVEeqIL33hMkPGEJgiQMCXpPkRJFirLdM3vmzJ7ZM79uz87+C3u2z7ZGLUuJFCl6IxoQ3nsPEN57Xyhv9/O9kfHqvYeCIYwaACuBepkZGXHjxo3IyMhvXqMllDbN7xkXPPU6f5qacp2Evp/F1SN60PnzT4f6R39LY07AnGvMHQzAXG7bYqKysubBSkMfSrUGUvlMH3NOkdTmNnXWd+0w0bsczebquda0Jt70/IrP/2ul9o/j+9o5vGJJF32TXK6Y/z88Awyns3udQyeuYy7VrGt+9l7t4AoMZFWf3sCsS9d0eqPpRSYS4d9o8hl9mnESK76+fTpJ8e7885Oefn01NI3R66R5JdFe8/i83vZdd3nhBLwPIh9hK+5j7hzAHKas7mMOYK6Qa3qv1Ra+CQXNvSjSK8kmlLz0700F5rTgqoHTlMbc2sWWg0lcNjAXHq6Bydu3MzOFHDtIqalOIlGLSOEZhXWNdh6Nud/8j/9pVWQQMPfY889a+66drR5Qps5Xm3Q9K0vRUufHfaQXwbRcVqMyt6vGDvr04aO2ddVq27Vuox3eutUqCfXbWKUA9DjRJ6jEo7/8hU3/0cuYoHa1BmhXSxNH4A05clnYEkrAcniRra/A1PH4SVs+d66tmP015nvV1gtNpX7DhgAmtUUDqBAT0Bo7smu3Hdi62c4DrnUfOcYmPfaYjb3/fitEM1KLLJnh5bOQ0kCvrqgMQNOyFbZt4WI7uX130NIC2JKWXttBfa3/lEn22Gs/RpsMYA6gRCaTajesJUccaFNiFIQnpP3oWmrjhP/KqjamA3NVaANu3rDBtixaBEi43ooBU7ribLpH9+6YlmI2KYAKvisAT/bupp1oDZafOGHDHnzAxj72iI0YP47FYpFrj8lXXCuAlcYKXiYwT1y9cKEt/PLvgGkHrEvvXtb3rrusPaaSrQGbGjFNPXn8hO3bstmOQbddrz426eGHbfy0B61rHzTdoCVNvNNnTjuAs2XFCjux/VtMLIu5d3pbr959MPXt4FpkAubK0Y48dfCgt6OWxna/e5g9iH/CwSPusc5oMOZIyw2wpxpQ9OjBQ7YaoHXTwvku376Dh1jfgYPQ+CtwUE5my/JxdgRNy/07d9gZtPS60O/33D/F7oPHLn16+gK5oqEWYE4CxYwUoUqufpp0SqprSHe56yGvBQF8yGeczKQFnmmY70GrcOPqNbZz+Uo00s7T/jbWb8BA64SWYuu2bWlRA+llmBifxgR0t5s9S4Ns8H1TbML06XbPuHutEH99HlgDYLGIsSktN4Gshw7Q3iWLbOPSxVbHGO5JW2Uq3R5zX0WzlSlrZdkFO4w248njR9BSzbdR+HW8F4C5NX5t8hh4+aj9ts8rtIMAyutXrrSF77yLFmuddRkwwAYPv8faM27yAO4qS0ttH/20D1C8/OQJ68/4GD1thk146AFMZDu72XgFYN5ZwNVTjKNTR4+gQYm/OGTggDj3sOSjP5ljSvNPWo77Dx6wPdu32akd26zT4IE2aMoUmzjrEesqrUjGXy3AHSV5V9FcATURbGbLSKZAxnkz+W/LJAmCTUCw+4lAnNL+dY257wDMOQ2n1PJzOQmE2TkR+uUyXuW1O3JMXmXb7/xs6t3Qwxox8S8Cc6WNdbbr9DHbfuyQ7T9/miBQfBAjuzSN81gvtSMAxPA+A6xvZ1wF4G6hfat8Pr7w8QhaAuFTBDlMTYOUb6pVF27gFppyAwl+/0gFbfG4xqMT6c9qekzAHJ/QbDPA3Oaje1m7EhiNZ10uHyTlA9eBObJrfagelsZ5GACXkKFf96yXyHCpZNG/2VvQ7LyRtUiuN3+7gsyvhwFn/3a8wWD8GsTiLb2ouRclXCTRa3pfZmxcaXQEfrLrv8HjNN6TF7XqxiQ0xpe9G0Mug4okE26xK0kyo9gVT6LEU1SZD7XF9CsSuEKGFN3roZlO5Ir1fYfMV6D1j7ksfsP7rNYUWltUEPzBEmBuAMEfugiY472C1y3fNIwdlmMfW3u9/fUPA+YOlZ2zL9cuJfhDG+sijbnCfAdbvCXJQyQ0KjYtNPp2/fVWqHc4iJ2kecJ9a5FcJGBu70H79b8ImGu0gQAfs559CmCui2tNNQDOuRkioJloqfPDX0BmJZdIL1dgBy/4NQBzJ9CQW/DpZ3Zs23arAlir42W0tqLCndnn4t/vsV/+3Ka98rK160k9AubQkpI/ONeWoyJp3uUCzJWfOW/rV6+2tbNn265VK6zLMLR0ACzG3TfJStDaKQTIkQbWnh27AKHm2eZFC5moGuwegJJxADiDhw1DoyvfzVDz2MvBfRXaQls2b7HNgH075syz+nOVPgak8VMLiNJ2YB8H5h7/MRpzAFQ5AHryq6X2cx+w11HY1HY/i8KNF5qyJCnhJtOJA0Q6gJiAjzL8jM0HdNy/ahXqqset/6T70VobY3cNHmzFgENFtFFyKS8rs7UAR8vmzbd9ixZbUY8u1n/qffY4cuyEr7NcTFr1FdcBTeR9CjBo6Ref2wr+2nTqinbdwzbuwaloQ3a19rxQCHA5iPbbiq9n28ovvwBAKrdhaMxNePQRGw9AW9y+rVWgVbcfE9PNaMptBwyqxdy5//DRdveECTYUMK2tIpRhXokqnmv9HQDc+vzNP9uR3Ts8WMigBx+0+wFvRk+cYI2YRCuwQyVA5GF4+3b1SkDGb63zoME2eux4Gw6Qlw8gVQ9gWF9d55PRNq6vXLLUtsz5BpCw1noCQD39xhvWd9hQa92hnVUCTLmmFmNHwJw2tSs55CjZuJQNzMnkMh9QrhAwS6CJNOVWAhYeFzjapbv1Gz3KJo6fhD+/7laM+YoW3+XI6CR9tFWaoGvW2JFtm10TcOpzz9sDTz9tXQA95V9NwGIbNAkrSstcM3MJfbZ7/VorPXoYgHEo/TDNxk2ZbF3xU6cgGPLfV45cdgCQ7ty3C1PQOhuEJtoQQM0cXgTyGSuF3A/FDTm2ZsVKm//F320vZqhd777bRk6bZpPuu8+6APIpiEgV99nGjRuR2xLbCxAo4Lsv4+mVf/5nN4+VRp4CN8QhWlUDYM7ch5dAZMHLJ1JzLS/qkzZhDrKppN3LViy3BfPmAM5usQH05+hHH7WR4++1opJi19isAZiTwF2LNcr9Evv0fkkdXyLv7ZysWVLAnFRocgA8M4C5EkxZaXyci1MdIoHwF/vnTpbPjerbpjk5Su36KLfI/Prkd2uXTm4wmNRo0TNdG7NgMGVlHttJVNZvjx+ywxXnCXbD2kRFmEcbq2qtoKbBuqM1Nwh3AQO79rSumLW2kWY0N20BBPWcEeFULRyonoSErt7YTYRbtuuSQDowp7lEH3MzgLlDe2wrwBwLMrTnE2BOGnNaF9O5/uHWB8kV2Ih5vnOfaQTdzI0nVQZPrMU5T1dWuJba/9HAnFtEJIyqX5qeotfCvYpLKPq72fK/FH/UrerTt8u2K+ZXpsBz6h0lncYl2pMuv5hd6+nLbT5/Xj5L8+ARHXQlqTrZzIEJK9lj9XLcXekaNVyJiSuRuML1cMvfnEqi2Ju7z5rryyuwGi5nsRqnLF2M9V0VnStkitVk04zp6cWz8/i15jKmF0o7vvJIS8t8SxyqcZnAXLrGnIA5BX8o4Obz9UYUkO4V/kfRxORrbVKrioqKSOtaaTRbTh6R0jXmDuEnSsBcTgnBH/Bd1VAIqCDuXQ6BhTih3xSGmuXy5iV6s9RXHMRO0ougvwyyL+JlMQXM0cMDAXpmPftMAObwL1fPn57QfpPT6bpJw1+gpl/95WiA8ALfAKDSgJZbKeju6sVLMQ895ZpbOSxoBcIcwjdcTusCm/Xzn9qMV14iyEI3gDmzKsxQBbCILy1uCwT24Nz+5JFj9uWnn9ouTDwr0f4Z9ejjaEzNtOFjx+CAnoJoJDUSvKEeLaTFixfZki++sDOAQl3vGob/qwds+iOP4P+rPd88A9/5AA8y1TuwD99c27fbyd37Lb+63gMPHD9yxM7s22sFnTDrxG/WE6/9CM2x3h491hde8AdrqUHPIW1XSvjVkV8NB34WfyQjbdr7sWxdGGg6rgFY3Ltnj2thVWJOOWbcBOuOtlxbNNMEtIQgGg1uGnkSHrdt3GSzP/jIzvFFv1PfXvbor35pAwBx2hEYolKAEC8JdQAp87/62jYvWGin9u6ywZOm2JSZMwFSxvoLSEE+gT3I14gsNq1Z5xqJ2wH72qIheA+aVY8/87R1QLtKOlBVgEb78Yl2aP8+BxL79h/gmmSFmHfmMBgEOEjzTECq+n3OF1/a+uXL7PCenVYCCCjNw4fwJ1hPf9UCzCkAQg18lqOVVon2onwMluBc29sLECyASOBQIaDQScx6twEyff6HP1rp2VPWeeBAm/6Dl23AiLutY7cuVouqW4MWxxoK4cZ1mTo4F2XNXt2U0QecKxKs8slMSVpuClBygkAIOzZssk6dO9vgIfi064kPRC3AvbswnabfGpDHeUxxlyHb5bO/tAtoko17/Em7D03TwWgk5knTlDEpGR/HNPbbDRtt8Ufqr6P4futuM19+BcBthHXhWP7gZMaqP/EnWVfVS0ODe45rOXyVz+N+wXjGcqsISoHm6WLku+DTj62moswmUe/9/PXp3Zt3BEW9pV78IZ0+fcp2cr99/cGHALR7PDDK0//0z2jWDcf3Yker1cumVt6Ucd+O1O3zAly42SySkQmu+rYGc969u/fYsrmzbe2iBZaPpuo4NFInPfKwde3Nxw3a6/evxEw/xAUBIrvkFvsivV8umfk2vRDaqI8NgHIJMFcqH3MXquzxCfiYA5hrrS6IYzMKTAX5Sz+9TUXwD2Q7fekVJXft1Ye+u/byLSVvZQkkNxgsaqTEPwFzFdyr55gbHZg7cdiOV1ywOua0HD4q6QNlbXmV1ZdX+nzcpU1b602go2G9+1knAh+V8NHPzVohT5HwcUjEOdfLq2rVlHvDt5tB84YzeWsTvDww12BbAOa2HN1njcVEIMeSIB9T1lysDoRmadkRZh91RFgLXrK18S33O/eZBtLN2VhpqBlpWzxXIi1rtupmE9NohEOX60WpNy4hsB34FNW49og1NPEe+L06rmNp9Wb4F+h819JNdK7mKJu6atb/Zjcypz/xlOfi/OH9QjmT/wkppWfXllzSujM5TO0yB0cqORwot6hdgl6SO8ovIxeJGedZlHXqvKTVH3iDx4uYbKbwVSVB6EpMXBWdS2cKt/zNqSTKw0dDVhXXLKMsOuoEfx9RE7OvXbrZl7+S3n9OMxC+FPn07CnCl8qcytB0oJH2HbI3FfwPOxK3vDewk5IEaIibshpKT+MGDLEIzGWbssb5L7a1Wbl9hzb944E5j8r6PQLm6IyoReQv4PSc9gLmzu1JNOYAOQZNRWPuOYA5QI86AQy+wqQsd7n+AiiXdqOKLn/BuTkE8YPVCKBWh7nieSKL1pUScRKfWBUENtgAWLNu6SI3dZv109dt+stNwFwl5iICgVxtk0rk5L4GcOkwgMDHf/6LHd2xw9qgtfTQiy/aiAnjrffAAVZLA2QaKd9Nebz4bsNkdgXmoJu/+sLy0OLpNxFz1BdesB79+rqpXRV1eBvQ7jqPlloNaqGNLLT1BfzooUO2fdtW27xsGahgrvUaPcKefP01NOYA5gBvI7AXBzxN9k1tj2stXfMvF2mZ/MZI7g7t/LEXMiqzy66eNijgQCk8ySl/T8w+i1noC+Bx8dNHAj7boNmlYBr70Q788K/v2JGd26wIs+DJr/7QRk0gyiPtLCdyaB75LxBa+dO337F969aCWNXZ/c88Z2PQMuw7ZDC+BImKCjMC09qwuDyKduOmVattNjRrAEj70PZnfvSq9STQQjCRhR7aemWYWmrr0L6DtSNaqr6mCtiT/NvkF4HeN1olMt22boMt/uZrW7NwnpubPvHTn9mMl15gsKENR711aJTpRaWWsvKzVogGl8xWZa4roCz4ZQOeg678vu2n79/99a/t5MH91qF3T3wT/siG4gdPoFA12g0BmGvlWlviLxmyqfGeSF2XfIvXPcABY059pj4QYFuD/M6dPG1FgHHtaWeJawQy7mFY/v0KALLkF7CusspWMFYWoml4hIAhAyZPBpx7wsZNnEhUYFz6Q0+N3IFJ6VpA5Y2AaTlobPYfO86eePll69m3j+Xz5V0RTKXhqbZikFotAABAAElEQVT7l03kIA03BZGQBqD2CvxRyEEjWpAH0EBd9uVXtmbu1wB2bezRH75qM/Hz1pp7Q0w20AfivRqw9whmxV99/pntgr86/DLe//IPbNQkxsDAAW46LmBOf7rvNC4lF80Jap+CVrQGuK0BOD0FOL50/gLbunoVsjlqd6FVOeaBqTZ01CheUgq8T2vpR9dchF9tPs45TrsVwoXkN8nmGVLHGTlu/5PQrhZg7mb3pMYYI9mraRpvTUfXUv+dOiavRRZ3Xhn1buhhjRI9i7TXB1wBc2cEzDHPbePD1+Gyszwo0apGa7lAmsbMh9Wl5b7eKOArRPuCIhvcAw17ggH1xK1DW+gqSqtMTzSXirB2qk7PXNWqvxu63XCCN5S724LY5YC5CkbI5sMAc0f2WQPAnDTm8tGSVBAqdSpDhj5WL2sWukJnxMXiFbJdLDQfRRcnX2eK2Igvc4EUT24SY5ralozgcDmehQuptEsd/EcDc5Gv5toRr11u7+uY2KepflZPS0iXK/ndrl2qd2M/ZFPz94zsRM4z82s0OtPJ+IwFLlVb0iQNgLQtnGampV3mUK6ClHJpun41a8yEIpcvo8GYXXNmG0X5ejZRz67heug1X9bnh/gC3nyWa0pNSSdLttdELCmULQ2f00jMTr+eOlRWU2EUSfr9GafISD/miedxf0l+GFLZo0ryz06LdG7NvbjNBOak5NSqBZi73Tqy+eHl3ZvcVHGAa8EY/6Ip67/9C6asvJkPfvB+m/W8NOa6un85D/6g8j5BMs1zLJo+OcY7gwRpEqXAObSF8gBV2uOHoxVaPtUATicPHbFF38y2RV996VFZHwH0muHAHPVAp1IO9aET7amLAObOA5Ds3rTFPv/97zxYQLfBg+xJQJl+dw0l+EF7q6GcwASZ+Umz5zT+snZs3mx///d/xyyw1M0eH33tNbSERlj7Lh3tQiUgIYCJABZpSBWjWdSWwAeFOLrfv3cfpoyrbd7nn1sNGku9Rg23p37yOsEf+uBLJN8DX6jd6ZOGNz95iPk13fyaYXSSbOkPNeV3uSV5vHzM7ARCWdHQNWlyCaiSBlM+FSsKXK0c/+Nj7D1AtENbNylYrt3zxOM2EZPRYZg1VgLA1GASeRKNwI/+8Ac7vms3be9kT7/2ExtMm2TGWs1AkAzQx7J2+EypPFdm+9Cueuc3v7Pj+/dYR0y8n/zpT23QcLTwCNwgE0VpY+UA1ghI07HMFeV/Tr7HcjCHLgHgk/+deoDOU/T13M8+tTkff+Bg1Kyf/ARA9XnLByxFBRDgiZch2ubl4aO1orYyfqQ1pv5U3+ejfdAorTqAyIM7d9n7v/2tnYS3DvD2EG25a9xYIvr2sHLA1ka0OqXNkK4xp5WCy9v7IfX4Ss6a+kHgojTGBGipjGQtc06ND5m65gJOCTQUX5JZEX1QCG+t0ABdzXhZOGcOJqULMSm9x0bOnGFTMSvtSIRW9ZuAvtX45VuMqfDhVSvR4rzLzU4fnD7do7gKFJNfNsk1F3nKHFiRf9E5JR0eaZcCNXBoeQDe1Zh1b1i6wtYvmG97N6y1tvh2e5z7YTqAoBYwyqidzIFBP/GFd8aWLlrsATaO7t5lQ/BXNwVz5pFogxKiw4NmCJRTnZIVrfJ2yoxcY6M1QPB5Ihbv3LbNvnj3XTuDGW4HNDlnvvKKDcSkuAPjSgFjIiAnWuLVifkOqoEtpWZsqs838RyP77B9aBcyRb6X0piTKavmZc/rwkMIOuEv/fQOE80Na06UUZO0RLop9Xoq8j65HgItZW9RCSQ3GNxppERgLpqynmFO23HyCEDMfjuIjzlpLZe0K+GDS1s0vatwxVDhH17q+fiRy3Oic3E7uwtwbijRWjvhvqCYuVxmrfKVG8eQpvdYa0y7YcK54QRvGGe3DSEHkJinNR70m27K+n0C5gLowXopbUz5bJr8IKK0LeMkLT0chrzJmuCiqzcmIbCp38hLkpLOf7zkVaafXJz3Iq7SF/y6GIun0b+ozDUmRNKxeOiLeNbMPrMzmGCymQrrOi+ZjG01wMG6Zsh5EjQyqPhJVlpG2QBGX6mX41hIL+r3XHpCM8eq/mI5ZI7PZop9hyRqyO7j71D6arIGH3PZvXs1Ja8uT0Z/XV2Ry+fKZjV0gpe5UXVlVOEnISUjPY3LZuu9VGbN31nXND6zktKo34qH4rYFmAsTbtKbcRK5vTqy+cHl3cuo9nsraVAE5bRPAXP/5/9lVQABAZh71jqgMafInALmVMyn5vS7I+shoJdLvYAKXZOmUy771jKFA2Fo4CtzKX7mBHoJrBHFWYBerjEHUCTNIGnMyVxUNKSxUwQgcnjvftsEsLEIza9q/GD1GTXGXvrZz3Cc399y+YItza+oYSetrZrycju0Z6/97Te/sRP7AJjQrnri57+w4Tjl70SUy3PlpfjuAoShDrWpUPXwJ425g0Rl3bh6rS38+EOAqAbrM3Ykvsx+Yt0B5uSjDsM/L6NycR53cSRy8HTlSWhLHhJ6+iMt5pd2lC5fNHsoja0BgEbATvhDgQ8gCC9oBHXA3Bdg7siBg/bBO+/aYSLU5tJHIzE7nfAAwByAZSWaXBfOnLHD23fZ39E0PH3kIOa4feyH//Qr6wMwlE8000rAIGmLiWYxZhk1aAAc3LXHPnz7r3Zw43o0rvLskZ//3IYB4HRDhtXS6OLNQuCRgmhQDP4ZG7zAtEKVUNqKAkeL4DinusHOYL45F02yeZ9/6iDsNICcqWhhFmFqG4A5HGUDxrkvNl5sJBdpBKovC4kuKlBIAJm0L/cALK7Hp9qyD9+3qsoy64HJ7sP0Sx/8tLXBB56issrMyE1CJTxk63/wJLouc37DXhlI4ySmRGDOwSnKCJxSAAR1kHpJ4Kj3JdcU4KINQFlxTr7l19TbYnzfffPl53YE33H97r3Xxj3yqI2fPNFKSkq879rwRV1mvZ+//ZaVHT1mE4kWfP/jj1sPgnDkM35lAiqQMnIj02JFJ5aZqcabgGq1JVcRVwGPywiXPfvjz2zH6pX4Xjxp/fArN2X6DBt77zhvqJquTfl1D9UAkh7YvdvNu9fMn2MdBw+xmWicTp010xqQWRVmr9WMKWnoiQ39qf0C5gQEqw/27t5rGwAVV+PTDoeO1gdfdU9BoyMRaBuYLxoAWl3zDpG5VqnGthhJBK5FVcKWWEttyWWXc+o4dfXOOAjtagHmbmZvxrGVzKhUFVOur9Y7dUxen1TulNLq3fAE4LHjI0ajJgJz5zmWf7nNR/bZ0XLO0JgrJhpnuw4d/SVRwaaqAehqcNkhcK4Vc3MHAv30btfJxmBm0hlN5tY8s93MhKp8XmXPNzSfFlX7Dd1uOMEbyt1tQawFmGvqpgwgxNdCYU7153pTtssehbVIUu6yOa/vYhj6+r3UvH/xNeXMvGWYCzITmpiKi/2mlKs7uhQ7zZROZc3iwU8vxVhc7GXTS+UX1QjMQUnrMs/bAsxlikyyyRJ8ZobrPmsB5i4WYWrM65KfxNF5cV6lNNtDGUTSy+kdJP1cVcTxn5l+656pAS3AXBgcSW+G3e3Wkc0PMe9eRrUGtgMM7K8EzD36wnMOzNXx4i1TVp+3ICSJREAjNf97tSFVdci0VNFZ8wTYUEZaVDg+swuACvPQovrm/XdhoBXA3BuuMVfStZOb0lVjQimzPQfmWC3LnHHv9p22dvFiW/ne+wAJ+TZg0mR7GVCmK8CGAgk4KEOdAhFyWBw34rvs2MFD9tff/rsd+XablXRsZ7Pe+KkNnzDOgzhIY64W/sJiHABC9YB+5AGyCJjbDDC3+OOPrBZB9Rk7yp5JacwVGNzRejZ4jHtvdSIIycYBP7/oPylZqZg2ycd/kjxx8ginoqB/bPwIEJKmlgMltE8gmnzvlZ46Y3u3brNP//K2nTyw10rQWpoM8DWSyJt9e/fBhLEKv3xHbe/GzTb7L3+xsvNnrdewYfYKwFzvIYM9GnEFwI9mLtmtt+aFog4A5zjmrF989LHtXrncGqoq7AHaPmLiJOuFOas0Gl17C2Au8uiAlsAvgVne343wB9AErZ2bttqyOfgjWzjfijp0sOmvvGxTn3wc34KAcYBRAnJyednxxTD9oXEpv20C4mTCXEvE0PJzpXYGU+g92761nRvW2xFMQjv372dDJk20cTOmW/vuXV2Twf0S5gMIouWmcSrA0Wdl8ZaIW0J32Uu2bH6sH2VlF7TGAh+tGBMCCNWtyiLtQKfHOGlEg66YeorRsjSi3s5DA/Srjz5wn4YjZzxsk/GjNwLzTpnmSvuvABDvG3zLffKnP7lm4awXf2AzyJMHKFlRV2Pny4nwiglzPf2RR7vb4mevhIAWbYgA62AwQKD84OUJaCOs9Fk0ET948y3bh7Zcq8Y6G0PE2/FT7rchQ4eg3Rf01VoJDAcsCz4aw/2w4JNPbBEgaTHabrNefdWmPfWktcIEVabdFbxkCiDUS6PGm0DRfNovbQ8B7OvWrrGlc+fZQfaKsjvyIQJN3D/FCjHXlSZlI7xJ7rqndIckI5gzbYkQw0nGr2Trm2Qdj++wfWhXCzB3M7tV96+2MPLi2fWNqFg67kMNLb93jgSaelYjRnOX9gGY4xnLmYC5TUf22TGAOfn6LEZjriNRr3OZKzXXVuLyoJq/WsA5mfvL1UDnwjZ2d+9+1qtDZ+sEONcOjXy8kzowl+/P8nDcVDsXb8R2wwneCKZuLxotwFx6f7FeyhpTUT7puTKPm8rE9yddjzNyZt4bexZZza6r2XTPpB+uhv9+nN3eFIda3HzXDfKBl/B7tcXTc6eDo9kcpOdrjnYqf/LCp/P4rhE4CxRivnR6npYljHAac2fXqHR9fA1U/LepsuzMGec+pjJSLj5x6ln8XJzrelIC/6KQ+ZZwPTQzy/raxPsikXu6wDOzXvosWwZRxjE9nqcoNN2PqaSrPcjmL3SCl9bhjdgyqvCTkJKRnlaR13upi2n5wiESz8ob1ocXZbyFE9SAFmAuma9Cb4ZOjVPNLdx3V8Gady+j2u+tZLBeDpgb8uBUe/TFCMyBlVDQn02UlUScjv9CU/Q8oWlKk7mfgA292EtrLh9grhU+50pxWj/v00/sm/feSXzM/dRm/OAHRFbtiMZcg1UDegiwEdCTR9kiAJCdW7bZKnxbrf3wI8vFp9lg/Fq9BLDQCW0dAYbSsVNdDswBIrRiwXzy8FF76w+/t0OYtLYGCHroRz8EYBqPv7R+VkkkSpk7+kIcvgUeFqDRl4vp7SHMQzcRlXPZJx+7iV8fgks8/fqPXWMurwhgjnL68uESUJvVdE2KPjEKlGOBL22ytE3jKMmaltp0qAeTNlHVsWNKHKdAOZk4QkE+49TGEqKzHpZm3/KV9vWbbxI4ocy633OPzXztVYIODLOO7dsTDbTWNQ23rVpj89/6s/uM64fZ5w/QNOw1eKAHPSlXRE7qE00BoI2VNXYauc3Fd9m3SxdbxemTNvEV/NZNnmT9Bg/yoA05vJC0on+cW2+r2ssBwJrMiKU1JxC27PRZTJbn2SaCBBzdvs26jx5t0158wSZPn0aghuA7TeNJfxo/AsIExpai5Xfh5BnGTL1Hdz1xFHBx527Ax/1WTiCD1u3a2T1Eir3n/vvcZ6D7voMn1NusFeCQNPoE7AYh0ksSJpuS9Jt0k59l/JBBgJJgLZWRBqDvyZRDe91sl3QBzti3uvmztCzL4XUO/ttmf/A3qz5/3qY894I98Pxz1n/QQNcsrGXMC2z85v337e9/+qMVYO70xA9+aA89+oidBZA7ceqEHTl+1I4cPYKvvRoCRRRYB6Kq9urb18HQzgSdkMzrqTtP9wUBSk7tP2Rv//a3dmjTBnzKFdlDP3vDRhIdtzcakVUEP9E4kulrHvdOAf2aw7g+eeiozQMcnP/R+1YI/Ud//GOb8dyz1grfhNWAgxWYC0uLUdqZenmUdqbuXVRLHbBdPH8uwSb+bpVnTtuYmQ/bJCKx9sSXYSu0Kh2Yg0dJWjJMoOtEvOrg5LCZXeqSxkAz1++EpNCuFmDuZvZluMuTOTRV0fWPqEgh7lOkWw7uKAloqgvzVwDmyvUsImW7NOYOJ8BcAS4vCHjTmUj1ucytdTyj3GWE3EbwIayKYBC5fFRqw9O6K/N8305drE/nrtatdbG1wfWBzFoLmVv18YMn1o2f71oG6XWPSQcJ6HuNBf1+X01ZJUhWGz5ItXdZSC4+0YbZNvxmilx5fY0l+SVyzMzxjz3TLRH51D51i4SGhJTQTD+OvHOSuWmhehWb2nwjN+c5MpXNAhe9trQ6Y5+Jh1R2f0EjIcWa+lEnrHMjbRVgi/wrPVVeF/wkK03pbE5WdHhP0QBpqibtOGTN+I30r2acKG82rxnErvvEa3Aqzbfyu1XgwyWtX2Lp8NYZzrz9KWHFHM3so6D8Etwl54F8OgFd4Dwr6Ua0J6lanZBisOkoqTK9XuUiQ3qeVMG0g4wifhJSMtIDqaZSXMymm50/ZI7zVXrRy4/Jppy3ypFa1gLMhRGW3FBx4Dff6bdKx10dH969yY0S5+kbAcxFWs6FJnO/Y3RDMJgAEwSYCbApRKVJEVkvEF1zLtpos98FmAOgmaWAAPiYEzAnjayaCMwBgERgbsfmrbYSbZ31RGUt7NLFfWQ994OXPMJkLRVKs031CBSKGj6niFr69h//ZAc2bnCNpUkEfxh932TrN3QQX8Tx4cVg1+SpBbnqCcAcGnP79tomNOaWw6NAvz5jx9ozRBPtgSmrA3OUU1kHH9Reyl8ZmAsj6FITpKaKkIPhh9z8jATld2056pET/lYs+vORZXsigX27eZstxa/ZSnx+5QC0DHoQn4C0sScRhuX0X37KDu3eZ5uWrbAFb/0FU8Nc64955UuvvW49BgKoAOiUNwDMSc7UJQ01VKfsLP2z4Os5tnXxArQbj9i9L7xoIzGVHDB0iDUCwgiUE2AYbhTthckhDwAcBZeQ6WQuWotnjp6wD996yw6sXw8vaHWhnTUO32ZD8UlWC4im9voiAD4FygkEk6bj2iXLbMWcb6wCbb9aQCZFNa3AR1oh0VG7Dx1qkwha0B+fd1369kbNDy0/3nLUTwKIoimlA4WhggD6iUnJM7Crs8B+OEr9ajxI9hrTGkfiyV+gkn52m1KZF9NHhQIAATJ3bNpsS//+ua1FM7Ckdx+bhrymPf6Y+47TIqUWLdGqigqb88479tWf3rQO/QYDTk63wcPushVrVtmxPbuIWHwMUC4Aaq1krox/oiI05noMHOSmv7369bMSIvPK3x223Hbq4BF79w9v2uEd26wdkYOf+NU/4QdwmLXv2JH7R4gi/wHmcgHa1K85jJtz9Mfcjz60ee+/h4+/tvaofDs+j78/TLOqBMyhMReAuQBwS4NS0ZUVeOUQfhdXoBW4HpC1Tddudt+TT9jkGTMxw0UjkPHg5t3cy0jO7ymJXjxczZbKpv65mgK3YZ7QrhZg7mZ2nY85KtA+Hqu+9GOdf5ctjse4/y5lW/LeXhLQOkBjhdkTlw1EPuOslKf8twR+2Hpkv53A/UUOwFwJLiA6oTGXh/acnjPSmqvjw4ueVRW40KjnQ0wrItLLt24X5tk+nbvZgG49rCuac+1xfUBoHn9+6Bmj59ENfdlsGahI9/o2X3/5GkzjgX90EkbKVgXZ75OPOUlR60+NUR2l1qWcIR798i+MYV2PWzqgo/vD13jx4n/AXpyFdZ0OAgNN3GYy1NTezHQ/U1kRulThpMjNaK9HwrxEvbEvUm0LHZbJpiabuHEYes4bRGrsY9I9W8jrsohltPf6lZq5eW5P1CI8jJOY40qyiLTC2Iqlmt8r7w2dKy+qxmvw1ItbeVHmyyZomPgWBJqVN3y29ixB1FnXLz6N96CupB97T0LjYjnrvgt0MvNfTPtyKbEZnsfFk5HizCjFq4oVZhOEgaxSGTkyRBAIpa7Ha14+nnC1OXppl1Plo3zSEnzsN583PdetdCxuW4C5MMqSQRZ2YRq7lbrqWnjx7mVEa1DHefqywNxDU+0xNOba83VYpqwCv3zCgZAk4nT4jbRCQrhl5DstNZgAN1oJmEONKRfgp/TocZsDQDD7nb+i4ZRjj6LBNYMokQGYa3RgQfWItwIO5GNu28ZNthxgbhOaXMU9uttdDz1gTzz7DKBEJ9fkE6Cn/DJJdSCQxfJpfHm9henggXXrMctrtHvRDhp7//02cNgQqwHM0QJcPItTgTD5LKTzAJQO7gHMQstsOT7m6nnW9MFn2DM/fjVozGH2V0PbUlpBmvX8Pz/JJtk4MBQT2DdNnMirKavniGNMJ+IlbpKrsgoYykUOQGIOlAj0ygUcWrN0mc0HENq3dIn1HT/RxmAiei/AY3u+6OcC4inS6v6de2zD4qW2EI25RqJ1DibS7vM/esW69e/joJaAOWm6CZj0QAZo2Z07dsLmfzXbNs2fb6WHD9pogjWMnnq/DSaghHyLuSlrFBy8iUc3G4UOKgQOxpadPWcHtu+2z9/8k50nEIdMTx9G827QiLsT0+jQVh87ArooKhCsgfoXf/21zXvvPTu3dy+gIWMHM6A6fN8VEkihKya49xIJdNSkCdaffqymHxX4Q8Bc9HEmhiCXAuSiHElSR/jOf+IDIy0ppIeyfm9EYE4XyOeO+9lTLWa+AGSMsUWzv7atSxbbOXz4DZk2E79ts2zM+AmYdgZGBDZWohk3970P7Cu0G4vatLPeQ++yTgSsOEiZQhyKdwV07ED7FCTiLBqDe7ZsJQrtWXzntbfhM2fYKCK8ShOyDS93OIOz4wcO2rt//LMdQhNRwNwz/9t/sn4AfSVt27n2mgaZNOakaadAIbmYaJcSyWfuhx/anPf+il+/YkzIf2IzXnzJCtq1AZirdWBO96PuXWlQtiXanExYSwn6sHzREtuAOfLRHd9ar/Hj7b5HZ9m9RJ+tT0A5AXMMOgQXgLlEXBnjWWnkSG1+nCX79OupjHfAQWhXCzB3M7syDiXt45/mgXgcr6ePsUsdRz7Tr8e0SCeet+zvDAlorOhP64IqOlnA3AU033ecOGzbj+6zEwSREjAnjbmOzNd5uNQIL5Q8t3ju1fo8Xw5AJ39zOLzgA0oRH1k68twd1LOX9e/QzXq1aWuEPeL5wXONOpg+eb4na4I4sDTo4jGH32lrbsB+JwItmb1PeQaqC/T7vQTmkvHnIJuPqTAotXwKkmGvPL64Sh90POO0ruJPGbQOlp/kf9SWzkmqTq1JOEnxzbGvO8ms/GqHl/NzeE8VzDzw9rIuUtOutClvrC+V93LlIg+pzJkHlwLm1C5vHLtI3veRSWdaFz2ncvsW5y01Xlkj4BWyh7ze9ymqoVwk63UmJynKfi5Zk5IkXiSDVOaEkbij4ktdilnUrshnTLuxe6/BSV56FFxdjQyTsEmgQahNHZS0NGbJoKjsaQmeJ/mJx+n9IqmF+UqFkpIpGslBLJhG91oOQ3dDzOlFCuF+SB838cql95n3UHp7vUwzY8GrTMuYwUJWRWnZuCL5ZGZwmWUm3eJnasAtAMyVl5dnifLGyA19Hkwe+YN6OftDZefty3VL3deWzMUaCnndVo8HOXDAoXOirrz9N28W7VMT4zzdHDD3638h+AMv6ENlyvrCsx69U1pJeKC6NDAHUZcRd6/T50wv+KIvv2OtAG0cmBPwAzA3D4AgBcyhMTcNjbm2+EiLGnMyRXRfaixsFThi8/oNtgxgbus3c61dn942DN4eBohqh98ydL4wgQ0vva41x6yoCGknAU3eefPPtn/dWoCtBhv33HMATFNsAABGjbR7Is/IQzzK1FZfuQ/u2WubV6225YCHAh76EPjgmVd/TFTW3pYLMKcACG4qSTntvb1h1nIhhIde2nhBBpkjKHNiijldfsmJr3coFftH7XIzXYCqOhb+Ah2XzUVb7usvibxaZhOeeNImP/mU9ejf180XBSAp4ICAuXWLFtt8AKEcfOMMAdB8Bj9vXfrgm68wzyr1GgJNmch69DiCX5xHY+6bz79IAXOjXnrBxj4w1Yag6dYIMCewR9FD/ebQDeLgFTLnMAe516Adtg/T062r1toqTJbz0fwact8Ue/Kll6xjj27uEzBG+FX7RMLbCZ1GtOPWAjQunz3bKmhjA3wxeqyKaL411dUEdiiw7kOG2MRpD2JeO8HyoN3Iy1LUmmtEiy2Y2QYZazTGsZ7qBerTpi7z6zqJaTpmUxkBor7n2IFm+HNfiZQqoPCJ48eI/LvFvkFj8fTBfdaWl7WHXpKPv/HWGxPPCoKUxHZVoUkxD799X/75Tas7V2Gtu3ex9ozj4m6dbOio0Xb3iBHWo0cPzKGq7ejBA7Z09hzbv2mjXThxFC283jbxscftPjTU+mDeKhkfIxjKO39ibG/ZhP/EEnv+v/wX68u4LsYnXTX+3rC7Df3EvlAgLWO79ORp7rsP4Pctyy1oY48RbXc6fZJf0tpNuytdYw4wj3bmM7CllZkDoHfi8BH7+N2/2R4CTdRWlNtIxtq93H+u+YgMFaRC0WMNjUxJ1Dd2GWM+kW+4GOSa5IxJd/Q+tDUTmLtw5ITlE8DlifGTrXdJO+a5NLlEeakgf+mnd7SgrqNxklH806ug/gSy6CMK0wi/sRdIZHPR8uP7kJRxrKR0ucfjuE+KtOzuEAlovCgCttY58jEnDakLzKV7Tx21XccP2gl8tOYAxpUQaKhjp85EvEbDPNn0YUqRu93XHM+pOj7aSIOunmd1Hg+4Pl262ZAuvWxQx67WiWeYXAVo3OXzIxcVetb4YFNi+vZdB1t2+XRaLcdXJYHmgDl0IFkrRY25vWhQ7rWGYqLPo7GfxwesXNZaMuPTWiY89wRQXKEz4tv7FbJdzPR3HRQXU2guRWykgA+qcLb4iesklVHbQhvDcHVOEv6VT6UygDlAOW4NMt8cnlVj3AIb+k3qCgz55bjWV99qS1hmH4AknYc70i9n/MS8sQnxPCNT1kmUUXpy7O70NB07Pdi6HN1UWTIpX4Y0s8qm+lDEm2Hay5Lu41TXnWasPVCONHwfkgJ/KZlqdId3KFWjLSmZHIdx4ifZPwk97WKt4jOy6tnjhSSvMiop8pVNMrLl2bPKKG8G7ezCGedqVaw840JyEok3d60pzXOJjA5i5ZEs+/hOEpNiSS+XXYXyK4Pv4Y7GRjmk5irJL41IOOZXB0m5eDl9nyqTntjMcazfL/lJkkm8cBiamPAQiXq9uhjzJnt2kf+YErPoPBw3jYf06mJ+TyNjc9dinkgrij+mi8v0+mL6rbsXt5nAXPmJk2bny2w8Aab6d+yGNn5JCDCVNELzRZRxbOuVZHWl9rdqAeauJKJru+7dqw6jeJwYIvCjfYzKerXAnLjImMScdkgRkKFHnsAaaaMJ+CoQXsBX5LOY1Akg+Pqvb3sUzUezgDn3MQetfBY6GNMx4HJs47p1mG1+Y1vRpurQf4DdM3O6PfIEwByme3UCyrxNyeClZpnuncIU8q9EIz1IpMwCwKSHXn3N7iZgQI+BfVhk8SUvkYXaIY0x+UWTf5iDezFlBZhbCY/1lOsrjbkfvWpd+/WxnKJ8FuxJ0AgXZJCBT5YipAnS74Rk78e6rcJkoCLa4k0TzsJvktVPBI6oj4L8ggZTazQHZSZzHKBk9ldf286VS+08Gle9iMQ5AdBmDCaeRXzN12Qt7bN2xW3syL4DtnHJcvv6339jjcWtbTDA3HM/+pF16NkNf3G5DmjJjFTAnGRWSB9dAMD57IOP8TG30CrPnrKxaFWNnoL5KGakbsoqTSx96meTpoA07mQuKWCvAWBJ0XBXAaKuFrh2/pyNmvmITaavho8ZbbnyZyYgx1Evyc5xPedXPMv8thoQq+r8BasjQEfFhTIrK71gZ9H2OrBvnx3etcfOs2+HttkgglxMffpJa9+zO4M3H8052k1/ycdc6JrAY/hNJnx/glNPImxdc40FNYY0Lxf3nPg58pHpqviTL8Jc2isTz00bN9oytMi2z/3GCvB72H/CRHv0qaetGyB/Pgt1jS+9dGns1wHSzSFwyee//51rF/YdNw6wcpINGTEcX3J9rAOmUfJhV8/LXNX5Uju5/5Atx0x51ewvrZ57xmWIeeyo8fe6ZuNRTEvf/t0fAe/WW9sOJfbif/tv1ufuu6wNUWAFlDHAXBbat5ZvI/iuANycixnrl2+9CTBXRJTiX9oMQFoFf1AAiirq8eAP8FsA8yV5+W6qe4z+fPv/+zc7gtlsSedONgNNu8H4C+yC3BXwQcFQ3JQV/l1gkqU2CY+fMPJ1zFkid803OvYs4ZL/6nJqEZqWfrsfhna2AHM3qx+5Pf2WZuT7oGK4Myb5I72KI7ku4O5lvDU9sTQFNfenvtKfxmLYmkapj08Sm67FPC37210C6I4zVhqtHDCujhGgP/Tf7FjpGTt0+pgdPnHc59QSnrHyAZrPB6H4HJdWUAPPhAY0j+Uaor621sp5dlWVlePiocaKCwqtK9py/dp3sbt79rG2nCsYRJE+mlCPPmlccvsug61pqF6SXMuFy0sg9bJLNl+38Qz93gFztN3nQV8vNT2nXR7JeNRcq2d1nBOVVWUcmNMRk7JkqWVdsijm4OZtqlscxPVGvDedFa40dxvF9Ugoqd+Lt0A3qwkQi+nZJWI92Xvli2kqnCqfJKbzory6LpnqsmeJBXyvH9akybWLynrBVG2pypyWHpa++Zn3kY5ESzILbmpCDv1GOabqII+zwLW4VosUY0VxnDSlJ/RIcJ5TPMT0rJziX1sqH3WSFHkJF5NfpSeHqezxUiSj86wqkixZu1CgqYVZl0XkCnT8MmRiNv+oHxlk74dcjEnN1JCdFNquEpK90wilg5zDfeZth67uufTNc0Z5pl/gODNn1sXkNJRPuxZpOR8hPVQpPtLycaiySnIa2seyHPuWIq6WkJdzJwGhSMqzxJOkmHZK15j062np6YfiJ7uoKGenpZe59Y7FbQswF3oy6bnUgLv1eus7c+TdyyiOA1oErheYSzGR3HCa0ERfQIZcwUdgzn3MCeBAY+6Ma8x9kNKYmwUwN/0VNObQOJLpqIA5vUZpwVrIkrWQu3UL2kPL5823jTjZbwPwMWzaNHuSiLGd8DdXT101DfXcpGHw+usXINNJAKy3f/8Hgj9ssjaAVLN+9jO7i+AHHXt198W3FgwuC/h1YE6AC+UO7BEwt8pWodUnv2wCUZ4FzOoCgNJKwBzAhyKAhoZqlzzMRA8edNf7ra89f2EjRccqp132BBWSw2XyCQRycA6eCpCkAMoczBwPHTxoWzdsspXz5tqZQwesNRExJz/7rA1HS6vPwAFWC2DWgENqmYa2bdPajh3AcTU+5r78X7+2ekwmB6Pp9Pzrr1lHQBWBjvVoDiqvgnNIO6qAY5k8vv/2O7ZzxRJrJCrr/fjXGw6g2WtAfzdpTp4M7u9MLxTo0DmIWktEurNoua1BQ28bwOZxwKMuAwY4aDj2gSnWiUigMjdVoA5FZKWYb7zTOCAnAeXJ7JJTtb22OjjUrsOpdgOadKcYN3u377DV38xF++s4gFxXm/LKD2wgIFHn3j08oIQE6GMwNR4hBl1fwnifJR0QOsnlrZQcxphfRvZiy3Px432mccGfopSqHxoBsI7SD6sXL7Z1ixfi8LvUBoy910ZhYnv38OEEY8BYiX4QSOigH+BlPdpoczHP/eKPv+fremubAIA38fFZ1gWwt4RgFnrJq2MMN2IC1Uh7DU2qVchxwd+/sOPbtgH6jbfx5J86cwbRYAtNwNw7+JhTVNaiNgX2+H/+TzZ41Ejr1LWrA3MaanrIKTJrGwBd71dMUucCDs7+21tW1J7gD2+8YdMJUtHAmK7khbIaUywwcB8PAvKk4VoNKHp4+y5779/+zU4dPkB/9rcnf/Ur6wcIWIzprfpRoJzfsRcBc2IARuKmca1jhKoHqoNz8ZqSk+PIe9ql2/4wiKEFmLtZHan7VNOIADkB4torSE8lqWX1NVZOsBZsD33eVV/oT/e5nlFxLGreSV/oadbw+5903yjkYzM5jeM1OW3Z3bYS0GhAU46dxssFPlLUS/uJvwrGzpmyc3bi3Bk7cfIkUa5z0UomKivAnExZ4wcqByCk3S1tZYF0zPlVPDuriExeg9YcTugMvSrrjAuB4b37WQ/m3/ZFrfE3x0cT6o3gXOAkS5DfZaA1SyCLXsvpZSXQAsyF+dGHEuuo9CEV58Q4D2qdLjPLuOlI32y9FGu4dFnGPNpfav2bnue7HgcuEmAu8uCVBUqRZ/EU69d8H7dQPp6l7VMPgSRHWpm0XBcdpmdLHTdXCRdjctz7gwaKUbQqr/VcKmNSW3r+yGYsk2RJdoEDpyPi4RR6QRZOOhaEUKSlwjE5jgTJLtbrPJEnkhODGiPJ/ww6CSMhd1OBtOSYCPVUBU1p4iP2W1OhJv6Uls635xGdSMITrvQTK87Ml9ZiH9OZVy8+i3JpqrtpzCm315LwlaoxOWiO3dgHKunyTyUg7XifibAKJ0JwOjpO6y9liVuKxyShuXp1SWyl5J7GrLOQFEr1eag0oUhm5YcHyS+ynCKhXErkv4rFPz+nTAYp5c3aRCf7HSIrixN1Olxo2ie0L8p8qyYEyaitwlOkp9+iMee9ebt1ZPMDzLs33Af+AqJc6uz4dy0ac6IhuuGm060CPf053QCU5XKTpQNzZwXMER1y9rv4mOPFftYbP7VpACztu3b2l6oqgLl6wC8BbEUgBdLE2vntt7Z64RJb9dEHlsPCeDA+z14CMOqK+Z+cz1fyVVqf5qKpoYIknDx02N789a/tMGU7QPvJX/4TIM4Ia9u1k7+oyf+FONafgDlpQsl/2749e2zTylW2BlPWBsxV+gHMucaczD9dY07adowJNZq6fSHCLOe0NKHw1zTLxAlB6VSkjYxxootJSo6AkEcnpS0y/22Faa00nqSpVXr6tK2FrxWLFtnx7dutNSY1A0aNsOnPPWO9Bw10bbky5CA/ZQK2ijFFPHnwsG1dudq++s1v/MVjIFpazwPIyJTVaJv8BgqUyoe+zH+lNXgGH3MKmrEX08V8OvLhX/7C7kY7rVvvXoCSHjLD21hHPYrkKh+Ahrxl8rhj82Zb/Mmndu74cSvB/99IANSRgEoDBg/GhBVn2QCBDTS0Ea073nt888ARvMwIjMwn0p1HHkVYNQBg8lmncdAO32r15Zh57j/g2ny7Vi6zqgtn7K4nCSjx8AwbRuTcugI890jIouUDMvSJLw69cyR4Vek/MBH6X2NVOV3+HPvYTfI3wFMj41H9oHZKs1D+81YtXmrrAeUUfKHH6FE2nnaOoZ2F+BOSxp7GRi7tVR82Ipt6QMb57+Nj7i9/snyi9T32+us2/YXniYha6FpnGgcC5hRMQn4Y29JqmW8vnTvXtsyZa50GD7LRs2bYw888Ze0L29iJfQftg7f/arvWrORergM8fc1GAp72HQg4S7vq1U/spYVXnIfpFHyfPHTE5n3wgS34+D1r2weff6/+yB5CfjWApdWAuTXS9qDd0nAtYBzkwXfp8VO2F393nwNwX8CUqydBN57+1S+sN/1ZVFKc+HcMwVD8fpDc1Ae0P45xUnxLpA6/XKcOyTljI4OS/C9mzshw+56E5rQAczerBzWkHIxD0ALoZJIozeYLDUSZBiA5X1NpdYAqGpPhXzL36x4hr39I8n1qdvBhzN3v41F8c9lph4jcSmnZbn8J6M4Md6e0uH3MMFdrTcHkaVV8rCjDfP982QUrvVCK+SrBG1h/dGjfgY8pfDJTPpVnHPlzhnWLnj+KSC8NulrMWquqKq0SdxP6oIPXTutPMIj+Xbpbrw5drC0BduSqQ64RCtOeQS7XwFYYeFcr6FjmavO35LtIAqEfw33v9z99833TmEtGdXiOp0nI5cFEGObCREbIJ25ejoueorWT7gXdG0kWzb1hbUCJtHKx/PXsQxWXAOa4KJ71nBA/cW2SsOUXU8fpTIj3eB4bwXmzeWO+ZvZpRZu5SlKUWXI1RV+Vc+I8KDF1IcmY7NSuyGhKrF4oSWZeCqf8il5yTWvwXOY5zWOSia93Y58ltEO1uq6EkC+yEduVkKOOQFz0Y1pCpqn+WHdCL8VMzJi+j4x6vdSealxTpvSkVPamy1d9FMvGtqUKUoH+hY3xEDOmMlx8EOWiRiu7RqVIpNOJAgpiSK5wkojnIqKRpq9hnCeyOP00nmJhKvWRm5xHucXLkXj6eaQfr8W9+HPuxL8zG644HxDwmkRIx15vcl075Sfdaahw+B8yxN+EqEjoeiClNnHOn6fHvGl7FQvvbmmJ2Ydp5SOdRDLZOW/hc3Ee1qktwFzSiz44EErs1Fu4967ImndvcmPEL0XhpSQM8OsB5lQ5pH3TXvT1suMacxxHU9ZWLE5lyjr/k49szt/eIWMre8SBOfyPde/qmmhVmIHU8XCQTzW8XHlAh4N70WJbsdIWvPM3d27fH6Do1V/+ynoO6ItqT56dZ/ErzS9pNKmuVoBGxw8esj/+67/a0d27rHOvXvbMP/+T9b1nmLXGL1mltJPgTxOO+FWZXMAXAS97BcxR12qisgpMEjD3HKasnQXMUVc1BaSVIe0iydTby96BBiVoQSLaPhHpqvIpzQ+9QHMTpYAhycs12MQP7c9XGm+cVaUXAL22Ak4usE1LlrDgr7QpTz9tDzwd/Mrlt22NaSovErRLTqjlq01A0llAtl0APLP/8EcrJwBBb4C8ZwDmevbvbwVoEZKTb/bUQ4Ncaw65nTh8NIlmu85a40Pl0V/8DOALrTRA0Eo0Txp4yKt/PcAET3aVU3CDdWvW2soFC+zImtUsxHKsBz7pnnn9J9YDLasCtPdqpE0AECswVi89LjjoSFb10pIEHNKWLwAM4E7nivQquZZgeqlAIFVnzmPSvABT2dm2d9tG64357n3PPm3jH3zAfUTWSjtNQBqyT/2pIv4LONLDJ5UeksNYpV6PDKt2cRyMYelKXq4EcjpIDEB6Av97u/Art/Czz+z0kcPWBq2xSZjTDsNPXI/ePV17zJdA1CVQzM1z6Ys6XtAWffKxzfnrO2hd5Nsjr75qDxGMpBEz0laMMaG7dbS3HlPgXMyVO+QV2Z4daAcuWmorPvvUSnr1tLunPWAzn3rCuqFtUXbylH2OufHmJYus/MwJG4nW5KSZM+wetObUp6Il8yotukrwaSTA+TBBTeZjnr3k759Yl3tG2kzA8AdmPWxl9Iu0VOsZOwjHNTRbIyhpzR3eg7/F5atsHhqU8gvYD/Dx2Tdet659e7sjdL3MCqr1+0Hy5Z8GejCH4JzjxqRfRVuydy1C7cmqLdmFPkrO1U930haa0wLM3aw+ZZgx7vlD0PIhC6RPxGmeAVVofJ47bUf5O4NpvMBv9QXDj32Y+zUeGcaMR+ZAjUv+NE41Ln38stcoFeDnafyGvV9o+blDJOCaloyLOj2/kzZpTqxlbvSPJoyAIp5Dbfgg0a5de4A59Pn1PNOAooDGCkiEDnyM5fK8qJUmcmWllQHs1VdUoZVeZ8X4ze2KL9BeHbragB49rT10BNhprROfPXq+agw6oSjfyFQ8b24vXlq265KA3/N6bkFFv1o3fK+BOR9T/LhMkIeGObLROseHpM+VIVFZfexyDwWAWvmVj3ONZ9Y5Whf5sc5v4ObUsmmSGJLC2s+BJ/jxuV/X0uv3xmSmNc3/ZEwWJell0o8jqYRMPE3tY3o2i5JnOp3UcZKuar1sciHSSRHWQZKYKptxkcus8bID0imLALngMzqUFBn/qKD+S4gGWemZqBLh2RjrSfGmRnDN2eDHe9xPEioUSE5DWzmJ9HxAiXRzm9PVhVC/z4lZ+dLlmcqeledqTtPLxvZ5OSrQv7CF8XwleslQCY32hut9UKUSSlSmZN0rTjq9EbGquCeLeHMyyq50/lI8iZYu8pcq4vRDoZCWXGHnWXU9lbmJdsIkxJo2ZXMJcBDZjGmey+uChp6bybHSnTwFwrNMCSmOvZjncUKeM5WmgxQdmI3zjPiONJVHx1q3XXYLIvAsKq9Nko/HIeVW/xW3LcCcZBD+tPMevN06svmB5s1iNGtA+4TA/mYAc5Jf0EAQ6APQwblHPQXAaQXocO7YSVvwKSDFewLmzGa+/oZN+0EA5rQ4VoRImaeqnMAYmeGdBCzavm6DffmXv9iFs6fxpzXMXv+v/9X63DXU/Zad5aUrj7xydC8/ZwJCjqFd9da//r8EgThiPYjm+ew//bP1GjwIQApn9wIhYFRThV7EpNEnYE5RXffv3WsbV620lWgX1QMQ9RuHj7kf/ggwIpiyCphzUI69+BQNX1C7gEVWB2GLYFDTCKJQ3EQnOdZe7ZXmnswm1Y4C/qRxdfLIMduHCefmpcsx5fzWys+ft0EjR9rkR2baSDTg8toUWX0+JoVoo8k0tZbFfwOmk/LRV3HmnB3ctsO++P3v7czhw9aV6KjP/OKXyG2ItcGEUia5eaqLKU711VXgIw4zybd+/zs7vnsnEVS72lM//5kNRFOqHSBUJS8penCrvfm8YDQCnJWVnred1LFp1XLbtXaNy77/iFE2YvIkTGwnWBv8rzXmyxcZL8UsyhywUtsSUWgSdhCOtmpy18uOACWlq/16YW4tTYVWeVZ3odLWIYfFmDRvWrHYehGFdirmmJPRWFPwlhq9RAHs5tJvoiFTTlWjRYnr+unhwGJEiXrI65ruBU3wvqd+B4zoh1YJsCU5SiPw5DGCPRAdePPKVbaHgCLFRCsegKbe+BnTrBdyLeFlq6oeeEDjibrrBUjBv0BfaXIu+uIL+4ZIxJWnTjHef2gPEe1WPtvUB7UAnq5hRz25AKutG3Nt59ZtthJz2bWff25tCSZx97QHbdrjj1q3jp2tkn5V5NwNC+bZif27bcC06TbtqadsAvKo1ssk9OTrKA8NRGlcNqKFJ3pLPvvE1i+cZz3HT0TT8nmbNG0qpn61bgpeT3sFmsrkSi+K0prbvnGzB11ZQ+CKgi6dCR7yoD1BoJaOjAuZJYv3AMoxlmm3+kzjX6Ct3wecapQHSasfgjZjkL2kr6thiwsFncfj5NJtvwstZVwgGwG9mm9agj/cuG7VsBMYJ6f9+nNgjnF9qOyM7T99wg7yd7qilPtC8z4bHRL6hD1ldayZwveaM5gjNEZ9LIo4G1MC500z+Z02Rr2R3/MfzVRx876PJ+z1AtumdbGbspYUl7gpq54zvvkYoQR7UdBI0nNM86GeRxVo3dXg6qGuHO+2aOQV5+Rb5+K2Hq21d7su1kXOmynn6wj2+hDk/yI7YQh6VZf9ifkvm6nl4uUkoD7zPzL5/c480ALMMbAkF/0Lw9yf0VE+8X2C5aevn/wjJ8+5KEutLXy+ZF5t9PUXGZHrjdycWhZNPw0XvCqtA7XG9PVHwoLaoy22wY+5FtqpFoatVdqE7yRVJrmWvov509N0HIurjOr3NPGjcz/xpOZpegb9SKb6bdoCpabz9KN4rZF1l2ryckmFuiY24rMuEvV8USjK43/KqGeiqGtmCpu3yfnRTwTmknpiHvah7lg+XIh0PbczlhRI36X4CGWj3NKzJKL0pFT2mEGVXIq2Lvm1mCG2KrQ5kvB2Jy0OvMb8qRxZB9DhfyDNL/8lGR9zMScV63qqxrRGeDldSF0MfCpdpVz6XIuXNW69jGcIz5+kYeGC501yswvZQv1eTnRTxOJBvEI9HMYP7ZFN7cWHcuseF1G/t5LjyGfMRzZnOJTwMx9L8XqgH9L9VyQ1ZtnrHVvvbqGAriJNr19rtitsopNkadqHtl+h5C10WZy3AHOhJ5Ne1MBQQuzUW6i3vjMr3r3JgI4PoZsBzIm2QCaWpeEllGMBEzKnwzaEqJ8nbT7ROue8/67LduZrr9u0l15EY64bL/loRcnHC2Vdk4s7UGDZhdO8YG391j76w5/sBICRNHZ+/L//HzZ4zEhr3bG9nakocyCqCLCoTr7Ozpy1fTu226e/+41VANL1HT3WnsZpfXcAjlwii8p0D258wnEAB1Auh78CEg/s3+/BH5a+97emqKw//KF170NEzDaFHmQgagjpS7pe6ARuBXCHscKDX8R9AcIixPMmvaWJSPX6T3Ksc/1FGUm7SX7BRLOcQACb0ERbsXCh7Vq+nJpyrBdgpEwQB4+4x7XYalgF1bKirwMowbbUfb/VA+6obxsA2k4BUH6CKeIRzEzluPqJn/2CoAMjA7jCSkoAWwHgTWFuvpVipikA5/3f/dbOo2XSk0ifz77xE0wkMZUVoAng50EQ4COPdp47e8YO7N5ni+fOwWR4m1VfOG/d7r7bJs2YYWMA5nIw1cE2FXNNQBzYE1AmeceJWFpdNaIJr0qXxopP3pINN5/aIN9uBQBMDszhe22tgDnGz4blS6zvg1Pt/mefsclozMnMWJpiio7n/eH1BOHGyb2VZEQdkrdP7hxQVaif43g/NKJ514q/AmTiNv2lpbZ62XJbv2Sp7d2w3iPyDZ082YZPnWqD77mLAAwdCaiQ52NXfojUlmq1S+AYNbTBnHTJvHk2Dz9zx9avtwmPP2VTAeYGIV+ND0VwLcT02H0dCpyj7g1r19miOXNtx7w51nnYMBsBEDt15jTrjMacfL+twXfg2rnf2B78zHWlP2cSpOPBmTM8srFrzCGLAgBN1V/JOFoD/6vJf3DrFhs8fYZNBeQbhQacfCopgIpkIuBUQF4xyHMe43DN0hU257PPbReBPDrC6z1o2M0kGrLMlOvpNEUtDgJMZEp/ilCeZO8XWCiIMJtm0Xrk4TXRBznJS60ux4VB+rEXukN+NMYkqBZg7uZ0qJ7TAuPw5uV/1Yy1Mj7wHLiAKfbJo3bw1AnMWSt8nAk0T4ZkYCY85FPjVAtozU+6F7TFvBq/Pj5DqVR6ctqyu+0lEJ494V5VY/xJFD4kaT7jr6h1aysuLrYi5upcnklNwFyAbTUo4guYjyHGkbZqwDhFK68GmKu+cIFnS5215tnSq2MnG0S01n5oz0mzWdFaNTr1ocy1N1U4DkAdX2lrYv5KOVuuX0ICEUwK9zr3PH3yfQPmJBoNJR/LPqb4YZ70f8kkqGd2cuh5tXbKZT2gD0+t+P6hdZMySJ4OzGktDFitYFH+vE/uDWW7EVtg039T5HTm1egHPqQxpzWnEnU/S2MsAgvOKz+hzSHdr0VqcZEimkla3Mcsl917Zn78fygZ6EuKzl6KbtNBpBjKKT//g1zZOZVAKmSUvNPSU9e9ULjmBFJlkvY6ycBHmPUCudQv8gq0Lr7qpSJfohPIpIpqvedJkZmkbu1S1FQ+VSLtIEWMnBRQ31xpU5GYLe4jmbhPJOGkmvpYdQT6qVpS5yHFuWwi0jwraZU6L+RSkiik6MJjU3t1FNqXSosZ2YuG0r3v2WtL59OzhgwxY8jjv+En5o+CSdGisGtSqh5lFaN+4D9eWPR9La+DhE8dOs0kr/PIveX/kjTPE1sM3aR4Qj+UD68PeuaJsjblg4D+Q7RO7wvs9e7Ioszn4tiGKOZQSr/NbIGUX3C2OApcNpP3lk0S5y3AnA8KDQxtGnChK7W/vTfvXka/3zBJ+yIQof0NMWWFuAA1mWHKt5yACWkMKUBAHsCXHNufw7xyPqaA3wDMSVNqFuaOM9DCUZRHmSMJpNDDO0+RPgEJ9KBvJFrlCSKMvvW//t0OrFtjxSyQn/j5L2z0ffdZj/597FR5KWBMLpEkCx2g27hlky1ZMN+2fPmZ+9Ma/OCD9sDMR6wr4J98w9SgUaSJRQ/nPIAUA4TA7sQ19A4JmFuzxhYBzNXRnj5jx9ozr7xiPfr2RTsNYI521aIaHhcbqlcLagEQHpEN2gKHcnHon0fABWkV6ZnO/zCxUN77QsNJQxo1OQAAQABJREFUiWw6lx+5AsDLIgrIhFXBFLagsbR20ULbtHSpa8INQXtv3JQpNpZgDyVosCkqZh0V19CBAuca8bMmM2CPlko9iq5ZjtnjF+99YHtXrrBaInPe++iTNv7+KXYXYI+AlRBwATlT5779+2zdqhU2/89vWl6njjYEuT2BD7semFLmYYJZAU/yKyeNvuoL5bYe8Gjp/Pm2c9Fiy8VctRemlI88/rj1GzLI2qENxpsLfRmCA0gOkncE5qRRVsHLylGi5xYCIHVo397ao0Wglx2flHmx5nOJv6AImMMgE00xIot+/Y2tnTfXDu7eZn3xNTjhsUdtPJpi+SXoeUFfoFqlNBMYN3o58hckgWX0R6G0C5GL/KlJThJ/eChovMIuf9KQk085aclJ87IMmckMdPYH79uRnTvdAfhINNTGAMwNQpMwn3arHwRU1clcF6IaW+pTX6TChzRGNxNZeMU3c2zXHIC2wUNs9MyZ9gi8K8Kf8lbDk/Ln8yeQdgFmwV+hqXYM7bwBRMUdByB2L+3s2La9NXA/HN633zUHl3/+CX7r2nIPvWIPYd4sx+Qab5KvZClz4BOHDtsHbxLFdfNGywNAnPziyzYW2fUbOsjOy6cSHLhvPGSjSIHSmMurrrdFc+d7u4+uWkMAigk2hsiwE6c/aK3xs1QjMI+FdtNDn/HH/S4twQLGSL5kDq1KaYpwP+gBq5dZv3com1eEVyX1l2SljriDt9A8xiIyUB+3aMzd+M6WplwV943AOQFz5dxP+8+ftJ3HDrrGnILdtObDRAnAiu43jds4dlPDjwskp9KVLy7k/Jj+0/2tY213+rgNrbyzf+OHA+9YOldjIaZpfASATU+JZCMtD/cD+rCg542PBo2LeDlr7+OHZ40itSrCdg2mrdKec59zzIed25RYH6K1DuvRj+itesqhMc+v1hT6qJJWc9PAi5Vl71MDOftCy/nVSsBfWulPv99199MP3ztgLkxzfkv4PInwfBwHoTSJUnOhp7F+4LkmK4M6Pr7zwMd/IuA163dt0pTPKSIOMR9PPbhK2v3SROz6jvy+pa/EjjY/1z65J2DB10QC5hr5oJ4LEF7AWshf/FnvuYUDebVmive12NRfQsLpZp97on5ixRym54/1K9WP+dF1px2kqtJhE430wjHd91ygUCgXLnjWJH9a9dmlOM+6KjqeC6Ekx6HNSfUJjynhpVNM8U9/J+m6Z/QwFE1PS6suKCYAiCqvLroQksNIIcWPMqVtoutbkF18XqflyDh0PjLyh8vOnpMK8ovyCORDHW5dk8Zb5FMUQo+pFHlTPAXaF/+qkaGeKB9/x0hOfCcyypVFK1SfuR6O+Xxeoozy+D2Z8JoQanpmiSHoxrqde05UlfgXHbXHTZhZEwn88napHJu3NRZW5Z4Wf0L9yhU350u0vYKkQFN2VerZRTe2LxYXFc0bdbzPOE88T3V/ypWRrpWjad5KSiq4VJKFjgh4DeydVmSDxHhIsaaNdM9PStM+vbVNWW/dI3HeAsyFHkx6UWNNCbFTb93OuzJn3r2MXh/AnGh/WWDugan22IvPWjvM1uRLSoECfHKVRIJgLr4buFsc3IB6ATeZtI4EcBQAXuWybyjHETcO5edhijj7o/ed6Udf/bHNQHuoY7duXkdFbbVV8xSVo+UiHp5yFi8tsrJTp23JnLm2Yf4CO75ruw2eMNkmYsI4Aq2f4vbtHPCRs+VTx0/Y6qUEilgw184fOWD9J06xkY88YqPvHWttS9pSJzc3NL398OhfpgGA9LIsv3YHAac2rF5tC/HHVUd7euPc/6kXXrA+AwYQYKHYZVED6CGzST3UpZ3l5o5MKMcxdzy4b5+d27/Xug0YaH1GDsdZNLxhyhknlTCWGFPeEckkDB1pBhaCRRVUYx6K5pqAFwdytmwmuls5zv0n2wgAuSH33GOdunUBYAmgnyLS1tA3rjWHSWsw1dQE1ug+9+ouVKABuMbWz51HQIfVRDDtaxMefMjunXyfdYZO66I2PubPniG4BCa8y+cDeq1bbd3HjEFL6xG7j3HQuSMaYbRTZqw5mDhWoYG1cfU627h8he0AcKo+f8HunjiRIAgP2Kh7x1hJhw7uO8015eCjlnKVmJbJtFJaXJpUS6Fx9PAhAm2ssDrMjztjojlMEVY7d8FkiHh1GktMyqpXfuPOnjxp+3fuscVob+3fuhmtynobiY89AUxD77nbzWVpvQNRpWfP2uHde/CXdwjAjhefQYOtL/2XJ606eFH/Sf5ajMj8wh8KPCD0OJBvPvnoqymrsHMnT9vuTZtsCz4Ht61cSTTTdpgRj7JJ06dbf8yBOzFmGyFQR//VMmZrGoAhRY8Hi2tTUJcDVYx/j/a7YoUtx0S6FrCsF5qFM558yoYAkHbu0pXxVOP+2Op5SJ2h3sUAeIu+/Nxqy87b+CeetIloSfYeNMDa4OdI95Ucii+nTxf9/TO7cPSIDRgz1sY+NM0m3TfF2uGcXGO8GprHjh6z7YyhBQRbKT11zDr3G2APv/EzGzJmFPx3tXIChmjxJJBMD8UIzOUCzM3/8iv7+t137fSWbURCfsgmEHxiFEEm8lsXoXWqSJdIjH7SyyvdDP+k8aCX9mUDfXYBs+udW7daOebOhYDpQ0aMsGKNDUBSOViPZi2qP25phzHptt+HNiEn+qQFmLs53ZkNzEljbv/5E7bz6CE7dPo4Hy3yrS3a1e2Yj30CYsxmL/Q1D2goa0bQpjlU83Vqzo7HXA5pTXvlb9luLwmEXqa3vTO9970BTcCcRoLGRMipoFRMrMyVzHvMXyE9jIRISwT82JPD+NEoka9NfYhUMCE9z/XXwNwrs9aumLIO7dEX33MdrC0fLIpw26B52H3mRnoiHAedjpvb0plo7npL2hUloOem/5FTvdcCzElkDCzJRTJJ/Sg9yKiV1s+sn7QW3s/HS1m35AJCSzPUfcxyr+Qyrou7d8FP7TgrbKt1+I3dNPTjfRruEzGqu5cNprW2kc9grWGVTx8J9VFerfIAZLq3ye2AhdYmtMfbGohBo+nmikfaK0/4C+VFm+KhXt/H3DEtnGuMOSRCeU/hRxz71lQknCZ5Aj9JHu2SfCoX/2LRZMpKLqQoJ4XVNvosZo60yKacXpafRHpeT6CgOS9Uq/Iq7nmcMWRGpnSSIqa1ndoZbJQicVVI3sgkBZ2E1x6upf96HV5vBnWvLD2liUbgM52GH4s/0eFH/GvzX3507pBVvO5XVUVTDcoRyyWXL9qlcqsu/ZFDdbqsOfDr1KVXAweJkzbr3UUZldd5olyoTwc6CpueO+FjUZC3roU/XdexyCT1eGXhWHzncH/y362qHIiElkDzwKV4FA9JeR2LovPaVH/CpjL59QbufbVDmytdQN9Z4v7xA78S2uX9rWL8KY8OZc2zedlSfGWflUCYLwDm8LsqX9zyXT5o5AgbwHuhgLnUR/yET6cTyEEw0PPq4o9YSI6b9mltiflu6b04p+/YCVtxC64TJ42IVDZ+wBDr37Gbrx8KuYboffN30Nh/SVroreTkGnatysrKxMkN33hldHMXOYcu5/gwL7xfrltqOSVtrHPPnvioosniXrVr1KR2t1tHOusX/aizomDjhBGBOXV4IYDL+X0H7df/8j+tCiR9yIP322MvPEdE0y7uv0wmk7F8mMSoIo2mKpT4QtAC/IIBTlRiclfBn0wz5Pi4AQfI5SfP2JK5gA5ff+E+rR585jm777FZrgFWR70KEqBABm15eBcDqqgvFGCgnq/MJ4gyuuyrr2wZwJ60wkY/8KBNQHvpLsAqAQunTp20zRs2AqQswWRvg7UlYMFwNOVGPzTdBgwcGDSIatBMglMtGOT/pQq60pCSppKAuUMHD6KpttFWff21a8x1x4Rv+mOPWa8BfNEmcEQDIJuAF9WnLZ9FusCjuqoqNMgIgACwdWDJIrsb8Gvc08/YQPzhtSbQgk8q5JcM4zM+ylN0CrSwqQIQOnUu+JRbtdpWollYyVeDbkPvoi9etEGYsbbHjLAWME48gCw5+FIDKzJlbchH7mgzFfOn+qR5l1NTbxdOnLalX8+2pZ98QqjlUzZs3EQbh/nnEHhriyPretq/b89uWwGYuXHhfBZRBTb8YeQ2c6YNGjrI2hS19q/3aqv81u3fsce+IMroAfz+1Zw5Y52HDrP7Z0y3iZivlvDyK00qPYhbsfCRGrLAw7LqCisiaqnqk0nQGYDAPd9+awuJ0HsGMLSIl5NJsx6z/gMHAc519joLC9EdoB1lvMTs3L7TNgECblu8mK88tdbtrsH24A9ftsHDhwMwdnXwT9py1WjhHcF8d938efYtE35x1+52LwDjBMxeC2kHS0W05tDkBCD0r6PQ17jVPeH3AQu2uvIqK0VOe7d9a2u+mWPr53zDC1UjIO8EG3n/VBs1eqR1QCNQDxAtPNRWAXPV9YBrPOyklVlC1Nw8AYtQF8Bcipz2frvd5r3/oR3C7Jcnot2NKeyYCRNtMP2qR1sd5q8V50pt984dthHeZabaof8Am0qgCAV3KEDbRw8316yjLzYS2GMF7dy7bIl/+eoxBNNjNOd69u7Dgy3PzpWV2rfbttqm1avsED4AC9sWW79777WHfvAymqYAzTgyr8FJvo9H6EoLtHUOL4XiGjPo+X//3L7+y1t2fvd+G/PELJv8/PMeCETAWpVMdaU5ooWCJKjBzD0lLdl8aJxTe1mkL2YMl544Zm27dCHgxfM2EBC1PXNKBaAhsL1WA17UOyG5IdQfd9IW2oOcmMsiMFd65IQVYJr9+PjJ1rukHdEZwxj0vHFi0Al/6ad3klxuZFsEzFUjqErtkZiAuX3nTtjuY4ft0BmAOVwYtEUrty2+NbWo9MUr+7gFUYd0pemSlgHxqaf7UyuB1NydFI19E+m07G8fCXgX0oGhK+l7OjfpVt/rzM+TcRLWPTzXdO5/Kps2ApLDSKNJEowbvQAx5+vlpwqfuHrpqOS5lsczS+Bcj7YdrXfnrtajfWdrV1hkJa3yPWKrnuGiF8HCJpqXOLq48ktkbEluTgLex9z46spwvzevMddIRPVcNPDz6atc1gF649V8EUpqVFyhI+JEcoVsF/OYNt4uvnjNKYENxrsOqMLPOfFznblMNI65yDhOvQOQPVjG8A7BmmTZnLm2iI/apUSBryHoSSPzsNaBeXzM6znibnv+v/9369S79zXz2VxBvzdgMblbQx9EMcGvPu4LaHMAgTVOSiuOtYdvaqQfq70hTYCFt1XlgzRCVlY72pRLf8oTTWRVNPo19ueLMqZtkY53vcqJU8naC4aMOo6sx6KqR+tIr8v5IYFEcaZ5JT6b1Fkq7wAL15ySKtBfsqkq7zvkILqhbk+kW+GHf/pg7enphZTZSyS/kQ+lOnnPIKZ8C7VDjQq1RpbbnzDkQz7xEempUGBRvIby6b+BR+VO6vBMamsTDfEdykYC4XosES+rbVqzqs9i6dCHwWzSP7pAOFJJ58PTJKPAbPql1LF49BGiPJ4vzPlqQ/inrGHMKLiQaIlHB83UoBTDoXiqrqROpyNayitKMd2rE4f6C9clb+UTP25BxHuZ6qkFNJe8Io1IU9e8fpVJ3m99TDBWNMbCtXCgMqIhTVN9cFLbpHihe8sBR8qoTpURp/6OIJ51n7GPdE8dPmIf/Ov/Y0d5F6xFcaee94p8tMjzsESSW6pHfoIP+pdeckxA71YaTyLoY8l5ED+hjuxnpGQjaaRvSslOS79+6x2LW8YQuwjMlfFu2gpcZVz/oQBzXVuAuVuv066OIx+IDGbdXOx8ItU+drb2KWDuf/zfVs0NMFjA3PPPegCABrSC0KsJNyflIr3mBrkGj7S/8nhB3yKQDDBFQQJaoQlmlRipYmZ4eNcu90mmr8c9ADl6jRrBIgfTNlTL8/Fl1oVAC0Puusv6Ej20Hi2cetTjGwHRZOKxdP58W/DBh3ZqyxZr076jdRo40Lr1H8CNik82wNYze3fzNboM7aa2NkAmm2PHWX/ANUVSUzvFi0wFz+G37jAg3LebNuPD7ix1VLtmXzlI9NkTx+30jh20s4GvfN2sz6hR1rozWmNtMDVB66rPwEE2cNBg64QmWZvC1q7Cf/zoUVsx+2uPollx4oR1GNjfhkx9wJ56/TWACJzlM0m4Ly+fVCT9sLksOSxEZnXnLtiWlWtsE5pQW2ln5dkLlgcA1In6uuEfT1pH7sMMQER96RGUoNsKUDkH3vL4GwRQNRjwQyCY+kKaink8GdcvW2YLP//CDqHlJkCpY99+1m3IELSfmACZqE/t32vnDh/E51uV9UIzb8KsWTaW4BLyyadFgTQfq5kot63faOsXL7X9K9ZYGdpYBfiRu2vcBGuNNhkhVXkIIzX+ZLqqYAJafNQ30oeYKHfo09v6DrvbRqCFWMyC9gzI/zcENzi4br2VHjmC7zI0+DDFLOYFuhiwQnNwLRN1ZVm5lZ0+ZRdOnrBygif0QitvOEEX7sPvWjuASp/oqUead2eInLr4m9l2gOiwpxhnetCOeHiWjZn1iA3AxLaAesUTn3dSCxD1hh4c+tMiU19vDu3YZfPefc8OoDFXceqsFdAP7YiiVwwIKB9DeYBTboqpBQ4Lz1a0XdGv9MW1hLEyEtPPfv36WacO+GNjnAuQqeZFbAtg5hqA6W2AaQWFbVwm7VmoFpS09YWs2leGdqA0Kgrbl9jImTNtDCbbg9Cwk3zrAJaljVaMVqHkt5OAFAoqcWrPHtqTy+J3tHXiQ0MhHxzKoXF63247u3+P83fP9Jk2avoMQLl+aHKWuHmvQEUfgxIC7S+Chkyg6zFVnvfhx/b1H39vlUfP2IQXn7EpL73ogUCkKSd/gwL/IjCnh78CdaidAkh38KBdt3Spbf70U6vEX6Huo76T77cpmMOOAeCsQFtEIKkWbfFhGx+szorYuUO20B5kw716WWCOmzqj7TrhL84RGdfuENncqGbIx5zAuUqEJWDuQgLM7TomjbkT1lCU55qk7QDmfCGa/vJBGck2vNAEjuLCky7zReD/z95bR/l1XXm+W6ViMZSwBKUSM5hEFhhiiOOAnTiZhnAy073WQM+8t+aPB9Pzhv6YNT2v+63p1cFOdzrkxLHjGGWhbUmWZIuZmUtcXHqf7z733N/9/VSSZVtKbKeuVL9774F99oF74Hv23kdfife5cWIYgnX8fsRLwL8pr//wdakPyn1nWm5wqc510RhCb8m7nPxGhIIr9mNZZ7UeAQSi2MaCrJnxUMBcM/OhTo2a2xRZVdce2J3razX9q62nToDFDciH8TdNLmXFacek/Z5wpoVPx/W+S8AXc17P6nf5R923p8p6tStzAFSsSpj/FbER9/EA5lRsoc17KyLvsTWFpkZ56EHlo4eknGSuRoenFTHuv46U/TJs6V7i0DLXsNC8iDm8DikbNGm8Pf6v/w3A3KD3XT+FEeO3Fj5RuHWexFsIKf7j3A6GXGpOpjX84luRBJJU06Who2cBNgJMdA+gR/hmY6l4eUA7lovS09ze01Xa+hMo4enrPeGDCE5DjCqyyjb507ij5/hxexSF0QUB8V8Kj9pAb2YdJOBDc2vFkY3muK7QfExqgK5CTDylJ7Ja9+hSeM1PtabSvFrzUvHnSSss+XctEuhobpsdD33so1CdpogpIn9eRpSVyltO8fL88KL1iubaEibIeOOj/IbQwT2WVX4oBRJ/MTA1FCI5J8HZY/CT1ldCQjyUkBcBRF6nMCipSfW/ql8vD/KsK6iykmfKT6CPkwgJu3/88fqFTsJ6cFba/FPZ+DyYux9eQFpqCyIWs+DlFwrcwytzgRZ16tSU3/gnalyiL96pLwe9kvSjOYUYPgbW+kb86y7qkmST3WcBco2MO0pHwiQpAKvAXCF90vKHALypXIoRkFDbuMrH5nRJ38uBvDkwpzwm5SlenFBCUzkI/AUzRuLJ1xzQa2Ytc4o15ws//KGdZBO/gbV3w+mz2CaXGShMBPG9fuIrX7X5mLpq1dpSPDijIQ31zYHXhHc8Yx4UTNzCat7lbnkuH/YXZaADmFMZ8BdqM9xUlR/ty/mnxcZ8xMmnOmyBN0V8V2VIpUli7u/+r7+0Bj6AkXNm2kOfAphD/FxipLIp5YvopChi51XY0GVPqxhaV1ErXQ0A8RZSN62oxbX5aaEANEjNNaCG13BZu2lI6FQBxCBBUyQ1TADAMgCD/tgqmwQYMXbcePAgQA14lLqoDio4uGe3bX17ve1eu87OAQw1svusXcs2wrWyGBPq2BtAZAhqpKMARwYAQPXEOH8RA1sz4JtOLK1kh/rMKSSidu6yt5YsBrg4bI3wpNOkBFLJDkxz3XnnpxSAr/tgJJDKBLwwkKGuOBI7b5Nmz7WaocOsGye1SU/+HKq2r7/8kq349TOcvHnSJ22Dp0y2J//lv7IBw2uYvBW7+quXmzovrlAf4bcE8aHLqDAu/c0Ltn35cjv2zkbSIt89u1k5AKBAOfU66hB9N4PyUNm3NNS7va7SHpWAOxU2Zg4SXTNnu5qiJNw6CYiEzkl2MPejYrCDQwOOYkdP6oWllV0cXBHNpiuXkHbrZVUjhlstaqnDAUb7Dx7oBzcUw28bi4fjnI67AVBu05Jldv7gIdQsL9Ppc8JcvwEwSvkqR/TEbZSR76LQmWoiIL7b2pqs17DhNmTqNJuzYIFV9e4DzUbbs32n7Ucy7SAnzp4GVGvCDqFP6CCluw+oDEidGGDL4LfPwAFWO2Oa/w2uGe716gc+4N9G3dWdOGVvLVtue7CVd2LzZuqz3qrvvMfGLVxgdwLUdkWVzdsxbdwHDJhTdfhAxYOAW7WLTavW2KaXXrTzAK5Xac/F5V28TjtjN1A2SoI6E21CA5KySNsFzvK66DFosM0ACBxN+x3YD9uJTKak5sEohpqqJPG22XYOgdjL/QoHZij9UuzEtaLGLcnLYtSLu6EmO6i2hu9gpg0C5JWkpCY4cddXk6ar1IkORtn81lu2Y8MGO8LgprZbSlspBbhrQSqtDenCMiQ2B6NGOuaOGTZswngkOJFEZaDWQMsWl6evSlJLlP3AEgFz7F4tw8bda9ima8C23x2PI9mKBK1UeNuIK4m5zoibXw+Y27d7t6s6v/3MM4Cux6ykW5n1mzTFZn/ms3Y30pXNKg7SV13o0Bdu/icm9PxxukJ+mLTp26W9SG0+Ssx9YvpdNpg2WUEZqB/Oy3tSKKqXPPePU+HcorwImNOfgDl6kADMocq6G1XWg1JlZfOiB+NAd0B/fffpYihJXy0wW8YKo3KP45vuVA8VQSj+y6/j+niUgNc7A3NsAeoW46Xqzn19tAL5uVu4h1aiMAUX4WIw+Xg7YjzTotDbHm5NLJKaGe+aMZvQQr9diY1cqbPW9B9kA7uyIcfGDYYmAOboM3RXu+WepesNkbQ0hstDC6iO6/2XgC84KUtVsy9wKXNkvlwSF+Vj23Jkn209ts8CMBck5oqQiNckQHG8HghfsHS/lqG8FeW13td3USq35wotJ34FZCk0/kxiof17HlVG/jEw32dMK0ajQIdWLX/hRVv6S8zU0FkOR9NjWG2tL57V/1Yy16/FXm6FpJY9G/pRo02SSN30nnFPvAtv8Tv16ElwZ8nrD2L8F6glYBuRLatnrXAeSf6DbLrXo02gA7t6Vg+xfoMGYU6kLzbnmNsRNCctF1IM7MVf7s5nwrYSJP8qC52+fB7VPB2KdpkDw7SmaGUDko+e5gHAwVpBc7Eu5L8f2jwVSBEWY/ajM+BVCpKEJP3X8wfdVuakl5E+rDt3zurOnMGszynmtVhTpS+R1kER870KTIT0Zd7ZE9vQXZE4EhiTjnMqB+qyhfWRzO2cv3DOLnMIzRVMvgQ6SBGyhtJ6QLxVVfVDspx1B3RlMkdV4RJOSot2LlpMfK2J9VQD6w/dz8HbOTa0G7DJrHmsIhUzB1Vdd2Ojuj/5LefdgS/lLhSnnkL70F1l2c4V22G2r81ED/EVV9G5u4Skk6JNwq80V+R5iTwfOXjAjh08SH97xe0M6iCzEWPHWSXaWQHoorelD/Xosf0niXkS4ce5jO1PtPUsAQb1z1qP1GGO6Py5s3YBoY/LCBW0Ul/KX2dALkmEVTIP6d9/gHVnfVeBJo+XqVOlaKg79fVSE1U9XWaddZH2eoaN+IaLF3ysUJmoPXXp2cOqWGNoXtMFoRZp62hOr3m11k6iq29AtqYPHtiPSZ29rMGv4MmaystLRcODt2Gf4VDJ1DHruc5qD5TP8NqR2MLuRf2hIUS96xA3gboCha/omzpXZ0eZ7zciEONAJCTVnioxqVM9ZJgfOlhOvsu0TocfzftdqwoeLqEhtG/TRjbvz1tRPTyhrbR94wbbhi15mLeHv/Z1W/D5J60FfCAHzJEAdNoD5pIizNzIW/jvbqQc6jYT4sP9GCopYjVqyR0ScyqTD1lFOkuZlpTtoDLOeY8eh4C662OMcWJlp8AcAJWAOUnMjZx1jz38qceCxBwfhVT1XMIllocmgHzAmrz4xU1NXsCcTjhVR7Qe0GstYFUb0lYy5H8VW3GtgDFFDEZFSGL5JUBDtPSxkobsV/WeOMEm3Hm32w7TCZNlSHiVa9Bk4JehyAsMANs2b7HDu3baGdQWmxkABfKwLWCVA/rb0IkTrQZwb/iIWqtAXS8CGg3sTguc6wYaf5GB5NCefbZ+xQo7e+SItYgGfErH3UVw4UVgkGxjyE6ZwKbWhst0aui9z5yFquo8GzF8ODZhuji4qcMa1iKV9iZqj+cO7mfQO2M92Rn87L/7dzZ0zBhONa10MMO7PvIbSk15DsWgyc0ldguWPf+CHduxg50D9MjdT2UTwviv3LRDoZ0oPFroqNXZFwPMaQdXp4WOBszpT9oywn9VeWJnQuBcCx3yjs1bbc/WbXZ4zx526y84Se34FjM49B+Fzvr48TaaQw1KXe2TXVAmLzqdVMDcgf37bNeat20vUpAtADder14nDQB8TDCoO9WrbOdIwtFVHYmv3T1Jk3VhIBowbozdPedeB+Z0kITApaNHj9jeXXts964dqN2ewmbdeeoDhXPqQG3MQTkmHj2QBBsB2DVsVK1VARq2aReRwUdgVZkmx+S1nrj7d+y23e/A59vr7CwDcc/aETbsnlm28OGHrAcDjO/dUQc+4NPBq3i1y+iDIQOYVJk3rlppddi0E1CmcBrYkgoJ5S9Ai7+rmmCjDkrD8XqSyob4HD9njtUwMe3PJEcDtSYLGiAFzFxhADsG4PcW7eUUKsQNqPUWJRJ4Uovp0n+g9Ud1unp4jUv5VTBAaiDTQCVRcfHbSDsuo33KfuMlJlhb+R42o0p9lm9C4LMk+AQWVqAW3Bs6YwGp+1UP9gHcQWp4amFglK0VTQR06ftFBs4lXgVyvrNkqa1++UXqop78APjOn8/JxtU+6OvU2SKp6mqiBkfiSQC18qf7WQBSTYDfeXUR0odbUWk/b92RgL3704/bXffd523FB2e++ywwl+2fnKl3+VH4m7pg8GaD3hS99xBIZaPGcQ0wd7HeHpx2pw1m8lpJHVwPmHsPSf3BBo3AXFRnvQAofeDCKdspYA7bihGY68EE1tVG4vfvNcMPjSPUk4pQfUNoL/omdKnf1pOvp+WZvPvDB/yJ1H5f7fMDsv+RjZ6rb7KQBeYyOQphrlNDtIMwkrdTc4lTTEOvGqdaGBs705dL7UctSvOZBsbly4xbAuwFzvVnMTS0B5L63XpbD8a1UtLRaK/NScXSX3pBWMOkaOkKcyl/7Ph5HyWgxbP+VKQ3AubYkWXOBLDCnyQ8JFkiMC4sTFXrcY53HSY+tMCc+A39XxjVs/yHsollpLmrcqq5aynTnxLAuWVoQCxmM64CbZZ7H3rIZt47182/6PCd5mIW5cxLXEI+aa9Jap6I97WhGTvha9PP8hKe4/fld++09UGEP83jdYCKNGR0kNdx1OZ2YUZkw6JX7MLxo1aOEMAw7PKOnT7dagEfyjR/ZR7oVSMaurgF2nrRU5Ii/j5XwV+b0I0AVCegfwjb0MeOHLYLJ467dodOYW7TXBjzHuXSBunT23qxuTyGjVIBgt0B0gSmyJ6yb7grGdFO5lEtgPcXAC32MZc6jFbJqaOH7czunb6+UJ60Xqrs09d6DB5iQ9l0HUI+BqCB0YXNVwFS4lb8SUL3PJu4m9avs1OAc9IKuXjsiDWiEif/MgFx2HnuDsgzbHiN9Qfk6z2gn/Xq2weJwhKvLX0TEfDTfPYCa6izaHecPXEC29wH7fgBtG4OsmEvo/2UYzkbYV3RMOkNbxMnT7EBzD+7MY/XvC/0U6EsfaNc2fa/pNx51uUhVAc8aJYafUPMGEd3fFRnXnYKGNZx+h7LmVNrXnr00GFb98brtvWN5Uhn1SEEAkA6eow9+MTnrU///i6kIO0XaV+pDXhanlBITe0+puG8eQD4grb+JOQiwOoiZbp7B2ssyuLU4UN2do+kwc5531DG4W0VgMDavB+B9tAQNt37sV7QoVTKpNJwgBB6Msuzb+ceO3HsqJ1kvXAU++WX0OZqBFjVVc431m3gIKuFzqBhAMys+fyAQ/qkq8wndcih1jRqp7JJvebNlX5w3iXaUQuaUVS8g75hzUt5xfKjfmQHstsA6I0ZbXez1h1eM5yNTcweMUY5KMe6qA6Q+yR1fxitnM3Ll6Dpc9L9VHBlaDypTU7AVM8A7jKJVUW+tdYUwCbtH9WZhB50aIy0unTwYTnvL/38F/b09//O1y+PfP2bNv/zT1gLBxvGdhI+Q8pK5RWqJm0nXjDX/JAOPPGfK/t7TcAPoYP4pU1wU/vqAOZUHF6H/sl/KCrM2WmHk6RttuOTc/K4CsiDbvpY1dGpwvVXyqB6/sAh+87/icQcDX7UrLvtUSTmeqOCJrtqsv3WIJRcNqkUD8BFdro0efGLm3cqFFocVITuN1xAEo0AcQGtySfkvWNWzPBHGSuQLnUkgGHaWSqjg9GlwVIDQvi6mDS1cOABHYOLYwM6uWFlPm6nAUBUIhUDgKUSACsX+YaGatFPjaTD00mkEgtuYmCTmqSDLgByuamUZwZeiEgnLeAi5lODp2xzVbDrod0IARk+/CERKLBPHejunTtsx9trsSVz0T7/v/3vqNoOhQZqiAyAzmPMq/PFD5fqQCfDaoIuqT2p+cYrTlBUbiowLz9njnf4pvf1wU4dVSkqjGUMyiVIdnkyGjQUR1mi3JqZQDSq7ADE2gQmKZQCki+dIltCmZeq3D3P7PDjJfBFC4NmVIob61G/QepR9ej1AX2/Q8MHNJUzbUD/xKg6U9HnzVU+Zb+uEqBJIuaiqwFe9SgR60boq14kWebgo0gT1/ehtKsIeFVK/BIGWqnY+uEBBNA/0dKpd1ehJZuGlxkcD+3ZbS//7CdWzCAzDGmxOQBLXRiMdXqqt4u4OIcH3wmER7UR7azWAzwabSvygVfIk349f3LBTfnXJT4pMx9U4bOMNlIOAKzDLlx6UMWhcIpLW9XJsJdJQ5MrN5AcKpVAhKJstFOpvMouniYxYTIrAuFyyQvyKZhO8bVzKXuJUsl2Tj0x0SIE9Rp3PwMQRxz4cBWMmK5i4eYAIpWuiUY9u7T1HOwh+hWy+chJwJoIyhZdM/y7nQ7lK8lb/O4FzutUZqONNbIjthrpxbc55KO1qd4mL1hg0+bfS/kgtUe/okG6jU9bdRj7osC6/ya5bf8W+5T2fa919W/vWufb7hJykgBzPhFBogsbcyUczHL/5Bk2WLudTKK1gEhznT7AXvb5tnP70UtATY0vlUNwaHI8y86cA3MXz3D4wyE7ADAnEF+7vu0Bc8qxijj2s/7OdwGp5JvXPfnL1IXcPuiVIcc3fisoflCO/nDie9mnRR7GEa/oTBFk60fOaXC9pJ55rvIJXtc4a+zQPAN//agPpi+VeYIrLJwakJrT6a2lLOJ7Y3NVwNyoAdVsJDIO0NdX0NuXE1Hqrdmk1f5Txpx4SNg3nvDquG6+BHxsV70QRRt4GnevUWU9vs86oZ2gTbhipEA6M1YzSCcdhOo4rZ20Wq7hIIbJBb0mSPsO1zSq9oO9T9fITnttR2WjDJFDnyvEzJUwppX6n9lSbMouevoXFEexLcT0xdxHHrZW5qcC5mRvuFWLck8kpOTfQdqaRTcwnu/efmYir5D1D0rvmtNoHqL5iy/4oS0NnmbUxtesXGVvYObkJBI6ra2N1n0QG70LMO+Bds4YwA0WGT7vddBINEULflQWYktVFtijr6CBKD3NWQSs796x03YwzzmIJoTAC6clPyTntL7w+RYCC50gWEq76VFdbdOYC02aeQ9mRboFcE7zUWh2Yi2mA746sdF85MhhNtM32/qlS+0iknjKXwvzKEniaR4pDQ5XwySe1ki1bMprU1h2i3WImlQ3dbrl9k06xGy1ndzCYXJIXykfWpOEjoh02eDV3FV3mXPpVTPUhnJA2N3Yyu4DqBI2c1lrqWyJLG0cme/R4V4b4e3c8WOsybQ2QINH3wVaHwIknUfCSnLuzgcftCn33utrDK01JCmo7yyWqz/HRiX++NOrlwm/ahPqP3W5nz+F+O6qxpPUPwWlxuBrCalaax26CU2rTa+vQJtmpccvZ9N60ISJ9sk/+mPrQ1sQwK75sr55bRYTuyAh0cdJf4mX6lU20GUySHbUT2LGaC+CFW+9graNgCrmzprjOynal8pR5ax5uNSnhwMKj+UgvvEAtX6gH+sfbb63IEl9CiBx0bO/sSMcdncZWkXYXdO6THl0tVbqSbxovVaF8EfN9Gkc1jfHegD+SmhA2mZqE6qvJtrom0uW2Cq+zwsIomjtXoK5o5YGBCygoxypH5M2kOjLtE5XAMOqUSPtntlzbMiQIRyc1SOkTb1eQCLw7TVrbRdA75Htm6n7K16PRXz3JMdaDLVbxjW1k37jJljNHdNtOiBdz769OaGZA+xcMIfvnbLTmqOYZV8ZSVeyYHwJVfinf/gdr++Hv/4NB+aaEA6SeSSVd/g+efFKCN+MF7ASvs6lPksX0fWbvOn5w36J4fDNdQBzofY+VJWYtjk1xuyVeBQ6Z4PEZwX1tpzEiQthDWz6KATM/d3/8X+zsLlqoxisHhMwhwSaAcLJxlw4eZKOhmd9+NpdCN1kkoLKjf/q0mRvKvIk+nrzrs79Ax+5z4Mn9bq6oJGw5x+6x4yEkjDxNUTwKJk4wVd8RTrJsEoHEPyCKHbWPwTO0RU/IXZygxa+pO8i53rWjgwDrRBs5P/84AjtSp0+edpWcirsbjoseiZ74t/+W05CrXZWdSKoLmUj8uYOctOfO3pK7qJXueuKd0VUfH/XM34ezUPxjEP48xDur7rwUEn4JGgaLykWd460dFdNaCIQxaFFJZaHU0++kyw9n7wpbkje0wi0xGt48skT/qFNhNgxXb0lUZ3l8OaBE4/QPgSUasLk/0jMASWI6PARHTyiU1/3MEC+9A8/sFJEqkcw+M1i97YrkyC3d0GjVDmKqHf2vKidBmnJhBunrzC5K/jk2mhor4E/n8wqjgj7f3EnmmEAFRW96xId/4sO7hp+AnX4Eg1oJdxET95x8bJXWgnNGEjuMQ5+0dkje1oxQkJCHomTk9TEh4mMgop7r288BGS6tB5lKylFTTiUL4Vi6OcfYZOJEAg0p9sCBjIpqL94yZYvfs1WLn4VddbuNv2+hTZj7mw6nBKfpAvm18m2ih/7o8Cm/4q7dq/YDtv1vJEjZPPK5EZhb5FfyAn5o3yK1BYoNgfmLlyxhZOnWTXtswsbCpJq1brFr3gv5OF67oXh/oDe1W7bB+bOAswdtAOnAObYce2BxEJPyjqqDfl3mimntGj5fvScbSd6zvZpHiAT930/ponw4AykDu+bZEfE91YCoS9R4act4L0RuE7o/D4qGdfTOqaevcPV2hGJG20SAszVs4DSIUAVnMxaVdHNxgwaYn27IH2ALdcu9KldAIAEzGnekdJXk8k2myQrGhs6rvdWApoL6E8jm5taoE0UAnPbAOaixJw2MiUFohHM68DrVHMKFX4YydvlIHYm77mOshXdLuUP7JhjiX4w95I011A+Ps9LWBEwJxvVOkBt2fO/tVcB5jSPWogK2r2ffBRJORbVjPEB8IA9pxkIB/oxEdEO7Oe750XJ5Y+w+gZCbB7Cf98U9LEWwKFUgCkbtkf3HbCVaACsee1VawDgKmJTsDvAQ829c7DhOxOzOeOCJI8G4DgIQ0+X04chVZk78RPSDXMWHez1xmuv2d5165BCO2oDR49FYwUTOmxkSvNAQJc0Vy4j+bZ3J2ZbkKAq7VZpowE8Js2da6MmjEOtFdBF8ylolygRQJ66EydtK/aX169eZcd27bIerMWqa0daL4AXSdwqWCfK9gqg41kAoYPbt9qou+72OVY12gluTxlwRuqGa1e8bu8sXoQ2ykXAkSobNHgwElCiAxBF/TXBp2xe1iEBtwuNGNmtHgRfD37pKRuEyR6Z0tEBDpojs/IjT1dtM/nVYXcH1q21bmiGDBwx0roiGNANSS6Fl2bTgX177PjevUiMXbCJ8+fb9Afut2EjR7haazTmr3LVBr7m9MpTvFTusX717BURGwgh5ac2pTiK640HfwdmefX5JJGamIOeZG27/KWX3dyLbFU3Ak6WIQE2eOIk+/SXvwwwN8g1fK5SngLlHJhTI9RfvEQbulpryDWucQXKdZLQwpnztg2g8p11b9kBVDF14JnKpDdSkcVIPkdhgEa0yc6hQXIMSbPhSBJOnjXbqrEhXiLNJNqANvNPY6Zp18bNtpzD4i5jo7krpn+Gjx9H3fdhs74MYJbNeDbOZVN7h9dXiQ0cN9bmffYzVj2iBgEE1jnUvUzoqBwEmK54CVNLz/zK20DtlKk2ktNOS8ivvlX9CSxU+vrTmr8YAQqpsg6rGQ4oB3jMHFVrg3OnT9OG99qa5Zhcoi23NTegqXUX5nb6ADaySUH0ZrSC6o6ftO1o8Qik7jNsuE0CNByBxlS/IYP94BwVqwQhNNuSKadywLkK4r709C8B5r5LdbbZI98AmPvCk9YoaVsKX9WveVtcY6lRpFWEX3tXfqtSMyl0aS/Wh8VNmeoA5lQGqjmvlXD7cFSic5TpI/KaTWA3z+l6QRVInYsufbBuY467Dh84dwBVVoC5y6gB1UyfYQ9gqL0XEnPozPmOl4xASuKphYIJRv4TiahALi03pa2PLV7+IflL8mFFDwrbu1XS9w/N+QqdnihEEnIOtZC44RDdwl0Ew8eq9+zlvLhj9Akfsuo2umTDK83grt+EF3dwSjjBCR2XA0EEdtt3DFeaml1id0yi7Itlmwyj4z37V9mj3/im9eLQAKUXjqYOqUXgLy9tvSig86D0wpU+KW2cfHCIngofongZKkRwgvfYgSmsIuqWEkudgkfBb+jsVDvQSeIqavKY3guipa/ZdKJjoAOFJI/RPVfmqUvyoBQDw/7rTCXxEx+1M9FVB++qnQBHLezWHDmw37ZtWG/vvPBb6zl8OKrHc20GOzZSbW5B6lPgoPIS/lTP+hccvAzxScFi+QU2PNXAix6Do2jkSsZfMvTEXKAffBQym3Z0zdwhK5ohHb2EFAKHxPVX0cDPAxE4cXMqOWYzRMNjlmagmklHQVQuDL4imJSIR4xtSaCG2r/KT2EdlNPgT5pRYk52+mQPEuN6iN8zIX71FezgrbRB9Cl3LFxok9k9a6LSdFpvs6Q2GbiV1vsB5mL2ncl2fmIes17ttc2s/61+Djy2D8wtmDQVYK6XA3OakMc1QVqvhcy8W4YLw/8BvKspCphrpmyixNx5qbJePIsqaz4w1wtVGgHM+nZjv6Yiiu0kfDrtF3JsNx62/SDvvbRjworpNBmIO67fbQlQB6Gvu1WVms9+tp1JYkZXSMkT9kWlpJAbkJKQtPZlFpKyDdsN+61D+rDg7dHb+nXpYb1KsU+FZC0y1AAOYf4mcv6XI+r0lcANhoEQpuP3mhIoBOZYcqfAXD01tQkbc9tO7LNOACsC5aIqK7UR+hN1RsT5KANzsVD8myj8JMhfLKPYaUZgrpR5w1IBc78UMGcOzM0BmGtiUe0gDIObt36nGQjn97f4egAvQv/JS159dmROd3jxz0llTtrJjMjXNJI+FzjXmXlhPTZy38ZsyIZVq2w/0mc6/EyTjXIOLBs+826bitSa7Fm3Mizoo4lgj39D/ER2NSNSGrqUrptEIA0dJLeMeebJfXsdCJlw92wbMWqU2xdWXElsyWTLBcK9uXwZIN6rVg8w03dUrY24525b+OjD1s1tDDfTilhuAcrrRMpNgHJvA37sAvjSwWjjOYxt0p13AeJU0+4Ez3MxHzvHuuPo4cO2AQBvGOlOvOsO7HphsiHZRG1Cum4D+d+0eiWaD9iwrB1lowlXBcgTzN2gPYQWzDns1x3au89e+fVzduboQetVPcAe+fa3MMUzyg/P0/hXgkSUyvUKaS5/UTa1f+l25aYsvN9mLLzPVV/7IplegSTpaczSrF//jr1D2ie3brE+tSNsHHaeZ2PKpBugmI90zP0clIO2JFSzl8rO61d3/eklBlGdc+lXfPl8Xu0B3nxdizsygAhNdLITlM1ODl1bCsgF+uQH1R3fttXNqQyeNMk++5UvW1/UQGUnWxockuoUT1IH9XlCaAE4qL0pDW8+XlcCKSUp1wLgeWT3PtRFOfBsLQfsAXiOw6zQpLtn2kCk8cqQEBQZSXtdom5PnDphm1ifDB1eY+MwvSTtGM2xNROW6aAdmKZZg63sbYsWo646wEbedafNgJ5odUXbpBnJunoOCzyGvfAlSNUd3L2NgxPL7K6nnrLx06Y58CpVVmlzyURKK1pSy/k2l/ziZ4B6bfYA6rsLMFVVIRVaSlFaPWrPat+t5FHjUSwDSTdqzi9wmYC2a8s2DnZbadvIq6b5gzF9tOCJJ6wKoLsM8E6Sqo1og5w6fMTeWLzEDmzeSHs5Z9UzZtiMefMwUzUdYBqNGfIrEFDqtqVSY5XUHMIuKTBHWo+wfp7/1JPWQB8SdPUof8LHPxUqr+GKbSN51S22Et3TYOSvnaCZWB+mR3HaAcyFmqTidIXbh6MSnSN1Fs5ZrpElrzmP1KGdMDEuRNRIIzAncE6Idd3BQ/a9//Rf6VBPuH20milTvBMLp6V2wWZWrQ0DjZfIq04CCpJHUBIxaIg3PcbLO1X3zriqUFM3xeBKbt7x6V0fnu7y8K8uvw5C8GSQD6E8dPyJ5PSe7dijf7xnw0U33eMCLDImXugOPIgmJU7T74j80jNJ0uUKaru7EGffgm2z/YhL92DHatTsWTYdKa1uSGkoLWXdcxJIOVUnWvAT+Yp8xPSywUTCycTAyXvcDZCzii6WZIybCZ7JZ/TNv6vz0yWausRH5Mkdkp9CPsRYNp0YNoSTD3+BtHu1FzbEUaBcwMiPxxcJvFwKEv7aWlABYtDVoHnm9BnbxKmnW9943UWsJ967wKbPX2C9sZchu2uuUhy48PrwuoWYpwZd8eM7cGIicddjfM7x4a7+k8uDnpI3v4l6rhZi/ShSe2Xp7vpp58qVRPDMpdlO4A/qlPDuZJL6DyRzeQnfK5MXwrYAwklsvZyBWQBpI/Ycjx06iM2ZFzAMu8Ptd9zz2OM2AvuRfQdyIAaR3G6lZvBMgDQhVfvyOlBCSdsLaV77G8Ne65Pv0l4ZXa/c82PeurdQbzcBzKkoYgEUVnZk53ru0f8P8K7myZzOVVmbeEYJ3c7THxy4dMZ2ssDYf/JYkJhj97cXUnM6uc6/4bRx0CvHZ8o308LT0oxtxoPd6joQ0ZQmjaDj+t2XABXcXr3fKkZ8DPe2RWUndR3T0yadFpZaHElyRRIwOhBCp7V2LS61fpzWOrAn0he03R6c1tqNhZvAOd9UhUHN45yk08ch0k/utyoPfwh0fHxWXZBZSWi0D8ztt6JuUmXFXAoASSfGPGohnReqAj4WwFw7Y7Dmr75xRxl5t8VPDpgztzH3ChJzuhY8+aTNffQRB+baAGDULrXYjw00kL9+Iy30KQzvcwD48G+Lu89HmE8IjAh/SKYDhu1nXv7ar57xwwlKASJ68x3VncTO2uULVsscfdosbEZjn02AjPp5AXOqf6UX53rhW5Wncp16+nsrKnuyt3YJsE1q6b37VAGecNCDpKT4J3BQp9ZiZNo2bdxoq1gf7GJ+WtITYBDA5ZNf/CIH1LFGAGwp53uXBs65U2fsWQ6Skz3nVszH3POJR2w0drMHYkusAlBDoJGALLeJBk+af+kAhs6YUSlH5dE1mggjbiWRexYpsYtI7A3AhpzsdcucTxlSuAKuBGIWQ6Medc/D+w/ai78B6Nm1HVXHMpv3R1+ykZRNP+LJ1pywywbM/+ziwLa1S5bY1jdf59CIbjb3k5+0OfctdHBLoFwF+WhCMuwI4Mw28rz82V8jFVzvII5UR/uhyuuAD+1CoK1LqaltqWyTS/Xvf17mwUfvCqSQESbVs8pC7TJqzqgMZVKlCTXet1ev4SDCJXZq+3YbO2U6EmAj7LVnf2UNlNkQpMY+97WvWBUSc1cFzLE+CMAcPGkyRiPwNCMv0HRbwfCg/lfgn6TlziEdtuj5F2zn2jV25dxpmwD4OBVQbhSgVZSW87YlXgGcZMrmAuZ2Kug/umEWSZuFMhkjc0NUmK0ElFv23PPYE9xjY++6y2ZQtiOh1R0pTNlcV5EUsXnTBCB4YNsOe2MFdbF9ow1Te0ZdeCKSgEpDIGqJeOa+/IUX7NWf/9Qa6y7Yp/7ky/YJJFoRzwMco+xI000zUQYgujRVDkok41d5dkk1+NKpy2onbyxahFTsL62OTfcxaNbNePB+GwfYVibVafIm5q5SJm1ohNSfrrM3li6xN5a95jYd7wGov5PwXfui0qo8E1ZrOJeYY3dVwNyLUmX9ARJzAIuPfvNbNv+LEZhjVUZ4N2uletEHSu34Te2inYti8jjyivWYXYO1E+VD5uQ5SLGaDhtzVA9tQL/8+/1fzoM+lISV2MiuyxkBrxfGBzQipjsLfBjFfBBnAeZ+8F/+GzrjO9x2Rt+Rw31XQYcKCLUff+edNn7qVLexJlRdwFywfwWxyFg7DMXBLYQJAXPdXYygDzQ8ux8vevU8ZDKiOsn4JJEJwP/26ivSjKnczF0d6PUupa2ORIOCdmdkD0YnYW5lZ2g39htO7dnnHUUtuwNjKK8qyk07dOkuXMJ9jn6S6ZyDP6U8uLdnOg0h9lIW5c9LfM+Vj9z4l3hkU/HnGCGlWvhQGCBLoSAsXtnQNwhJRPneOESOuqgmlLl5u4ieobLdTTsuxQzwjUgc1LFLt50dxv3Y1Lh45pTVTJ1ho6ZOs6Eja31XyiddxI0cqJx9Eu30RTwM99HfU08KMT77PfLh9yRywpNTF+CUEHHVacKFV3z1QJSYRh6p38HLu6Wblz8x6/9zsbwelGWc/JlC1KJSR7JLdF62JQ7u3mXb2fHdzQEcXRDlH86O9Jhp010CV/YjZS9CfYgAUE3MlKb/eTK86yVz5VIPjiHtGADfGMDjFUQmWPRWjPTbitFv8z1w0wHM3a5iVhMVMBcl5gTMnWNSehBgbgfA3AGAuVapsgLMaVEmNaC0hXjTyfUHofmkvnksp22ofe+8sO//hX6j4/rdlwCdQuiFbk/SsclEiTnvOXHU3EigXBOLRJc6JnmNY03YHxU414bN0G6lFVaFBMzQvv2tf7de1gv7TSi+uZR+3Fz1NSRuusersA+N7h3365fAzQBz20/st07dcxJzOpyKkg/zV3VGtKSPAzCnQTjTnLzQ1FNmgTk5yr5cmaRduC/l8IdXnv45RVCEFM3nbM4jj1hzKXMC5gai52NvQjQurJ1w+pP2ssCsevEAAEAASURBVD5oZ9NP1xFJWPn5PIAoAktcYp95lzYIBcw0Y/dq3y5Oh1+zxtb+9nmrqh7GxuAkj7N36yY7cWifjVqAaY3Zc2zMxPEOxIg/9cDOJx+T2x7m7tM58uf86MPiv+aSmsx5m2EtIHBMwIQOG1A4AR7acNTmvTSS2pBK24l5lXVItq1/+WW3Uzjsjjvs0S98AcP4fQB5OlnXEuxvAeQdxw7YP3zvu3Z0z27riWTbE3/6ZRtUU4NRfh0EBqABgBTBLLdtLKAOIMNLT4CcWNM/+BMfsuMs3rpgs7gz/KjfUJlJqEDAjR8OxzpGB1j8ls3UPVs2Ae612aynvkDZTLRBSEMVJ9JQ55GsWwFwtJ1yPcfm66Ax42zm/Pk2hbzoYIdSwqkOWklTtjMP791nP/3+d1En3W+9kfj6xJ/8idWgOqzx2PNB+QoEciCI8spdlDd8akauS2Wq8vbL78qh6oG4VJCbVlEdIYWldZlsRB8g7bfZoN+GfUGp7M6/7xM2ZvRo+/u//n/trKS4pky2J772VQ6TCxJzDsrBTyvt96oqz+vaU/YyEz8qN98UUbnBlQ6qO75nvz3zDz+2w9iD69Kjqz301a87oNm7qq/Xk9RY1Tm3tEq2P9D1U1OdHgAf6bn0HbSlcbIYW3Av/tNP7ArqzPc8/IjNBtAaPJL6ZzPARyr4KiVuZzZvLp08Y6++hDTcspetChD1jgcesBmoNMOeS2vqMMarjCM6WHCRS8y12Kf+6E/tQdRedSCL2ggFqB7M7f5Jg6UeYE4AHYgh83Qo0SY7ARxfRdpv8XO/sZf/6R85ALDB5n3uCZv7mcesz5BqB4N1oJzaYzHSdaVIEpY1tdnypUvsRSRKL58/a+Pn3Ys6831IYXLYCm1RSSv9UvoPl5hDmu/FXwiY+47b0XsMic35X/y81bvEXA6Y876AMvAeKlSPF2vhD+RD/rjHYP69Fgb80L57Drztqc393oC5ixcvipNbfvnEHapSdeG8Rzty6by99M5KK8KIax86nTbUNb3mvBwCC2o0VGvSLej593c5K7SswFmukbXLURIoNsTCMO5OGO1+qDNw5J+Pt+7oMXv2O9+1IyDwOoCgsj+oNragJK7fHfHpsYhRj6Ejq0CUVoOSbA7oI3y3Kx1Qna/IXI47dXbZK/rEks/1jepE9KEpdPTVc2EMueVfhWnk++a/+aCc7+RJqBPQR63U1ImpM9MAdOnied9FOM6hD61MqqsnT7Wx06b4MdNS15MWfZZmyG6O51ytQhjnvOIImc3jxjsjuSR+eZ2TyiehkOeeUMijLbfIRuL/nm4ZYpGMd/I3JKJImYg3DCuqkTL3+JgpE9WDAKFKdp7OYZD0wJ49tg7Dq5fZKe2O3YuZD3/ST5fSrlVRMW3VG5MSVZ0E+iovcRTIh9+cG+/BiaiZEJksBDJJIHjzjl+DWZJPTe5Ew6PIX8n/Hq9sW7yGDZhLcpLzCozn3pMnta/4T4tK1YV2/C6yc7x17VrbhrHhVgb4UdPvsHGoEfdgt89P+NLEUZNdlYn/eUtN6efSx13+pF9YamHClkTJlKnHTeopJRiDxXsugcIgt+U9JJcPzF3i8IdidhTzVFlpMhRLqIDr8Xg999vC+UeDqNqHj++Ujcb3CMwdApjbfvSQ7T+VSMyhYpMF5rxZK4uZ9qPGn98ac2WQhr+tdaB+o+P6nZcAHdH16v1W8aJmE4G53MJC0whO4EPVSBIEOhQpHNLUYA0XL7OYueiLq+5IoQxCrXVwzz42ALXWroxnsuEpmRwdtOOLKhJgve6LWbXV63SDtyo7H0s6YezOScxp7tbInw6UkSrrZlRZIzAXT2XtAOZQC6UDLmM+vPQ3AZjT4Q/zPvdZm/3oQ5yoSJvWeK/BjUbpbVOth+fQRvVl6NLcKTxFl/BGUD0UNOgALuGs9k88SQV1Yt4lUKYTG4WnOTFyw7o1ADNvIi21ze6cC3g0a7adwH3TqjeQpNtqY5BsumPOXJeYa4G/CA75gVTM22S6Q9JMbayPWpnbaBPY+eDuYROGfV2Am+ZBkuhpAcQQUKfVkWxDloPyNGJnbQeSZm+/9ZZtWrzIStmwHIbE3IOoFPbDHlkZkrAKq8MkNI/98d/+LVJ+p6167Dj7469/3Q/ik6ZBgx9exsoNvhpaGkmPwwSQZnMeAFWcX/FHWUhVMMyd4BtexZPSKUO7RIfNSc3WJaoAUmSH7PjR4/YSIMq+7Vuww2c2F2m+0RjvH9Cvn/dPIEt2Grtmz/3ql3YUCTSBVHctuB+BjSlWzQmjkjrTabBaU2oNqb7pLODSj7//fTbL1zP/K7Y7P/NZVHJnILlW49Jp2qRVWeoeKlqV7ez6j1pM9FLzUH0Hf8bK6MGDH4hAntR/llIHDRcu2+rXX7edHMR39tBh6z9itD1w34NWi3Tcf/8P/8GOnz5pQzjg4nNf/1oA5lA/lS3ENuo9HEoGccpRSejSPbY5SeO5TWDK9wLSjfs3b7Pf/PDvXRJz8KiR9vk//3PrP6yaNUeJa5cJ7BKBVszo+GEd8OdgIryqzeoQQamBltF2dWjGa6gT/+ZHP7Irx0/ZzMc+aXM//ZhVY5tPQh46tE801KZKiN+AbbtXXnzeXln0vPUZPdLu4JCNGTNnuZaKq3RTJvXnsfUMoLbkl09zAm8PewhV1nmPPGTNypDyleQyCNrQ57GxKUngIsYjma6S1KeATkmgruDU5UX/9FPaR4k9+qdftvue+LQV9+hmLUzKWwlXghSeS9E2X7UuCNC9CSj68qKXAS932gCk/ibMxa7j7Lux9x3UmdVGy6TKKok5gLmXkLj9BUCuJEgjMHclAebUhqONYBVoGEc9C+3+KHf6LnTFevQ+3l0+Cj+eg3aAuUs2fdhIG967yvqWd7Uygqmv0aU1VVyrhpzn8h5CvPffTh3AXPuF5gVMgb9rQScBYiNsj5pXGuFUkQoncK4TA08zA8KJ/Yf8NMZmBjadfKkPU6dh6mTHSk7W6YpdBonvKxl9UBF0aC+d6BYbiSLFBXbqRiDnNeE7xEkGaDwUzneSvdXFXNF5MqDIOGTyzXm0SDumW3i/KXCOJPJYSYiElIOnPxPIJwTwpyPNTx4/7jbmJDmkXZKeDLRdMXavDg5W/WOJ/AT6gWJwS1KEweQpBs3zdk+i5YaK4J1HyQlEKuq4cqSia3TRB/yBLgjmSCTUsxXSLnGFK+SkvYCiHKnHezZcoKE6VfvQgqaJXaGLnGp79OABF9/v2qWrDRk+nElLqe/6FPngqGGeSEnBaMhvjxu5xQ5dYbMcZJ+dIxyim8dJykDp+KSCQVTJZWkqfHvpZnNY+HxNfUEgplsY9kbv19DJBi6g6XwWRAj5CimrZPSkBad2YlUfkvg4ixHhutOnKcNW61vV36oGDPCTx/xEV01OtBtHRC8DJxXqRe8p5fDg33jhl6F05K27/JxOjKv6SghFd7zSMAXZkddtvWJ+tHCIhz90AHO3rsj1uXUAc7euPP8gKdEphJ7s9uXe+wE6pqRbo8/kSX9cmkvpyXnQ3IZ5hE5o1anxTSyYr/JcwYK6d1lXG9C9pw3vP9B6AdZVYo9KPaeUKYuhpXukz2PH9R5LwBdtdCiCx+PhDx3AXK4Qfaz1CW1uzJXEXADmDGDueXv56Z8B4pTYvZ8VMPcJpJUFzIXNOLX3ZGj2th+af2yx0EwG7OgSU/b35FuJbj6eEt7n4fAkkElAkVQBLwGCbcSGlzYHj+zaib20aps5Z54f8rCdDfQNK9+0IwAFNffMRGIOVdbxE3K2xUgsgoj6RgV0CZRr40+STZ4Hwrhds8gM9zye4UWSW1J1LCeODsZowibYGytW2LJXXrIze3Zb//HjbRwSRHMWLrBerBWKADUE9hw5cBD7cu/Y8md+xbqrzEagffMZwKyeVX2QCr9ql7EZ1xmAS8b6pXJYClhTxkmoTUioSbXVwRRKOYJyCtcMONPCWkSgmdRXHYxjzaf0XPKK5zNom+xCou/l53/jNuZ6DOpvj37tazZ0RK11Zy4twFHrxJNHj9pPf/ADO7Fvn69zHv/Sl2zICA506N7NWtn8Vjn4gRdaQ1IdsoW2cvkK24jttdNHj2BrjNND58+3ydhCk4zfuwFzmrmp6r18vX34D21FX2mY/5dTTs3M/3WARVN9o10FRKrjQIzXABDPcnpst77Y+/7052zksBHWiErv//yP/9FOolFTPRUbc4CeVdUDHYgKEnyAZiQYgMLcqKD0s8CcwCpJBe7fsce2voVUJjbcShBaqcXG3ycef8z6Dxrs6rqN9N2XkN6Uamk3/AVOqm7qUfVV21WbEu1KJKMrWas0sg5/Y9kyW4Iq64nNmzggYqpNXzgf1di7w+a2gE/quZQxS9J6uzZvtdeXvmabNr9tQ+6+0ybOmm0TUHtWuxCA2In0Jd229DfP2bJf/9p6Ijl636OP2j3z5rLpwLhDvQoDcNvpyXcqU1UCFWW2SpotOiW4FYnKOkDZFc8+a0t+JnX1TvbJr3zV7v/8ZwMwp7okX6VgBVLdLm5ots5XUN/GruOqVSttF99jrxFDbeysuzkAbg52BlHfJu9SQy6nsCv4K2tqtZd+0QHMUbjJpbYeNh7yJeY6gDmK5fd/OQ98vZEX76QK2Uo82/VLwuYBYh6eStc/PijthkgNTbs9El/VAla7Mo72c9eObhDJZZAVJxp8CgbLQpb07lNe8U4Ujye6/Cu83CXhyW/uQEjS8I45eVfsOIESTbESpgkeq5DsLXlX0s4zSfiiX1T1jIcm0fXYgZDtMr2XI25cykDhuyW4iTdKKuUjcJl7d4+EaMhHGtTT8DePRJwkmm4FFHLBQgIeIIaJTnKMpDKpvO/HtCycKpS9Qm5ETqnnuGk/ZDZ32ecYOpam7l4rDs5JxUKTD51spzR0HHlX7DcoNa8b7dq6Dz/eoBQqtCVc8q40XJ5reIllGu+hoJM8eSNPnj04KXgWQmj3IUwubi6BbKyca+7pGkCJCO3RycVo/+kaOtlgBTRVuim3gXlC45pNGPciGoLvBPLcxu5rU0uTLyzVHnziiNStvmOdTOyLUCYAKfNOK9SN9ncDaf16gs6du6evoc4VIrS/3Ncf4uZIB6lIJ5FSu2H+Q9Bb+hvz0wHM3dJiTYnpk+sA5tLi6Hh4PyVApxB6lfcT+ebieH+lXijppDw9+kT1pd61ycvHD+hx10JJJ/jJXmcjqq1tqAZVspDpy2mtowZVWz8kDrpjc64EAlr8l3LHxHgkf3NMdYTKK4E4r9SSvwOYu/aL0Eh7rSorc14kY1xiDkP0L6Eu1xn7ZQueRJX10YexMRckkNTG49irNq+W6uuH5HvQRxBG8tCGPXX/MDxo+D5jWDkx3xMQJ+P2AiACMIcEEd+KVDIFgB3etcPXKdPnL7RJkyZzQmZvW4fZmU0cRnUSYKn27ntsOgcSjAYkC2qMgUc3/K80SE/zmjbAR613tAns+eBXbcT5TXh01vz7xVUSRqyjmPBYKyqs9dj1Oo6poI1oEuxEckxg32hOspw8Z7bVThjrp9gLQCljc3MzNtlkY2zH8uWcZjnMJgG03IvdsBJMCtVhM+3M+XMOgAhQ0bxI9ul6+cmfCFEA7HQq4QA0eIsrD0kYyc5cMDcSbGLrJFFJQBmqkK0AWVfOXbB9e/fatm1bbQ9gUBFg6qCxo+w+1B37cXhdBXRll06A2ymp2SLNd+LgQQ5NGGxPfvkrNrBmqNtcFHDom9OuUtrqIGATBwFs4HROHRhweM8O6zdhgs26/z7ULe9yO8M3AuZUvt4nqh68vPVDgetPoBI3uQvc0tq1mf6y6UqjHTty2HZv3WZbly1G/beHjcSczfz591m/nr2s7vAx+5v/JwBzQ6ZOtk9/Q8DcIAc0pYrrKsLUs6T41ADiGlevWWBOQGorp2lv4WCJtUtX2N4337RBo8bYFACvOzhQpLxLRTjo4cxpu4BWVTPr6959elkPBFwqu1Sm0piSeFQakphTGUvyby92Ede/ucpWA4JJQGYo6sQz7p1n1cOGIvXfyyorK71t6eTeZa+8Ztu2rMdm3hWbgsqrtNqGVA8lP6zZabOq5ybMIizDduDSX/3KKtjYGQ/IV0ubv8CJr1fR6ioiXRb4Xu9lrJmk2tu7qsq6Yx9dILAOfmhhXXWRE4iXwdPin/4UILPUHnzqi7bg05+yyqre/j3oJFitu0oovFLy24m2tXXLVluN+vYm7BFWYFtuBODhAqQAeyDAIht2AgHLEVGtpCGUdwBztLLspXaufo42zl9OlbUDmKNYfv+X80DPEHlRJ3HNhWe77jGgdzJ6ESECe+emRTWdD+OIBrlSfaBcboxYi2gRpGN3I6M8hxNb6PI9/rukR9QcP3ryYcwdfdDF5aYuonp4X4WLTkI1yYLzkpZMLKGbonxTgURRAnu+r8FLAANyUbVz47tVlJN2Mopcn0QxwiCe5jvhO3CY5CGSSbOk4T5zKXCczcg5CadbAYW0BArdFS2bZpac/N73pbJII4cU6OVTl/YfPEPte7lrNmfJMzc9eQrJj9510XTxC60puimgnjXYyd/bMu01pJy0QYXAM0vTCSY/7u5UgoOXWdLm5RLTTYKHBOUPxZSP1DNQSz08XOqZ95CEzHOLL+6XJY5D9jWGe7f7Des/j6aoX5uCu6ZlFzgOJUl561UTWL4FFznXK/2IA/34+URLVFWA0PYbd/EkP7WekKJ+k7pK4uGQ+unr0hW+xRAucBLDBP8kAX+J/jfMfxLtVt5ifjqAuVtZqjla+pxuCMwlNuZ6snj58KuyqpXGlprLY8fTbS4BOgX9u52XqKufTFOh81P/l24eJONL6s97C4ub5oZ6BxskPVfEwkWntQ7vN8BPa+2DpE13JCO6MG9zcM7TuJ25+HjT7gDmMvWr9pl51aNG2kJgrgyMR8CcTmXVCZEvY2C+M9Kc93/h8374QwNLCoFEcWao8dfp+l0//gbxpN/jJqf4RcbeMAIkkaUoKadFqyS/igVCsDF7ElXV3aiMLsXAv2yrDRo5yh549JM2CAkmnWa5es1q2wxQcObQARs9c45NB0SRkf4W5ixSYY1rHjEpHuJaJ7yJzQwoFwrFeff5Jjw0IcUmIO4KUnuSMrp05hxg1lHbtXW7XTh9kvht1rdmhE2aeZeNQwVUhvAlXddJwDuqjCtXvG6LUDuU+u0o7PJOA5AZNXqUXYbW4RNH7fiJ49aAmqjKpQTQpKpflQ0gb1X9+6PV1A2ArNxVMiX1p7xIiknzpAgqtaE234QZjStn6qyRAwjq+Tt17Ljt3LXLjhzcj+2vcutfW2M1kybYJOzG9eiOtJyAf3jTHEanbf7wb/4/O3n4gFUNHWZPfvWrNmD4UOtMvCYSckm2BJhDPMGa2VjYun6jrXvjddu3bbNVAQjNfvABuxNpRWBB/sEjfHq5c49X6A6Vy6TPVBhew+aFADvlCwcAMoGOKkOjf7xCft7BtvGmVW/a5eNHrXbaDJs6e67VjqjFBECZnUEr7G//03+2U2clMQcw961v+KmsArK01o3AnPplqXbGdqcyFHtejiSlwxBaqYd1AI6rXnkV6bat1Oksu+v+hTZwyFC7dOmiHT582A4c2m9oAnq76dm7Jwdp9LN+1FUVfXgXyraUb8XTTvKmefOVuvN2eOcue+XHP7EDu3cCuBbTVmdbzciRVg0Y2hczPdIQOsKprC8iCdd4+SJ1NtzmY9dx8Ijh1o1xQd+beHUVVKQEV2C3bimqrK2AsX1qhlvXAf2t7sBeVI6RCUYKT6BuZwDZLr362NgZd1gtKtTVAMNdARKV9zbCNXJi+JLnnrNXAeaAimzWJ7F/9/BD1m/oYBfiEQCsw7VkV1H9gUDpTRs328o3VthWgGZOBbGhM6bb41/5svVBpViHwugQOAfmWpCakyprh8QcZRsvVWAHMKcy8E5WxRLGibDo0/vv8xJb6hX8Hh6vZQfPTL92rb86Gg8gQgQOGfRwsdMuoQPW5d6kps5SHZR38nrnTzxocIodlUe4wU+Wp5T/wMh1Y8VwaYAsEXd0xjRsJtmIMeI9jfmBHiI1Je9llC1hysD/EUj2AtxL5afASf5ip55jQhzruiZDuOGjgc39kx+9ZJGEJJonkQmXo5mZ+LfjnyWV8c57zEs/45PHMYFy75kYKocbXgqbCX9N2GzOkmduevJY8Ye71Cd9MqSy5l3GbV3lQIM0bjLyrp1JRY5NXdGdRFI//pz7Sblxp0wOg0dw1bMmbFlv8ac/v+KD7kQJEzuVSy5+DFjoEtzb/y0MG5PJhS4MkfMpfPKQ1xJwFnPOesq9RRruEsvPC1ZtDopqXNzUL/iViSon7zO4x2/C646YCqZ+JTcxC3RELJIKD+FNMVLSScXGcO6eeqYPzk4MczPfgEe4RT+Biw4bc7eoOK8hoyZwPWBu21FszJ086qey9iw4/CG2B3UO6TOVlWldeWllw+R5dLx89EuAys3vLW5tlmKfGNJI+kv6UPWBSbcZEqQtev+YNDZJzbVKDY3F/iXszbUg+dC5sQW7Ml2sH5IPg7A5N4wTE7uzqCqDVofE3AerN41R+tNo3SExd21PqJ7yZoC54rIKu+8LT3L4w8MWgTlv0sm8QbUUHr3DTfpcQihQ8g0EBT+FTC5FyHykLjEnpIZvpBR7WwLmWpHqWbtuHeDWMjuGZFrtpKl21/wFNmXKFFf3rMMO8dp31tlGpHdO7t1r4+fOs6kC5sYBzAESCCDSnzSDJJHm3ydzWmE+cZAo/F7lLoBIp50WAUgd5JCDLavesh2cBtoI+NUAQNd0qd6aAZCqx46x0Ug0jZwwDjBrmPUYUIUROgz6w3tngLki/l575tf23D/8vdukmzT3XhuPIf9mpOkO7d9rh3bvwnbaBS88qayKqRK0c3phKmTa7Dk2fOxo68sBCzptswiVwk5IK15GiqwYpiWNJ/XLo/sP2q6Nm2zLytV2DkCuGcCvBcm+ZlRkKzBVNGvhfRySMR4baUOslIMMQn9EOqgnd6YgTh08bN//H/8DMO+IDRiFPbWvf8P6Y4O8k9QeZaNNhUU6kq6jRJC0qrddSE2tWbbUtq9fa71HjQbMeQTV4tnYOKP6KG8Vb27+F+qbIiV33iCCQxJO7UMAoYC5sNkZ3qXUL7X/3Tt3Ip0HCLhpo/UEWLqDOr6Dg/h0mqlAqhO799v3/ut/c2BOEnOPf/ubAHOosrJW0DpX6pW+WZLUv8A5XfoatDpWX+7p0y9LkvmtpctsJQdm1O3eZ/c88KBNQbpR9usOoDJ9BF50iq2k4BRRghsyBdV74CC7Y958qx0zxvohnSZzUX4YGmsXAXNXAfwazp63Qzt326olS7CTuMLKkJIrIQ/dkZAcNWmKXQD4O4LU4rljR204qquT58y0CQCpFRwQovKUCrNM/EgT7gSmlt588SV787lnAGUvWCWqvV0wt9RC21DO9KdnAW9X0XbpCfg39q5ZfuDjEKT0eiB1WAp413AFYO6FF+2VX/zcLtN2arE7P2XhPBvH99WDtqODP6QuLSEfSWKeRT36LWwqruF7PEPbVc86GMD3i3/xFxwYMcSBuUak7ATMdWktApi7iip8hyorFZJcof0Le+iQmFOPwBVuoWtISun3dgscwRPfUOgmxGA+O6l7vnPemzpZhfOoST71IjdVvAZDp8OPwqiDcnCOt9h5elzeJWXnYfNSePeXQP/6MQP9G9EpiOsRYqx4v1H89+YnikoxAHN6DuBabBm6qyg9ZQKmHPDsg1pSSpFrTcvDFe/Jq8ckNs4pDSpctD1kEjzc9JuGKqAX3JPg7hdd1H7e7SqkGsNno8aFRuQhcEPM2KZipGvuon69FBRYlGJKyTM3PXms5MfT5ye0Z9UH/xistVsXACBqibZb5IOqBttIQHRC/eFywytwqV/RymZNFEQwd+ktGPbmKeMV2NUvfwVlk2sHOTrv/8lT8nQyySvVdq/UPRtYIfHIOekp9xYJRZeUhqfCmxwo6OCvGtHFW0EEvao8FTL808SM8Lj5hEjRPHKgoFddqlddMY4HwikbypMS8YIrG+ZmvoGC6B/oNXDTAcx9oEK8QWQ1i0Jg7jySEzqVtQOYu0HBdXjlSoA2dG2vkfP+oE8aryL9vNFHjvRX3vfF/o13H9949z6Pha7AOdlPasagedulBk7BbLPeqBwN7t3Xxg2vsd46qZV4HTbmPlhNqbz1p7luBzCnkTb/0qh+DTBHYclGlGzNLX32OSRefoptrUpb+HlObMwAcyKWnfPQXLlCCv6sb0TfgO44uCYPz34ljGQ5UlgBWrIpJwBIoIvURVe/+YZtQiquMyDRNNmQQ4ppAMBVMaDIeVQ2HZjDxpxUWSfMmgswd7eNHDcGkAhwwoEiEgOYk01igSRtAExuTy7hS/yILZ9HyM1BIoA5QAkdPCCV0HWLXrONr75q9UjLNaPKeZXTKTshjVSFxNMogLm7753LKZtIcPXrY00lbCJTDsUt5AdA5lVOpXzme9+xFmz1DubE0341tZxuiiokfAjUqR440KWsGpCk3Qnwc+H4MXhotWETJtsE1ARHcqCBpPBKkGC7CjDXLFVF6kZ25XRYzL4du2z9qtX2DlJedah8tl3hSAn8O3ctB5AZZOPvuYcDMWbbmMkT/TAHHfIniTSBLi4xhw287//VX3EIxDEbOGaUff4b37B+1QLmSqyN9LSZoHJQubgdNIFlSDC+tXixbV670nrXjsT24CdtJmUgm3S+vqTI9c2pOcQ69m8xlHQyNwxhHMSk7mV+KQBzASjToQnnAILeeG2x7d2+FeHJRpu8YKFNnDzFhiLB5uRpIyf3HLDv/Of/AjCnwx+QmOPkz77VnMpK+xAPQRAlALPeBpM1hJqgeBM4p/KUqnIrUmurBMxhX+709l02dNx4q6ZMjh85BDZ5FZVVbIKycSLQth4AdCdSkOeQ4rsKGFWDavV4AMNRSE32RXrND/AQqJnQbwX0O7J3v71BO1r1ystIugGikWwJp5n2HVyNFrLM9lz293seuN/uvm++9exf5SfjSkNFwFwpGzbi+QLqzxsAyLZgC0/tVCf9duevBLVnqbHKfuIFDnY4xvdz/MA+57FqxCgbjbrxPQCN/ZDQKyMPqtuNAN9vvPaa7cF+Y3m3bjYEoG06ALIAvD6ovgpDuICt7xMcJrlnxw7bQxs9smc30ovH6FubbTAg+Jf+/b93actWvq8GB+Y6Acx1tkrGtZeffrrj8AdvBfrxnsaxhw5gToODisRvGoo+PFeWF3UUhZdP6Aods+/Zjk8543/ocLjrmQExS9c7KTzUXYROKzQVFY6Hz9COvGXjZ7zTx+DPbxKwMF589wiFxHw0jI5JSL/FWPGeJnfLHiIw54zDQkwp3HMTOiXoboRxTr1MA8/6DeHDe1oIzqV8gm+oFCbsvHo75O6TFw8XfiIFvSWx3CPrHkLmfm8GlMjSUvjYprJ0r3VLvpPw0eQSbPdJKWRTyQZSKjGl3LPy7jGSaHFxE8vEFzDEExAnI7U6HEQ7d3JXmw6FR2QyJBIJmZCwvLMsRP9MIE8/814QnPjBM/Cjt2sDhzRCO4GrtFxTWgS4Nlbqe1MP7eTuhjRDexBnScrccmWhp4TPd+VN+QosxraRIwSNhKjXR6CauIX+RlH9LyYeiQWSgTu5JfWZxkrSjOUtd6/vhF4SPebOX2/mG4jxbsU9ZKkDmLsVZdkeDTWL6wNzB5GYC6eyBom5XizQZP46d4UFQPKOR2hbOf/4lDS17AcSvTruH/USoHKzbeJWZyc7V/J0vH8LiYY5ViZFOkuFcdBB4RjLtNCVUXAt8ttQDevc0GI9WaANQu1o/IgR1gcJukrGPcnQ3M58ZLj8WD56X0CZa67bAcxd2xNqnG0PmJPhdgFzSyIwB1C8ELU6Scw1cqJi2NxPZxneduJcKTYkb/Nq7/w1ojYnY/4auGN7FiASXzTGlyLBVQb4IFBLPfoVTp5ct/IN27xurZ0EcKpBimcqKnljkEoqwU6X5oWXkV5as3YNNuY4iODAAaTRZgZgbvw4ayKBYGeOWRSgnKSbFMe1MOBJcza3MUfaevNL/OGnb7VY804k3o4hwbTtzdW2ndMomy5cAmAjL6jUtgAKqRy6YVtr9oIFNgJAoy9SaUVIzOmUVNnlKiLMK7/8pf36+99FOrbeSpGO6gKAUo59uYG1o6xmIuqlgEmVfPsXkP57ExBy9/oNdgrgAxZs7D132+S5c2wkoFq5JKcEKpEX2cGWAf9yDos5vO8AkmsbbNeqVXb59FlXZaxHGhfkxUoAWsp6drc7AWPunD0TqapeLkmm7JZo3AQcPAV484O/+p+cehuAuS9885vYJEOVEWCOQgjzbgAWqZaWMNmSFOPu7Ttt9aJXbdPqN6034OSsRx4BnLzXrgJWBim1UJz+GxqGl3AoZ/3yTZIVlbrKO9gUTIA5gWSAO3WcjnoA9c/FHJrR2tpsA0ePtnkPPWIDADIrADTVjnQowen9h+1vOfzhVN1pGzJtqj3+9a+higmwiG20Zg4rkyor4pIOzLrdZMrQ/+GuNig6AuZ0EIYOmVj9+hv2JhJzJzdsss6oEau+Ol9tsaETJtno6TNsDBKC3Ssq7SKSaq8j+bZ9zRo7BnCovmb0rFk2ef48mzh9mnXlYAgHpsllS0OTXTpbh72/LbZp7Trbt2WzqxILbJNaeP1FpDD5RqgcQNgqVIPvt7sAOks4HVXSdwKVJQFYTJ4EvEka8gDSdceOHLG+PWTSoxe2CXtQLthDp+21sfEj6bY9e/bYlo3rbSP2GcGJbdCEifbwU0/ZYIDNrl2Q2IPeMQ7w2LFho6vv1h07bKVI8o2fha1E8jmIg970/R4DhNu3Zy+g3A4O47jCIRmApocPwy8Sc6iNP/UX/8b6QrONb6aButKprF3aBMxJYq4DmKOJJZda/IdAlfXChQvi5JZfPnGHaiPUL3M/wg7Ey++stKJuFdYH45ZtZUxp9MV5OQQWaF/uEN70/OG70kUwrIn9d7s8jA8sCknO+J/0g7y3NxAnRULE0D0mOxu8hclmiBOKSsTao5LPVZZPjxe9k7ipm6cZPeOd9NKVNSE9cBqDQNnnGOfW3MW3d9AJOR80xLM8SNaBSx7U4aaX/LxmcjGT4Kl7GtYfcnGVzSwpeTu5GCEXFI/4Eu8xULyHetJbWnzRq+CepaCwnmbGUe8xuSw/HqSQ4QLaudcMwZyjKGfeeFawpIHq5q9JVA8pN9KUpJyDyhpQGYj0rp1Ov4gY60jvHj2hpXeno4fCK0mn0Pl675EfpdBe1JiOt4/2AkD43ermemnnu1+HeH4g3uCI/x46qbfIYwhKuWVIvRtv2XYvOt7inGBCVTelw3/VR/D337AjnSk3Dxo4TPijLiOPXp8hnvgM9a9ps+iKsJ64PKnwnMnGLSrjkMTN/AYO8oG5i8dOWsmFeps/cYpVM1HqwqTXT0pT4PjXHvFArD2fP2g3zFhjrwahCSpa4/s51DEOXjprO44AzJ0GmGNnuAeTUbcxp8mq2kjaKEKf7a8451pWfpGmwTvqIL9gPg5vVO7trNYwVwoF5enQl8XlfZt7quElHHDXAlAn5cnrKosmGTW/iN2qVkC5EowySVJuAIbM+6Ji1L9LN+vG4kqHP3QAcx+sMcax5LrA3NF9tv3EAevEuqGEQ76KUSPsxGKZpXoYK32MomZ9sGx/HuAcxsH0PTe6tBf6YBm9mdi0p0L2lKP2gDnZmdOf7E+9/POfWTHSWguelCrrQ9Yk9UYIifMs96G5hxT07HMNyk8HJ+xCymb3jm0OGKhONOcvkpkYLm2+CnAYiI2uIagEVvXqzc5Mm51g4f+bH/+DHefeFTW9+zAwPwLbbL379gH8EcAGOIAE2xqk6batecvqDh2y2hl32p1z5mAwf6I1w6eAOT9B1oE5TKEQrwXJJGdc6cd8iG0xrfoGEXMpLsIqD018qzrwoAm1wHok9KTKqFNijwKKHNi1007s3QtA1IK01hQby4mwdyCx11XG/AH1NAd45Zln7Nc/+L6fuNkFCbZBEyfZvQvm24Ah1daVwwO695FNOoK7Mf46TnBdj+rmm3Z4wwYrA9wZPmOqPfbHf8y6tr+DXk2Acr46gU9JM7UAeOpAmVYk8nT4QyNSV8cOHbEzZ8/YKU4y3bsJgKmkyIYAoM197DEbWD3EKrtWAvQQm3I+ffBIAswdcfDrqX/xbasaNNil8wRoevsAlHOwEj5bkPzaAzD31uJFtmXtKuszeozNeAAbc+RbdukcOIzlqQrOXPpMXAtGd3dXW6ANKC+UvQ780KZFE2lsWLPW1i1fZqeQ0hqNSue0e+dbNerCOihBfan+ZIvwNBJ//+sv/9LOopYpe2ef+OIX3C5badcuVg+ApdLqxBrCVZkFyuk7SP5Ufv5Pm/4ciHAVwHUtNuZWv/SKnVi/2cqw8dcf4OkOwNEhqNFKMq2MPkLAqyTsznF4whpOKH1r8RIk7LZaT/gbgSr1fY88an1ppzIlJcmoPbt224a1b9su2mlJZQWg7EibOmUq9v/K7Dyg3A6+j/3U07Ht23186An4WAMYO+9Tj9kg0u0CwOqqsV5uAN204SbyJpulZQCwUq29Sl12BTAUb0pT9aV2ug9w7tc/+TE2GA/S3vrYPZ963MYA0A1GRboz9XuZdn3q2DHbSvq7URc+Rpum0XPIW7mVQq8IegIC2QG1ASNG+umrbZTTttUrrVNlmQ2ePNke+cIXrO/AAf6tNfJdltJBdEGdtQKg5uWnf2lP/+C78Ndij33rWzbvi09aPeB+i1bY1Lnbrta3p5rQXY/XudRm4rokBvM+/jrhP3zOnoNUYg7DTHb55GnrBOA/bdhIG967yqrKsVNIsNA7kl8ViZdP/GZuWEQ3leVOHcDc9cvJG5bqqeCKDU7O2eeCYLnXtNIgxv/wGmJq4BWVSCcm5x0kfnwaSQg9aHJZGDLnkksw/ynGyLqKfo77wIX7J7zmwkI/BMZJ/EcOY4jC9+j+we9iMeSONHgRl/ED0D188LiKrTQ5PcWYnslMiYX3NGjeA/SURjZ/IVkP5fTxjxQULqhS5hFJXwprJS3CNETuIeVd6SfOnk7youfCdwVz7yy/Sdz3fYNJ59sTgwp3kU/TTwiH1qLUCU09aJfL64KBxgOHqB465vsaNiEqupG+Auv9Zi7PdxpQb7n26zTEk1w9YBI6P1Ia2x8IfCPv/MDv4S0vQwlPzph407ecf/lEOd/J3wJvkUNiJRE913L2Qgx14QBIQsPLHv+YljiIn7eiuboI95SPUGAxtsdTyaiOfYKkyM6GiCbx3CmloNBJ/CQob7ENpB63+SFw0AHM3c5izm686UxmAXMHAOa2A8wdOHXUWgHmUhtzCTAXW4b3Fd6EQiMKX8a13OIbrhgxvnfcP/olEPuP25QT9aVqNt50/Fk9UzKfwjH0STwkgYpw0OJTanpNLKAbsC3XihRFJxaDsic3ctAQq0JtqBcqg90A9cvpC6XG2gHMfbAKDH3BDSTmOoC56wBz2DCTxJyAuZ+hygpoGYA5TmVlNZkD5mjTXLmx37+IXKUJaKHNv4NE26bXl2Fn7aKrUYY4WnoGgE7A3wgOERg/cbINrhpgp0+dtG1I8ax86bcY0i+xQQByU++Y4SqCFagTKv1WwImLSJlt4FTWvUiMXUClcAgbY+MAZ0agyirQoLxbV6sAoJHUkewT67ttlmSS2HZgLpmXQ8/Nbmje5H8hT5r7SO3TgSDiCATTdytp1/OoCh7au8/2bt5sO5BWqwD0qL3rTnvks592VUFJlwmEf+XXz9qzP/qhNaMOOAAQY9zs2dhjm4PtuP4m8OgqY5nAKElrybj+AYCUTWvW2RoOAZB6azWSeJ/71rdtIKBPMeqHjUgkaU4sUEWgmWwxC/zUQX8CZK7Cn3g7h4TWCQCX9Uh0nTp8kOx2AjicbZOnUz4jax1AE7B1hlNNf4jE3MmjB23gqJH21J//eZCYkyooVaQ0RF/lUgL9pouXbTPg4bply2zvlg02AEDyzoULbdpdd+j0CgcPfU6o+Zz+kktFfpUKiP/kLDfVSWdoR3BOhxecOXYCW29LbMPixaR91SbNnmtTOG23EqBSQI7CCvSSHcK6I8fsH//mr+08klwDsaU3+7FHsZE3hNNKuyHxxnEVAEoClyRx5oc/qFPmv+ayUNIjDZqCANiSBOFbK1agyvqCndmx26pG1NhopBZnzJ1lvQBQXWINqbZi0lZZdAKE2kt9rcf+4JoXn3d7htWUx+P/7I84FKLK6TVSXm9TB2+9vpw2esJGcwDIdIC+4bW1ftjHZUDIYxwAchKA8dhupNK2bbXLdXVW3qOrTVqw0CYDStbUjvC1egA9O1kj8yGgRG8HOkhCJ9gKnCvjZFVJm0oVXJJzrQBoRznc4+mf/cQOI6VHY7YRc+61GbNm2QS+N0mRtkCrHh5PnTplBwGZDwISSoqy/jL26VTnpRXWhXbaG3C8dux4bzcnkaBb9+orSIeW22Da00Oc9tuHAzAEgErVWptNlTSecpeY6wDm1MTC5V9BBzDnXz6NS1e4qVv4PV2xj9IHHVnIYyYuO4Ov/+b5h0ihr0speGfnPoRV8Agu6TnmNrs4cZJEl18WmBNFdZIexwmJz1w6nkbyI1enozj6K+iAk2AeJvLgbplw/s7glUtDiUeqWQrx+dbclUe/YNxz5z8Jl/AX8+PB4EcdYLwCewnPHg8aqXfiEANzT72gqsm6Orr04jHGcFdeVDwhiFxCe0jLNhM10lD8dpyjt6eplxhGPMQr8h2d4rv8U7pZfmPE93tXXSf1H+mLLz1HHiKfgeO8lhGd8lMnosdJI8YHpUVQvSZOeo/p5BPJf/Moab5pF5lI4THQTuspJplPJu8tSyPP4/2+eF4SxjLPKe8p/5k8x3LIpBmcyCMP4tEpJnXkX4Tc8VS96c93uZNkRUZ+sU1n47qf/FX4IREFlnO41NiSuE6blBXf/5JgmrbrMROL5yRxbvnPgezv4jdwEBbasoui3e4OiblbW/I3Aubi4Q89OPyhD9IVUscIbSjwENqk2gcth8oKLeta/tJ2lTSpa0N0uHxkSyBU/W1hP46TajZ69rvaWmhxYZyXpI43MG+FAZRj8S11vgYWQbI3pcVdVxZSfSu72UikWHpVdLUunUsNmROk5QIo1wHMfbAqjOOT5rntqrJ2AHPtAHOdMN4uibmrtliqrABzJUgpLXjiCVdlbQaY04EKatlq497+08mV3pJLnhrjATu2oG668fUVGMA/4/YV9bmE74P4AC2dAN/GTJ1uE/jrj/Tolo0b7M0lS+04Nrx6ItkzBLW6Xn16WjnSRgKnBMwhQsTpxpdtL0DCcVQeL58+bVWoOw4aOcJPlSznxMyBSBsNGDzIpZQ0dxFIKLVCv3hnQuPfq9qHJozKk3/Tyo+kqPiTGRV9q1JtFfAlUEhqnW1Ip9WdPGP7t+9wdb2L585ykuYI+wzSbcOH13AGRAUHMTTYq7/9rb0AMFKP9Nro2XNs2n332QTsxvXo19dVJa8AtEnaqUhST9ikqztx2vZt3WYv/+hHAGwnOJBhtD3+z/+5DQVMq6jsYo1tzW6+QSBRKyBIs0AZVD+lmqrTM4tVtrg1Ygvu7OkztgFVxp2AnCcO7LNypMxnPvCg24MrRtJKIM5ZgK0f/vVf2wlOcO1XM8w+/2d/bgMAtmQjTeXsJmQoN0FYAqN0Qu3K11+3zbLrd3CfDeHAgLvmz7dJqJHqsAipZuq78zGZcozjrySJVc5XNWHS3f9CeatcJS2nsm24dMm2Ii24YelS242EWQVSRLWTp9oIDtoQUCiJxiLaFHCbA3MXUd9d/Nyzdpn89hk+xMbdO4ey5TAEVHjVFiqQNiumnxUIpTqOl06mjfMGBx6dZidbvWy5vcEpumf37LdRd95h0+9biCrxeJeoVXtwlVJyJWtvZUyar1y6yEmre+zp732X9nAccBM7fd/+NsBcf69/2UlcRV42I2HWo7raZi64D7XiWUhDdvGPp0VtEoBPKtJ1J07ZWlSmd3PQydmjh63b4MEcQPEJuwdAVQd/FFEnAucEzNFaPD9Sh1WbVdnpo1JZyhZgRRFhaR+nTpy05/iO976zlhOFT3GK7kS7GwnHmTNnArrxLaGN1Er7UT1LAvDE0eOAeYftAie/tiApV8HBL72R5pZduiF8Sydox5s3bbB1L/7WOnMK7dA777QHHn/cenM6rR+4QfoC5nSys4C5lzok5mKT407h8BcPf6BFdkjMhYFAXe/v4Yr9QZJ4fM0xk3RmsOYdmXdovLTDbOhbAgXl5togGmCcUOrnLnJME06aCO/aKZKz68NHYnHUdDqZSB5OjooX7h430+FF18gZXa87RZ6CP79JWiF1Xvw9cSwMlL5/sAd1YNnLpdLysgc3IUMeLIIOaTQePD8eJ3Ae6OHggdKQySvvHpZJgYLEco3OCTMeS+E8bAiX1KIPHkkw74D1nASLzuk9uIcJRmwb4tLTJlR0ixE8SfGS1F9h+aSzpxjh/d6TxiJeYlPxPCf0Yn6Cm37156HTFEPZETIJHOOLnj/nfgpiipwmCimpGz54GUXiCpnGCw+RTuxPQuI3JJl+KzcO9d5804mPGBJr8KxB1stJ/LszP2I0yY+CZa/YHhPvQCdDL7jrV3XBrybRIpApBP/OUwLyDMmFcsSDsGm6Hs5jJHzqa0poK5yCOwHc4Vv8hXIWGXyYSHsIbk4q4SP2RYp6uy/nDy41EeoA5m5Pab8nYE42WEKrcWZim/FWRmVlWl8es95+5BIqNM/vlr38LhvmLWP640DIR+lbnpE4PqrJ+F/sr+iNfCMBx2hjLhiYZzFKn2xIzgQ7Q+fcnpGkW6Sq6iexAkQM7NEH9XdAORaLAuUkLadJewcw98GqMPQFARDoAOau7QnVR16rysqiHkRGNuYWP/vsdYE5DcyxD40ARzovULVp4PY/sysAaLKjJdtXAkEErgjkEAF9Hw2ozsksQd8+fX1cfWPRIluCbbYLSPuUYOerEkBJ6ph8Hv7dyaxJERJQ+uquABRdPn4KFctG6z5iEIAHdrb46zF8qE0A0Jg4Y4ZLzUnKTCdaSmo12BrjC2M+IZhI3bQ24MNcI8xJIv8qNUlXCchxAIn3CCQ1odZ6jFNRf/3Tn9shVBm7YLfr4T/+ExsFiNQbCdgmALLXASSXv/Sind23z2oAr6YiBXWP7L316GFNdCjI4Hm5FMGb7NKdO3Uaqant9spPf4r9tD3Wp3qQfeZf/msbBuhY2QVgDiBP0n8CZJoB8wTqtJInndhZJmAfSSkcXZWxHmmsnaiCrl2x3La8ucLLfP5nnrB5qLR2702ZkuETBw/bj7/7PTu2d5f16t/PPvXNr1t1TY1VoMaoMmmlrK8C8GiMFZB3+XSdLXrpZduxfh2qvXU2EoBn8t13ceDGWD85VuUpkEdl5uWsjlJuXsa6e0mr6r3cHaQgkngROFd34oQ9+8Mf2KHNm+3KOfpL+sXKnj2tDOnHq+RVEoxSO1VkSSQ2XW5wSbSWRsqgW6lVIS3ZGVNWOjhh3he/xMEKg/3QBAGNYsXnvPDmEnPUqbdiyl5SiwL83nlrja1+eZEdWfe2TZgz2+5AfXvo2JFWhqSmCLSobPk21EeXKRPwcOLQYfvHv/1fdvrIIRtQW2tf+rM/s/79B2An8aKtg976FUvtFDYQ5z3xpI1HwkxqzI2UrkpCl9qYZjFqA5cAU5dzQMTrSIuq3BcSZy42/DojGUelu53B+tYgMSczP8W46675sqRB1TZ1Ym+55DwpK9maW/TqItu+dq3VHT+C6vFou+dBwFm+jVK+LX1/OhBE324TEnZNbB5J3buFZ31nZbS1SsA5qWd3xYbdho0bbfmSxbaL9tStejCquzNt1rx5rsHgkomsEUoA5Er56wDmvHozP97qO4C58PWHxh873fCWKavfwaMvKmEgpq0OwofJwJR3FurM5O4dGo3bLyLILfwEp5RI4i6aka4COkkiuZtHxjVJJ6Hg9Dye/PkgdfmiOIbDU7YAgoc4TcIEF/9N08Qr+ofw5EF88BLDZJ/zI/tb5ifGiE6F79H9vd+zk+oY27kMWQtOPpjoMfCvbHveuKtowl+oxwiM5Gcy4TcpUy9DPfOn4d4HBS9jhYNyknaayzSeh3aekiB5z6lbGtG9E3r4koYPf/hLfFvBNPnwiaqeE6ZFR/kL/wKN+Cs/5zc6vM97KPckJW568t/wkKYhHvTfefMyCuEULLwmmU3KSK3TSXgAheVK4vn3xmtIO8SLZe3hMj+ebiwR4ntZpf65uk5SS33CA/4JWwUeea8fdH3u+VQ6/hBJwzmZyubL6zfJQ7YM5J5GTRhWvkPuVObhL6WXEPX6zyQQ/BNKSTriprCdBHoqG9J1HomTV05JuYmGwkBDPZ76DU2AtbtdyK/SiTu4OqWKORw7dApFPtLACnV7r5BUBzB3O0v5YwHMpY3yd9g4b2elfKRo58bPW8l2dg7htao+iAT053sG+uF/XJxqEamFnBaQrSwgZQBci6buSL706Y49ue5IIvDXo6QMVTRAOSJzqKODcgLm9CfaHdf7KwGNLfpTfXQAc2Gmky1JSofFP41Y5ZR4lDPI6u9GwJzGYS9b4nj71Biv+PHOIyRxC+N/sxb6fAedARD8O1F6SboCsJv5PiqQ3innO5Atqs0YyF+/ciWIXj0SckAW/DW1NBInt0aCEqp6ra72eAb7cpfqzlr3IYOtx8B+/FVZd+x01U6aaDUcFlEOAFECqFFMn+xqf8q3ZhtIUSnfmiu3CCgkE8qJz1k8O2E+orxpfuJ5lMojYX07CODt1KGj9rMf/aPt37zBT+u87599ycZiw2sAIJcAsw3Y7loFMLYPW2S9a0bYJIz63w8w0rV7d6TfkHgjbUm8SY2yoqjY6pD8271lm734k3+y0/v2WF8OMnjiL/4CYG6MdUOVs5W0tVZUuYlvAWdtHHKgvqkcEEs2xlqRdBLg1djQYHt377XVry2ytxe/ipRfoy186os2/9OftZ7YQNNBGyexlffj733fjghY5LCBh778ZRsxZqz16tXT03BgjvmW1qWSmDuPlOAznDR7cOsmQNYWmzh/oU1CzXj4qFo//MH5Id+dfd5HGXuhBSlLSVo6MMdd5a4/lasf/kB+ZGfuAsDUkuef51TR04BMikAopMSuMids4xCGIN2msNQTddGAHbVdqNY2ofbbpaqP25kTOPv/s/fe31leWZ7vRihLZDAgCUTOORiTTHK2q8plVw7TXa6qru7pO3PXurPun3DX3F5rfrw909PdlctVjuWEEzY5Z0wOQgKJIBASIBDKcD/ffZ7z6hVgu2yD2y7rgVdPOs8J++yTvmeHQoDHGUgn9hkwwHLgKwFbqlvVvNcxjKh6JkmAUUktUgdcH8bpww6k5spWrbFBE8fbFOKYsWAuttV6+TyzBalG1b8kE2ULTCCowM3fI3VYe/6MFY0eYz/4z//oEnP1SKCtW73W9m8M0qLf+PkvbBQ82bN/X2sAXPOckAeBY5Jwy2VsaMPb6xq8pK5e/ppdQ8Jt3uNfR1L1cdLvmUgxdsOphST3mCcDmjkVyYfao9qY8oUCr6uz3qBcF6rP25vQ8/junajI1lkJqrH3LV1m02fNgPi0IQotMrsXWeIQb4nmPHIecuCUa/F7DlKZG+Hl95e/YTVHD9qIe+fYVOIaMW6MS3NKlTUTgFgeibNariM11yUxB+nSDjGMeBd668ddl4050cSbpc6f3yEGv/nwZ15HnilvVAGYC0h+SjKFD9V5pB9uPJPP/Cl/QjcTziGZLjeLAABAAElEQVQ2XYdDDU5HOIXG5u/0nT8Mi1pdqlPST1/7oKsO0WMKHViIgzBp+YlxqaOLh15TCr/1GDxQzJPijiFTF6mw8U1HdB1hOq5CqI4UU191ulB4D5N8qOv4CwGhhzITMpx8q7Impfbnoez6UtGojlITEt6Hjp2zXhKX/ul/tGWgATPEDz0I73XndA3JKYnkyuP3+DwO1ZUi7Thi3ehJ6rPkquNeySgdOlg6bxVPNhlU1+FZyKPzjD5SXjmHOqUcHcmFl8pC52x0hFCcHXe3vfL4PJDi5l+SQColxSG6EUYDf8KA/ixEnnygbCgQh+eVx5psh7dJbIpHH/FfYfznces7SuzxKwYOPVJy4U94FMPqXXIkMXo4z7v/CS9jsJhmvI/fpp9jO0x/9kmuQ90n5VWhk8TSsuM01L3I5K8DcUIyPHB+St7plb9O7lUGp0UyWfF49KUi0k0Sl9Mwee7lTr9W2ORwupER1ZFvNBBH6ttUIELRFnyyTVilr8lcpgZ3fmFip6dJuyF+qUm0MhmUHYs2TRY1MeGbz0rfmKW/5BxI0QXM/SW0+rRhvvTAnDNk0mg+LRG6vvsMFFB/oiOtU/oMscVPO/XDPIz3mu0wzPof9XPq/+T9UUazm1Bnk92nbqi+3QCcK8jOsX44eZCh+34FPbExh5FtPnUpOTItKTlN2PULsyguuo5PRYEwtwjjUBcwp5G08+HjtOajmvskrz4WmJNXVnG4PuOfx6nxXf/CTRJTiFFrmWY5UEACJwfe13w0TCg8Aq5DOG3F6ac5+XnUK8+fPhOA6hwknVDjayQOzRVcco1xX44VZO+s/NARO37wAAbsq6zX0KFWitH80rFj8KaJzca+/XAS1MtVP3Ox5Zh9A2P3eNOUJJu8debiBEFSPuquW5FEi2VqbWlDygoJNPIrQC/VDqFVOyCiAClJvWYguXWhChtnv/mNVR3YBziRa4sxhD8eW3JFAIPyUHoS22EHdu22za+8bJmoL44EzPjGk9/EkUBfB+U0pxGAn0F58sjj+epqO7x3n70HMCcvmVKP/fb/9d8AvkYjkYdNOoA4ebltQXosGzDT+xvy1eZ5whsstGkBkBMdJf0kz5zbVq20PfyUztJvfwenAt+wXqSfTdg6gJs//f73dvKD3dD2hs19+mmbOH2mlQ4Z4jXj6wi+8/ol7Qtnq+1PlLe6vMzyUROdgzTX+GlTrQjat0MoqWbK3pnXcsIXmgNCNWsjfrdbx0uXLKb2I0iRofkcrNBMf3n82FH3/ClPvQIsUyAkccipgcK5nTeAn3pUNd989lm7gk20/tBq9kMPWk9UWXN65NsA6kA0y9LGB6CSaCQJQ6knu606yi+JM9Wp8izeqgTI3Ld1m+1GnTUTKb1h2JiTDbX+bkMNqTLALiJwNdoceFmmCarKjtsL//LPdrn+kg0BlP3uz/4O1c/+butv1crVdnDjemtFuvDrP/0797ArVVtXYYYOwGueLwGq8rIrYG79ho228f13rL7qpE1b+gASbo/YwMEDKUe2O4FoY12pus+GPwXGyc6gS2hDTwFzsm8oELodPjnrNuaes1P790G3Gzbh4Ydsxrx5NhrJuRbUogWyC1DLwhEFlZyscYmHsonGAiFv0B4EXkrqez0g77rlr9P26qn7J2zOIw9bQd9etCNGMMJnAaJntgqYw2swy98uVVYqJXWor+sC5kKfr95W5PBTxwDkD+/ynzhxUzK+QE3S8wkDnYMGmhTgwLv0xasGNHUePtbpO/Kv3MfJhjrK+NIdKND5hWpPEuG1BpxQesWdPPdYwnPv1L3z5D0BHagSofh5Q09op0/9c8I6gMKd4gtxcxH+62koQ0xU2Va8HYl7/jWwK/6YOeVThz6LQcOzEFH460E6/Uk+6/RMNx4+Le96pvL5YOG5DKH0vceR+pPwhzLBL0ibhVCB9vpO8Sf58hPvOUfaqX5c4odBWYOayq6OTwk5TanzjkKGeDwtj1lx8YzvdA60CM88zSR4DOpxhxKER0pL6cBbsj+hfMmOgL5VB66dS30TAY1U/P41f/iWGLjgi/S04vubzrHebnqcuo38rxg9XuWPeHUf/6bSosyRrv5Wg4IyyC/yfHiuMgZeT49FGY67sHHQVSjZT9AiycvNDqnalYeNBVSeFJHKqzh0oeuQST1SFvx7v0iChoFQAUPw9LM/TPvzcXRKC5qkq8RDPtJp6JnhTewVkiwGviK8SqK0PFfxpSIP2dQV14Tze9ogfCIe1ZGpCaj6GxVWByenjG4DAfyZ6JHeZ3nk8ZnO+ic6Jj+Piu/l8t0j4K88sQm18/6PiRyZUHLehjTxch7hnAPvyoZYM6oubYqP72Rno/U6KgyaLDKporEpic/tCKmRD/LXpcp6d8jeBczdHbp+dWIN/Yn3TXe40J36Y+KOvY+UonxDjnFLqmZSK7qKvaRaVNOus9jE0pEN6zfQ+qPC1hvPq30Le1k+Cy2pr0ZQLoJxOite9Zhdx6enQByDNMJ1AXPJPCeNnD5WM45pTsBfPz4KmFv4GM4fBMyJOX1858y1Yg7ThtgawvMYZ5Ty8vm0B0nyknyrhPVYgIuAGLUdgQACqSUZpDH+Oi8175GkidqF7Gi14M1wH94u9+B84XTFMZwrTLYpeMUcP3UKao2Absy7fS6h8EgjXQeoKj9yzOqvXGEekW1Dhg/Ddl0fPFvm2rWma26v7SpqtydQO+yL+uyQoiLLz813NVHlL8xXAJC4zgUUaUaVtep4uT3329/ZmbIj1gMQcNn3f2ijkLQagM0t0akJqb/T5eX2yr/+b9RUa9yz5dM/+rF7R5WdN0n/K15RJIc8HQWU2rZxo+15643g/IEyPfUP/4h3zqFeBtnVa+AnAGwQYFEO3jOVt5aWJqePg2BIdWkN0gRAd+DgQduNPbijO7c7CDnv0cdswQMPWC8kyuQgoB4HGu+tWOFSXbUnyqHhVFvwIKqO9811dVbNn1slJUZ9y1FBZflxe/Hf/sX7toHjxtkD33jSSkeORNqupzUBbqrP1dxIass+r6PusilXC2CS1DdBfLxONQ/XXNXrh7gVv8rBpBSnoJJKQ7WVJ7EPVFi1Y4VzYI4HwXnFKfvl//gfVot02eAJ4+2xH/7Q7ikusSxoK/qrL84ENMqjvupwqiAbaQ2AeNl4V+2FFFw/nBpozitwKwOJxSu1F60SVeJ3AfskhXnPiBHE+QMrKR0evNmSJ68v8pfHXLTyxEk8yG63Da++jLf4LBsyfbo9iQfjwdR/Y32DbUHyc/faVXYegHTOY1+zafDn8LGjrU08Da3imlAgpJRtBfStX7/WNqx4G1XuEzb70Sds7uNfcz4VfZpxEFKLVGUvJK7l+VVgYqBlmPuL9m6vz8M2WtWJE/bCr35pNadPWc9Bg2wOasxjJk6AdwYhJQrgRgU4MMdcW/NsbSapPTgATn7cviL0aaLu9wMY7964wSqwW1hA+WYtWWqzsJfXPT+PRqmaon0iMdelygopbnuoRwz82yUxR0PWEU4dA5A/vEt/1NkoJTUk73i0MOXO72OaCkOm4k/vdHhuuVFn4T++SuVaL5NvFDbGqG/9u+SsTswP4vBrjy89DKF5p0PfpTpHj5EnMbIkQIxOt75w9wdp+Uri0HvlKvylzGllSB57PbAHQkdIt+RJ0TnxgcroySYLbb9Wxvydn2LU4YZX6flKQvgpfpV87uF0rQ5d9aBDue84i9ah2P5UQfSATk/3/lNmPUwIq29viLjhpQ8YuvZ0VAa+v+ESc+o4mWrrGXEIDPE68cLrY0UUTgHcCw98QOKTDzv8k+S9+ED3ynLIJGlowOehBianLfSWe22VwevQL7gmX8mQGJJK4kyyFJ4lf29+luKzTqE6bkQLRRd+yV9FkpRdtFDhxScO3Hja+sNz/5/UEuEVMvKe8z3feHDFpUN0FU/xc75jQqfw/owwDnTrGQOIeC8Aw3yreJIkPV+KKzwmfk+BW75XuGTwEZgVfk5Zz5t2AP1I4kpy5Y8+jk7hw85/k9hCXnil+0hP8bEfyhMX4VZ/2SVTvXOVAQipgimfqndRMvB+mOx4/ARU/6MYNJFLHbz0mFUm/4U0xCs69IWOGIdon/40fJKEVd3FX/xSAZSunjMRV74UWhPyM5WVTHKPWwtqX25wWTudAMoZBagnMBEdw0RVLuelAsL2cFInys3nc4RSQUPK3AXM3R2a3w6Yq7xSZ4fOVFp0/hC9smqHNvB1yEvgNXFj0quJ125zBB4Of2/z+jM+Upq3T/czRtz1+V9IgUD9O1u/HmenKMNNHEFDrYe+TKCcDGjLzlM2Klf9s/JtytDh1r9nb7fVI+mYbPpcgQ9gHfBw51+I6y8sbFew21IgjjtdwBzkoR90/k2jlM9eNHZrHFYQ/sig/YepskZgTvOZ2M9695qKuyOFOOdRzD6/T9JQJsI78uPBwzc+byaMb8yRpwDUkCEF0jc0ELURqTdK8icH1KUZYG73lm22a8M6gLmjVjp3nk3Hdta4yRNB4vAQqjSZ+8hW1/VGvJUiHbZtzVqrQ8oqE0m80slTbAzeKUtKStzOXQ32uCorK6wMZwnyADpkxEibgCdKATiZAtuRtOvOHFDS/G0AT2dPnbEjB/bbelQFG65ctv44T3jgu99Dum2UFQJUNSOdJRVJSaW9/dILVo1TB9knmzR3vk2YPNVKAX3k8VKHa0wR7/ZtW7AxtsLOHzlkvQCY5BX0fqTSCpFOu1p3yY7gGKEO756aio0h/wMBW3oW4oGUfImSoqM2qBuRvDqPOuiu3butbM8uu4TK6pBJk2zWvPk4n5hmecynpJnQRLhjx47ZVtLcs2Y16qD32NSF99u9hBsGXbRh204ZFPex8nLbuQubaSvetWxUK4fOmGlLH3nUBuKgIxvPutJikIONZvq8Uzg9qEO6rg1wcAjAnZxd5PQssFYyKLA1rEuIlDrSEftQv+GP94e8kspqKJjmsprDBh7Q5kUmtLxQddr+9b//P3jyPWdFEyba1595xgZiw035bgFkkp27LAA3lfUI6rr79+zBjluNe1gdQL5nUFaBig76EeY6+a1FPXkl3nRP4yGVirEJS5baDJwcjBo5Ksz7KaPsr3WjvB/s2GGbV71vlXgH7jN8pI2F/+5fstju6dcP6bcWO3LokG19/z07CEA3cNgIm7ZwgU3Ge29fbOBlS0qNQ9Kk4vsWQLfyY2W2G0cRh3dtRe26CSDtSZsLmFqAl1l5IT4LXcuRfssGkB1UXGxjUXEugFeDDTqEeaCXbMu1XGu2ExVI/1H3u8nfddYDgyZORFry61YytNRtxl0HmJOdwga8DJ8+c9olS3uzcdQXm36+UU+eVJ8q6yVUjNe+/ZZVQJM28jVy9hybNGs2aqyjLYNyuASk6gs+zEJnOQd11hwk597FVuRLv/534mmzJ37xC1v8vW9bIwMesXr77BAGUH+Q1LVT5dY/4hS1aT/8FK6TJ7d+8IV7opzCw5z+Q4G5y5cv3xWa3TxxP3X1sr27Z7NlIJrcj47qOsYfvTE7HUIWQn2GAehO1pc6wnjo0jtHzmKgMLiAIqvT5KUDLxp0FA4mFFPqm8hsyp3f8yx1KLAecuiSwMm94glLWj3XAlw7U2ogvnOrLowX7qKacwBgwqdJdOG98sMDxaEBx+NXg+Rez3REgE2DpP6FgZUwMSIuvAyKRx2DyuYh+Vh00Hf8PAxnjSr6VBCCOtt2fn6tpxEocsKGfCgPMSldxyPmL97f7hzLprMDc8oPAcNz5Tt0EAF0UNmUNwLToYMW+bXy6EAOHwqcUVk8EhECWnkZ6dkVr3Z5/IJr7cIQmG+C7r2CiwayZ6F0PRnRmme68UkJ4TX4MLp6mgon0ES84uBJkj+f7HgESk8pdxy6E/11uLFUlSLJp5dDaakcSVkUVwiu8nu2FHOqHH6te45O152TDQGSvym6cy+e8X/6OMmHYtKAr7KTDUTNcfKd5Cd08qK5eFr0URGhi8qelNXrQHHre2IXACd6C8TRB3rfHV6K5ezOQO27r4qDsN5WFE5hPB7+6BxOCRFCaRWvBniBh3K/7mAXA5bvBBPeaUY8+pgYPX1F41/7sxCpaBLChPuP+uthQ3DPk8oovhAP652uVUb9czpRlsCOOrPTla0+kAGQsjYz8OtQ+3YQVHHpW8WTnDUxlJSl+EU0S9WFfxnKpopyunObOkhXdZqKixeeryQtbzuKm3gJ6UdoB7qkjnmuSa/Kd40J3Y5NG23Hu+9Y7eHD1saEQXQX/XsUl1iJXLOjKtIb+y3XsS93Q+Cj1196hkIad+tvSAn6UWYH5ijU1TNM9Oqv2ZJJU624Vx8MubP7SaMSS1Cs8Ltdhj6/bN8u9S/ss/TxvZFcXsQLWeWVWjt8VsBctV3P7m69kWjow0/qHd4viA+TI/Sn4QY2DAevUyGSZ2mfJIHu5CkmfCfjTI+L9ntLErc8SP/gU1yLYnc6zvRs3M341ROnV3p6uvDCTcWK/W3nULfexc+U88BnYSR25kr6QkmmC5TTYl2SEL0z86wYtdWpxUjo5Bcy1rFQJHcykakFpvfrnBX3zT8edR2fkgKqH/1UQx0Sc3iW5L6RCtuPV9Yj505atx6AFHjRzESKpht1oxpxfvAOAi5yZiGuD8tHZKbIHB8W7pbnHxrjLSE/6wMft2+KRCWK88JYOAfmKI+Mt69+7XV794XnLFNeWVFxFDDXKok54tG3OnuRNYdQ3GkdkrcPDxX63dR9COjtL/0bXysRoc/Rk3jVfkXa+IvSQJIIknRRy6WrtgsvlruRMDp7ssxGAqBMmTfXRo8f72qTypJURNuZ/zQAap05Vm6rXngRpwrlLlFVcu99NhsQZfrU6cwxrlsZ0mD7AViOb97oYEQfvLouWLTUDflnI0UkAMXnKhSm/vJlKzt61I7s32tnkUrLQ3qpdPpUm/8oaocleIKVFB4AhoDBa5fqcSqwzcp37rTzqN52o0+YRNoz58xBsm6gz7ekmtrOb+uG9bZ99Uqfl46RY4WFC200EniK53RFha169Q07d/Sw2zYbQxzDRo62okGDrRBwRnMo8bvqura21iqrKm0fHm7rqk66dNyiJ58EiMT+3eBBQWqNcJoLS91+Ew431r75uquSDgRgEug3BSCnsACnB5RXdvC27dpp2zdvwC7WWRs4bjygIfRDNbc30ncZaC9oft1CXLUAkTuQrDoOiNiI1NnkhYts3MzpOJUYZs04f/D5JWHVvMRH4eBMXUc+9flkeJTiLZVNfaWDs5xdYu4kEnP/9N8DMEd+n/jJT2wQXq4ziV/9sGaWkvrSBvCmtWtsw5tv2NWzpz3JgaNH4+zi760fAJd2R24wv9W8rQmHIrs3bbHDqLSehdZ5gJWzKcNsPNAKfNQ8WesMSSTu2rg+sd/XamNQEZ1yP2VFIq0XYKmcSYgWW1avtk3MaRsAt0YwhxWPjsBJRSFSb93JZyMeWdtQ1b5cW2c7tmy1k4cP4EG1GpuEw+zehx+1GfffjxRbDt5ST+N04wPbjYRjM3PjPkVDbPGSpdYP1Vj1XRnMO7XmuQ6vXGX82bVjm+3dvN7roN+oUTaMulrw4APuaEU0geHsSv0VOwMotxXbcT2weziCuu/ffwD2HjGwQDtrxnZfE4DhBRxHrH71FTbPm2zAsOE254GHbCgAdG8AVwa00CdAO9WfaJjLJC6n9TrA3J/t5d/80vnn8b/7O1v8/W9bEwMfsnrOq3GdocoPa3Cvmtv+6cQvzjYd3HPbD75wD5VpeJiTgDmNNFeRqO92+arNGDbKSvsMsAG5zA94Jz7Xob4vtgl9rcP7zXD5qf52+2sH5m6dzIUFqoYp+NM7A3V8agQucgpnSSQ0qtblI/khY4m+4KVT8MGOMAKqVIE6HIjgXmk5cEZjUY/mQAHVFwAbRE1pMI2IaqvTy8lFBQzRXYE60keXFE0KlCBOgWBKIlRwGFQ1QVQ+1eFIj152DJR3MYVEkXPYYZLbaS+X8kUEIYfc6J6wDiAhMq68XmeQkQi07BwoDv1aeSa9eO3WZOfmOdMpbyqvOjw1VqAsz5hyJYkzpSHm1JGe3l/CnMqHwsWzdtoEfqUmwhqUyJ/KKtFmudQWveWlRvcajJUvDZoSHRfNFUZAq5efuHidmkwIlFN6AiEc5CB+ASMC5mS4VeLVWq1rh0KdJyX0QmlXQgOHbmUjIpM8yHCrvFW5QU8GPdWB1PpSklBeMFGF0ggB0D2HTjKyqwmErrVzJtHwdnb7VEdh4M5wexiKT7wnHoEyfEyOOInOehJBMG5vrYMknN7dfIjeyonorDz4j4j97PQP7zTYu2Fgyu/gNemLnzUIuett8iDRak2MIzCm2MR5ngT16Z2cBhmuNbG5gg2R8I1UIWX0lcGKNpbF4OHgHGkILBIddKi8ni+lzZV4WPeyRSG6tTIZU31K/Vd2HWSzQvRu5Zk8ZOl7B3C9sEneFLWec+jSb0Ny4Vnyzm9u8yfQL9KN6AnjoBqJRR5We1JeBVppZ7CZHVzlJRseyySfauMOivGtt38vG3ESSLlkfkTG6JvgFfELhcTWMoMwfJfFAJ+jeOA7lVNlVz+lviUCbV4DXji1Jy0yQ/1qAim6iW+zxV9eVoBCtRvVBXFSEU5T9Qf6aXdOeWihD9uJWsBu3MvXHj1ijZcvWTsqLdepg3wmsMVTp9rjqCr0cmCO7Csu6iKkfBtC3oVHoeqgvfhYbRsadAFzd5bQEZhrgbbXiPpSAswdOQswVwMwx8ZbT3Z1e/MTjyWcFzKRtDPx5818kfQahKMWaQe6T4Lf2QIofv/Fv3c4ekWXtLf0mNUn3MlDtLmzMXbO3d2KP+bZN786J9npTv2ljtjfhrsP/6vgIrGGDnGONkR0o7FEUgV0xsyfkMxBskFzH/XbeUiSlPbpb6P63GPDCnq791WN73rnQ0bamcs0zrm7tFdaf+2Hz9FUNxTUjZxD3SauPw6Y89oRc6qi+eavApgTZyX8TqHCkfCuz4Uoql5LRTSX6yw64TWvC5h7Hs+oebbYgblHrA0gQfT0+YU+iP2Qx905AW8jIaVO/Wyq3eld7LPIi9qh/3iszRanv6KkoWg+KDXXbswv5CxAi9eWS1dsFwCKwJGzlcdt+OJFSKPNtVF4RZWBfM1tsjRXa2JeCChy5kiZrXnxZas7BVDF2qsIiZ9ZixfbzGnTyEaGncW+27Ejh23fxo3YLqt2py25ACzyCpuDpGsmY41skbUCPjUBfFy5cA7nA5ctB4+yYwFbJs+91wYiNZcDkMmExuf+miPcYO7dcPGy7d2+w/at22A1R47iKbYQJxXF2MLr504oZOes7dpVu0QemgD0+wwfYZMWzrdpSFf1QW2wjbVAzelq+2Dbdjuxb6/VoELLxM/y6Ft68r6gB84BoJHmg808b8SjaQN2zZqbGvBS28OKRo2yx7/xDRuElJjM2zSzxtNc2yXXoP3J8nLbjxOFPQBqTaj65uQXYDdumHu0FWh5EaDvwulKAKNzlo/67+SlOBBA2qw/aYf5Omsi6uoidK48esy2vPmmnT92FNo3Wr8Ro2zWQw/ZVNQepe0QnW54+/TON4zDcfxSPKkNaOrZD/EBh4IHaSOpst6wupOn7Vf/9P9iMgCJuUkT7IlnfmoDUEEW4CXQWeCHwFl5cl375nJb+fyz1ox9tO5IVA4cMxYprr+3vtjTE7jERNQdMGQTtr661lVURY/asjKk6rBZWDzEcuUEgpbSypxEa9zLZ8448NV32AibAlA5ecZ0B8py4BUBhwKR9wOmbXYnEBtcgrAA+359hg+zLLz4ak2dg8RZI+sWSXReBiRrBdDtiQOTGV97wsbjVXjosGEUIsOBu0pUsde98YbVVp70MhViR1E8miksgbFGfKz1pAC0y2fPWEPtecsjvZHkbQLSl0WkK9At8GU7qq4VOLzYa4fWrXObdDnwUT6by9k5+c4bTY3XkEy9DLhch5fZi1aMDb3x995rk6ZMcycW3fNQxxat9Y+yqj3nIN6ahzHBLNrdu3/+s73y2185rR79+c+wwfhta0HTRTYHHTOgXD6HV3uPfQF1xuubjsAjqcd+EZ7dFPALfKtMJ2tWLsWbXcDcHa5I708SFgh9BkxCGrrWQlVngUDS0W9qaLRLqGZdRL+9nl8rtgncgCjATzYGJnNBquW+Wuo5PRElzaThKAJlWZ1VGDiDylfssOj17SKDQy12Cy4hgn1VnTAdu+wqFAwYAJo+CM8w91gug0R3Oh1JFmiwoK/whboakXapMhHz9YZMx19PZ34BBLcWl9VN7HSoQ+4OSJJHQ+3LjlA/BpGeoPxa/Guxrg5U+RMtlEcvN9cq7zkGtmoQ/nry186CXyLdAuByEMnuASLfH2OW2mnJpyPIAkjUboUbD6XUAgBcwo9BRk1PdNDvLz1E+/BBGOwdOCAC0Y4ZNLsTDJR0NiePH7f6ulrEfhvDIpuOOYPydqdO1LkPHFTkIsTKi0tY8bkj/ORPtGFm7umo/Fl0iDITK+DkPPSrQiVPO1UCOlXHQ7DHUMQAoDq+Bvgh4Ed16GAe37QzGDRea/BJvTwzXYZ+cjGvCb4AkmzoVMgEoT+gRF/sIvRkgJD0ljoz2VIQiOblJB85PG8BbPTdEHaH5DL7CvzXTicrxupO/eUyMehNPfSDV1QP8n7l4K3KJjJBQMXrnaXi5zL+uPS61/l2R5SaDNJsRCHe4GOJWYvf5EFJO46nEa+/QlmbKKd4UPnKKejhnpT6qC3QLrLJl4C1bnynyZkOLbgEEIW2AO2YiFxgQlBNWWtOVXl8snEhPs0ijsJ+/a2oqNj6MkAVUBfxUHkEWmkSSM05MC21B+0g1tBOz58/Z3WoA7QBpstVu8CjQvJVwE91oTrIQxVBhlPlccrpw1m00xH++mX4k/4gCZ/21i9jv8JrP7yMXIlPNMg7LeGbRvUp2Mw4jT2UetqqaKKdrKLSIT5ZUsuRZKbKp3pUfKl2wJ34VPwrY7sCM6uZEF4Uz7GoFPicDX/kaZCmvaoueuGlKw/vZgI3xRPeHoibhH3ToZU2dQ3bHbXwWi0qDJdrzoNuYtMQfpI9lywmDQXE0Qd11CHsaOYyKVF/JPBZvaW3MXjgDEafz7LTe52dvGZ+F4nvCDu1Ao8HsMP5CKoiA1BV6MZGQRtjuvdBXrpAr7v9N9QL9IN2XcDc3aH2zcDc5QjMVVfZyQvV1s541oNxshc/AXMuSRxbW1obi20o5jLtFWwbgDl9Jh6640cSaVxs3PH4ifDmuG++/8xpimB3gzYxY3cpftVsYAclcPsj9SYpn06x702906dp5Q+X/KXP8zB8oHFLc4E2xiCN561s9NWiKsVDK2DzsQhpi1KBcoV97R76wRzGZ0+LqOm+UtdKSkdMLp7D066/n4YCvvCnrnzOq7qCus1EpFkQI7odOFNuh6tPWgYSc7KTlIV6mCR/vGZUwRoj+ebLDMx18BFjbMcN5QrFC2sLyklZ9VrAXA63mXTCawEAVrz4vC/4Fz+FxNyjD7MIB/whoM/MmRenDiJPu/P24fPS0FJCMCWjqyRg5y94nOTBAxPQN0RJQ5vRLk1Dm8pALdy9eGoOBNi1CwmjfYAn56rKbTjSbxORWho7fhzzHuEsbCgy/5AKagPzX0nMrX3lVasD1NCaowQJounz5ttkwAYBOfVXrjKPrLGq8nKrxmlDTVWVAyXih+7MX+TlUpJNbczJRUz1t/nMAccQz4hJE61oBKBHn54udanuX2V1EInMdGM+eoo4y/YesENbtrg6pbyndmM+pbIJUNEvp0cBGgGDbOTUKVbKfGdgSTFgVi5zKdYH5O9c1Sm8gFbZGWyHnTxwkLVWgxPU54mUValqM1mgh+alfZj3Fo8cgafVsTZxwkRUdAu8/iR8obmZC4aQV61Pz1SesmOo5p48csRqT59y0MjtURNrM3O7jKwMgMS+NnwaKsA4uBgychQb/MG+mG92k/Y1SWBVnLC1r72K6u4Ba2R9JbBnGuqY0x94wAYUB2BQtAk//nrb5EHyTPyhvtjXDOGxh9WlSqjnkpoT8FUHPX7zT/9kdRfOA8xNtMcFzCERGDfYPbzWafDNxtWrbP0br+JUoRKgtYcNnjQZ1ePvW/8hxaz7sn0+6fyPdEgm81nR4yhlOLB5K95oq13IxIU3oC0xWo5UPukv8lkTj58+w0bBd0UlJd6HaKNaDhhkruBM1WlsGx6xysNH7GRFBXPkangk1Ld4SM1IGzsy7aP5db/iIhsyfoyNm3cv9CpxoQ4JiFyH7yT5eXz/QafxudOoC+MoRaql3tcxFumQHeYc8tQNybQc6mfoxAlWihTpYEDj7J4IiIifRUPSu3yhzk7B73vWrQdHOE89N1JnF6kC1husvciQg9FZzLdLxoyxEZMn2TDWsvcwjxfNtGbvpnBQJAJzrBIsH2CuO6q8777yZ3v1d7/B5mCLPfKzn9ri73yb+VsA5jRuxrU+F96ePB4R95aD+PUcWunwa6UZbr8kf5XbsG7tkpgLNXhHK1KdRvqRmgjyXHyjnzoE7a40XmnwzrT86FE7xe8yYEQzOyJtACdiylwakPT6+9MAS2H80lEjbQBML6BEEwoBQS7ppEFJDZfYhf5rV/Y4+uhHD+63U4cPgYxfYCdHnfQN61Uy1O4hrvHYRxg0dIjvuPRAj17G0tWlqJG7bSfym4UnmGvYQ6lhUX6CHY5KwKrzJ0/YNSQTxPWZ+T0c6OtfhCoZcZWUDnNwSO6jr2tA8bLyFzoLILnG4HEe2wvH6ISOYyfhEoObdm9ka0BHHgh/TzySDcKLT/HwYW7UdEBxseWghoyojneOvpj3cqcDc39ZIww5IiHy46qhnEUzSQOKfhIBvghwdopybt+wAeDwPDsFSJUx8NBHsotRYD3I3+Ax49xt+NBRo9xVtXvEUcdHIBmh10Dko7/O1E0u4ukyMKsBeO+uPbZl3RqrQCReUkB90Omf8vDDNnn2bBs2Yrg10UlJokiTenXemtRfu3qFHamzVlF23E6UHbOLTB6uAbi0AaYJkMrtDRA0cDD8MdpGTRjHoDgCA669qVNsaVBc1YXKqIl/Dvk4TydbUVFhBw/ss7N0vLIx0QwAIzAvKw+DtgzQ/YcMtWHw28gJE6wPYJ9AJlfVhA6ivHhFZdXgoTTizyud+3iknvNA9A//NICqExKAkdYZKW9nzloZvHZw3weUsxIRb+qAyUQ2u3R5gGh94YdiaDaUXwkgTj6DqADi6/CEH6I5Pwe+WQjVYHPjyKGDdpj45GJe/Nbe2gx4nGX5AqlpD6Mo44jRoxg4AygkEFL51uTP+YO4Rber2C2pPnfWDqOecAIeqQFcbQHEbYFvVM957I5KxaF4xAgbRhsTENabPGuA8okotHK6iXYht/xVK1GeUw/8QpO39CP2K+mPfXLCd96fwCftiL03MxmrPXvOTsIr+7ZttUunqpBART0Csfcpc+61EfCs+MGlUDWgE2Gc6GSRP01qZIeilbiqmYCVsyMoWxgXKWsjQKlokwVAWkBbVb9UPIzyYnx4MJO8Agb27uzytbUnEpjQRF7MLtbUWDWTpBPw2mniq60oZzf5ok8SsjCgLNC1xz2DmLyOtOnsAg6Bfj0H9LNG7TymE0a8AlFySKMVwPwUE9Dlf/oTk4Zz1GORPQwwN5C8ZCJ12wyI6sZrnb7plLx716FuuoC5u0dh5tBEDsxhzfC9pFsEzJ1ElfUowFxl3TlrZ5wo1GYWY5qkWB2YU5/Acbs25C/S/oQlqJqz2uhNjTIt3Ge7FKekt+TPFpt/nRad97G05Tt+pEd5N0hzl+JPj1bXYcQK1Em9S6OXiubF00su/Bt/wB8/eyS+4RViSfub8FpQG2LDkX65hc1Hea5rR0qgAYmTXMbZvsztRhYPtaJCNtSy86yX+t4kmpDHWzkklde05LouPx0FbgHmoL/6FIFy+kmV9XD1CYA5JFeQChMwpzmyABLnAa9nOMkH6sAUkTU65SgO5J+48m4bW6eoP+uN85n4/kOS8v5P5eS/+s4sftksPLIAk9Ysf9PexzaaJHHuRw1yIXNYdkJ8E9JV0RSvl9n/JJfhWvGGeVC49wSIOz18etliqNh/a6NR0vQSNBD44Z8y19CcT4vaDICqa5ev4MF0v5Xv3Q3YccYGz5plY5B+G8EcQ1XiaqfM6ySNJlCrlk2/HUguXUSSSABLEeDSGEC54cOG+RxXQIFsbmljVpvGVcxjqlmztTIHF3CieWA7800d2ijPws5aX+ZH9y5caL2YP2fkoU7oQgbqfRhloI9vRkNPzYHb+PYigg97duxgjcX8njlTG5vxok0G2h1ZbIL2ZX5aPBLbdoBMmmtJYOE68zEBPd2pE0nrNbJe0wboB0hiXURiq1kSuqxfxO8C+bRezGSOX9h/gA0ZO8ZGjhnNZuhQyyF/ZMrXLq5FwbXWWqoUbeRfpd+qZ7P32MFDVg4g1Xq5nnIHh3HS+ilAKGDASCTDZs9yQQ1Jymmdqk1azaNV3uv0hZfOX7BNq1baaYDDy5UVSNnV2vgHHgSce9TXt7ls8Ko+9RM/iFbhnx6E5zx2VlFd6yo2sTgfVj8qgPYSUmbLf/Nru8RccwBzyvu/icdb1YXKpn/QRGsQ8c3+AweQOGS+XHbUcpCAGwiQNnPBQgQB+gO8Mt+E5ySVmcUyL49ZqMpSx4b/LurrDGsBl2Zj7i2QSeBXIXVfQFr9AQKnTpnOul1243KxLQhwS3r0JJ6uwK6rAJYStCgjnrJjx1zwRcCsBHTamOdKXVxrvB6sL4awThk6ZhQAHSAm8+agxYWgAaMHOmvMi5sQQjjrUo7l1FUD4KfW/q3Un3hAcRUWB+m+3qi5jscuc2/KKIBXcyvxksA56RqIvmpHe3buZB1V7YIStUjltbEm9g11Nui7I0DUE8GcSawtilm3S7ikO0Cd6KWfa4IRrwv7EHcuJRcwlwGvrnjlFXv197+Fls32yE+fcWDuOhurWl+6ZqDWl/CgeIfseOV7lXPZ+YBD4gvxjF+La75Mh3Kbthbmrkti7g5VZBw4UuygRq8bpznXyb1AkhqMjFaAlB9ct9ZqWWA20tFJIis7H2anc1IDb+aZREXVIfRjoT9s1mxbuGQpnUtf3yliZArxa1AiDKttpLwu2kYQ7uM7t1sNnQwzCoAWFr90Fi2Afg4itLYjftvXJi1aZFMW3++L9UzSlAipGoQ6q+50QBkMRIdp3DvZxTlOPhvo4DT45grIA72XymUbHYu671wkFIonTLJ70XmfMH2KG/JUC5GosN6fOlllhyQWu2G9g1+tDIgClSRm2z0zi4V6vQ8cmvi0Iu1XQGMvmjDe5j/+GIYxS5HM6xHiVKeqn5M1NMi4eA+szV+vTwLcdHhd8Mw7CRbucvcsSTYBZ5LgEQjxAS6f961aibROKwAEOt0MiCqHBqF2OpA2JLokHtwH4GosNgME6hQhAq4FoEAOB2ColxsMlC7azk5GrxyM03fPZhC6aOveftvef/FFu1J5msE60wYgWj/r61+zSQBzJZTzaouWm3SQlKEVVb1TgHDlBw9bxfbtdolJg+w0ZAIQdgMokg0uDcZyo61JQXd4YMiMaTZm/n02C/FkuV33HQsmGj6pgj8yyM8+dlW2bttsZVs2eWerwVM7V7JDIOJJDPsGoKFE1ksXzLepGM4dP3mCDzjadwHy4B+H6kGDvWo4dp4eg15yQA+vk+Ss9qE68HZAmppMdOcsF97d2LVqgAd2AohuXb0SAO2iSxSqrtXRa/LTTt6vU1ZJaRaNGWsP4MloCOBhPiBkI1JTijwTuuTAVzIyKxB47arVdgx7EBdoC1m9CylnIZPJPKddG7STKqTExYfPmGVjGVwmUJ/5hHGAVfxB3tgrN6ZVPmju2LHd9m/a7Ltkbe0t7BwxAeF7AVbyBEbDRbIPqUrimblosU9UBDzd4L0k55xHoIovDJxIgSZOqORepzjZ0HXsV0S7ZNShqEzs4jvyeRnQ7OyJKjtK3Z7DVkldebnvoLU2X2MS1tcmP4HXJ/hiFLujMrKrSZ1LycLbilOu1LOhW3fqoZkNg3JUKvZu2mCHAJAbr9I2afdZeQW+U6Z6EJiu3qcHgJzcwC9+6GEbNKSICV6+GzZW/tqg7QmA/EPbttlRJjAOwrNAFU9opzuDPkTDp/o5TUCk0t+rqMTuR0x/Bjvc7aglSrVBGxDhEKdRH7i418To/MnT9vKzf2Bn8DST4EH25N/8rUsDt7Pj2MonoqHy8XkdIaUuYO5u0VusKlCuBUJrEa3tnCAxdwFg7pRV1Z1HlTXLJV9lEFuLg3Qbc6n+J40rxH+K1w/OrqLPWaqI8XHy9jOffIL5mWO5XQRweRqb3z6dzmFuF8vHPlN/TyDRxXfAOaen+7Hff1yAuxh/bJs66+d9p/hAaeqnRQDn2C+H8Q0+0LyK/jX2waH0KojGPfGLrpND8cBM+jnIpvkR47L65qYrV90g+ZABA20wmzX3SNMgr9AKmHvk0r+hs5DkKcSVHm2M/q/pLNr9R5RR9RsPXYfxOIzLHw7MsZzMCsAcjOK8EPKuv+IZxaie5DZHZJBPXNjbxnabBD7lI/LlfP8RnzutvHAhL7IPlS1QganWujfftFWvvOxzgvseexT7VA+yBujhkvjaUNPaJHWoXaRuAqW8faU9E111pNdPp9fJTYhHfTZ5Urxqs3rIvQAWXQqgkYRqM3OKZkCEVgCODMDVHEAJeXPXEcuueCSIIOBcbVSOGxSJ1kMCUlLmEDyNMC7IxIqkX6VC6v2gpwdRnFZETtk1H9SaS2qpbmKGZzeQUlIOnVO8rwh59fzSx8iusswZSeXdHVN4fCon8dE/aZ2kuabAK4GSvtZIaBvjkFaHNGya2CyW+RaVTWCc91+E9Twqf8yRMgFV5GVTmieih+ipvMViiA6qOddCUbzMDbX+aGXup7VTqk9UOIHWjLc5hazn0iT99K3iyyENbUxo47gRu3o1aE5UYpJkxztv4hRhgc1hDSSHC6KVTAMp7cALTi2/T/7olR+hTMkNJ93rnfpeXbdTT/XYZmsl75LwykeKzTVskk9ieJ0bWddJK+k60nCIUrLmyHEnH655BO1Fa+/XYVytCTQmCHCU7WPxgdZ6bnII+omOEorQ3FVSh3nMiTvMvQTOV30pry5YozqCtjI51eyAHNCUiMZPZ9WNpBOlWqx8SdqxGwBWAE7JCmkK5PUxh3y1sRkuMFUqq+1J/YsvdGh9LeBQZdT8W1Jzzp/wKyl6nuAOb0OeP8oo78ECsF2zjXiDYz4CQxeVVXmT9p3UoLtnIcogbIDYVFK1Ay8HbVNHfjfqAWCuG7jBCuzSvfb73zGXb7JHnnnGVVmvi0/41sFhvvUykkY40ngzeRJPIfYYSueEb2KAL/xZJVA9hrW/ZLO7gDmv1c9ekWrgHQc3NCotNgXaSG1VDK/BQ425DFsABxC1Pv3BbnYW8tgNKbEhw4ZbDxBndZiNoNxX2F24UFVpFR984ANev2HDbO4TT9jI8RMQyR2YkoSSVI8q9BxSVWUg/5vfeYedn7MMLNk2Eg8//UqQZmHQlJpg1dFjdnL/fqTVTtugceNt4sL5NnfpUtTIBrAzg/ip2hod+w0GnmY60B1bt9qWNavtAp1oP3YBhrKT0J+8asCSZ5ka9N5PkOa542XWY1Ax7psftZlLlzgYkKHJLh2ORMYPs0OyA9fc5YAaeewWF48ahShuESpxSDzRkNuuoq8O6HIZ8OoQi/grtedRDexjM7F9MHbWDCTohlo7HZ0j6KIjhBa5VWvxLNp7f+ZPdNf50DeqowjMCfzKY2IskKEGSa3tgHKHt25ySa3SabPYnRplfaGzOheBYI0MKmdPVbqaoAbuaUsfQKqMcmgXRgMl6bYpEcL7YM/EXGLShQAJ3QDZdm7cYjtWrrLDGze56HE3OrG+pSU256lvYvB1NmUsZcEp19x8z7fVDF6HUdU7Qh3UVVSg7tfXBpeOcInCHCYNkqoTAHUFkeOaU4hEwydZgEolk8fbAgzNlo4Zbb0A55rxsIMSrmU1t9m5ipPUw2bbvmkTQBguv4uLbeiIEa5+qYFfO5FXsN918vBhq644jgTjYJsA2Dp5/jyAw6HemUf7aSqzOmHRXxXiZy59oqRnHAzffq9nor0viBgkMgDlZAdCBoVJ1L1K7d25CzBoE1KeB5AkHArQU2oDBt3jg5HoIfC64shhuwjgpl2nCTKSu2iBjUZ8/gaDlaud8ly7UVWIzO/fscu2vPOu1bMwykeNYNzsma4umcMkTcBOPTte55EKkxi5ALuhiHTPWbLUpTUlcdNCmxVomMlAd7kaL1bsqG0FrKoVEIQ6uCQTe1L3AjB11CGGXnX0qFVjQFh00YRj8v0L3a5JLhKH7bQHNzQtWkGL2ON07jc8qk401Hs+CR+F1/5X7V50kB3EqrLjdhQ7IMfWb7La4+XWcuGyp6GpVuHg/jaN3cLpC+baSIDgZq8IeiP4NALxoplUrluYnNYg3baV3eMjeAG7WFmBGsAURN2Hu/McqTs001YvsBurdtqIirxUIhbg0WkctiWKSoqMaaVPLrWjuAOg9cjWLXah/LgNGD7KJdr6sTjVBFP9w3X6Gu2anoRuVYcOs8N72SYuw9PVow/Z8InjmdgiLcvALN4SvdSH5ghApk5qqs7an599Frswp7DxMcie/skz1of22kq8DszBbKGnSCPaXbz0OiJFlwJNJq5dNubuHMHVZlxaDkLLxpyAuXoWgZX1F+zYudN26uJ5+gFU8ZkkSo0mR1IuGoPSsuATYvgpPnO+UsR+6Kl4jX88i0/jOQn0qU+++PnUX3/8hzF+L1tqMtvxXXzf8eSTXylup0ek2W3S+eSxdnxxt+L3PpTIY18qDlBa/lMZknKobD5+xWcqZ/oitCOrYVxLu1ccDswxLstcgqR6W1mAyBxDd/qr3kgHTxjK+C3JasaM/AzsZ/KNxkQtqnT+qhyxTYn+n+cRFvshRV3/RcAcc61Ml5gLtRR7jw4O0tDc0V90Ko8zE08+cUEjhTrFdmdukjypP/jIbHlj8D+E0yYq+/zMN7R5t/6tt1FnfY1N/x52H9Jy8xYvRuom3x0rtDDflFR+bFOpdNSmONJppdgjHUOJb6VjzGM8eyTqhWJ8SU+ttq1D4fzHvYqqx7Ea9P7mI/YJSTThYwKFLk5fk8POiXsUMb2b40u/j+nemocQof8loXBOJZ0exS3X6XGmv1QcMU8h9vS3t14rT7c7PP7kZarc3Mc4dfa1rD7WcwLpG21qOVjokSoCnifAnCSIxT+yA9iGV9CyI0dsH8IjFXgZnf7Ag3gY/RoCD8GEiWscKU5F7pUQE485SPKiJHTEx9yrD/V+VJ+GGDyIgsbg/oA/8bN4js9jOD/zMt7rvcKm+CW51vObj1hHtzznQUwv1pXCxGe6Tk9P9zrie71zWuusFzp4GePycLyI4UOAW//G/Hl8SRwxPn0bfvwVbyZMoDRi/5lqw5F3kyR8Y9PbPg+SfKgKBUAK0JOQQy7L4oyGFlv5+qu2/I9/YC2GxNwzz9iS73zbbQRLcs9ttLI+6QzMJekrc4ozSfN2p/S6v937L94zlaYLmAu16o2eS6/hWweET1p5sXGE74hUIIFQbgAMGZ0XSMdK2G1T7QccObB+PdIil614/EQbjcTOOKRZ5JJYotQNAC716IyfRax584oVdgpVPElrjVr2oN27BHsJgBEyrJgNAJCtCR5p7dm+w9ateNfKWFQXIKZaOnuWzcVjjACVQtRL28jgob37bee69XZszTo8CTXbYMSZH//hj6x07GjC9Ha1uxtIurRcvALAUGnbkJTbs3EjC+lsm7LgfpsJkFcEQKcdEKm7nUbdUGDTNsDAa7WXbPyDS23m448gNj7ZckH3BahkIcV0BKBn365dGCYts5JRo20sKmtDhw+3AkASgQICsJoRwz4LYLjyvfetfPs2JH7O2fClLNJZqE+aOV3bLgBDNFY6Cu8kILTU1WI35I2R2041GTsVVYo6EQXX5IJrgS55LPKrT1XZgZ07bP3zz2FoE9fmQ4fZnEcfw4MT6r4AUwLQBGBcZRfqGACDPNW0MPGeQhn6AczkaQdDEmz8axMzkUffPWG3QvWT2dJutafO2ku//b2dBQQSCCkRYKnI5qEqMffbTyMKjMQcqqzt2GrQzohsVRwvK7O9G5F+ROJL4uKjps2wiUhNDh823PIS2kpNRiLwlccrbP27uFI/cRygKMdmswM1DWO3pYBH9UhNqUO0K422Y816fmuQwtuPVNc4mwV/SH2wl6QgAQTlzKIONdl9iC9/sGmjqx0OnjjRxiNZeR8AnXYANfGSKmR3QE0ZvRVtRFb9nPrqOP2KO+jv73imx74wZuEiablsvhNg2HoFF+oVFfb6cy/YuWOIkqN6OWHZAzYeWxpDkCJ0u4p07JKo2wf4eBBg8wS8JBH/+7/+DVv4xKPWj902OYnQLpmAvu2bttiat96yyg2brc9oPEktWmjzliyyQaiC5yBFKhUjAUcny8tt66pVdq7sGO0uw2ZLsgwDpsNGjHBAU3Yl2uobMM6707atXm0Hd+2w3qQ1A2nTqXPvQxW8P3yOLQ3yV4MKwV6A1D3UWfWe3dYDu4HD5823B5AA6wvAGNWxfWAVXcQrHM6TfpX2B2KFt2nvk/DxG41/ksyUEdZj+w/aQSRbK9dtQOX0IkaF2d1kAqQBrsegfjbzW08DzM2z4bRzAXNxAFXNCeCTrKSkW6UyXbZ3r61++RWrO1GBDb4+dt/T37JRqE+UYJC4EBWKFtr9Gfqlt156yU6ysSA7kWOWLLW5y5bZlBkzcLzQHWcR17CfccrWvfuWnTlwECSlxaYsewiPXpNtCNKmstejRel1FqyyX7mX+ty5dr1Vrd9shYDVo5cssAe/9RSqBP3ChI/8+R4cRJGL+wzq+EJltb2KKutF2m8A5n5K3Qx0lXeBc9iZpWTius/nCCl1AXN3i9pi/3SJOeQyAjB3+YIdP4960UVslmKjJJNxQuYFpMqqnWptjInPVT9qa3GiKe5Ia2V6yy88CT1ax1t/8UX/Q3sKJUgyGm86lfmLXoi7kz8nBXTQoWvnA67SSOQLHt9MZBzRpoXGXN/sEf30IQwYu2BIHca2GIFeC8DjJ8kCSd9I8riVeVwm84A+uRhM793Pxg8ttX75hZaveQHf+CKSc1o03P31H0lVfO7ljmOuKKzrrxwwF1fklN/n0TqLGDcdXj8J3zvYrHmC5s3MNzT2rmfDc/3y1x2Ym754ic1kLqRx/7oaBudszEn4xq3uY7+kaw71rbEd3Zx65IubsuO3nfOZZM7ju13oD3+mtq8jjRThQfI3vu/08A7fBFKoROm0uMOJ3IHokirz+kqnf+i3Qv/pf/WAwCJtrEMvnfpM+kQ5NcyhP9VmvJxA7N6yGc2TTW4qaM4jj9rsB5ahycEXzNs6wJhIm/RYQ6HS85JezLjxpmcSlIh5SQ/Tdf3RFHDaxopPCxpoGesk1nOypiac+hOvKSJQHGpHkuSL9X8N+/nd6TsyW+lHGltt44p3bPWfX0Lopc0e/SnA3LcB5ug7JNwiqbnOvJCWES69ZkOG/EXaZXL/Zat7laALmAtclYwO4XRnKrKjU4fQ/NegpvjlEcf7Ll3TSVVVlGNItMJVFkuYrJWWDrf8gnxn6Ch2KpfKjah17gMU2LV+vZXv/8B6Y8NqLvr4s1GjlN2CXIAR6YRrZ3bDu+/aypdfsvrTp2zcgkU248EHbKxLnRS66LK8nV6iU6w4eNhWvbbczqPel0ccC2kQk7DBUIJ9pla1KMCNhhrcM2/Yf4lUBgAAQABJREFUhOHHtVaFnbriGTNt/kMP2uz75ri6Gq3Npb7akAQ7sm+fbV+/0fauXmX5g++xkUhXPfStJ61v337QALFU4rtG/mQAXjZWClGb7IkkVzZpS4JQhjp7odYhQOUSINN2JMR2IKVXvn+fDZwy2e5F+msG0j43UFNyiTloKFo6wCGK8Z9H3lhF6xsdlcBTOnqVieeqC4FyOsvmm4C5dsDOHRs22iZEqs8CMgwD/JqydJlNnDotOD5gkQcC5WnJI2s94s4SD9akPUci5QJbPfU4KCknekTnAzJQAHh06vgJ24eR0M0vvkSZC+we6lrqzNUnKgBVrtr93/+uTZw9ywYDoEoNT6LFAvYuApCVAeQJtOqFlKHAolLqX+Ln7snXFwHsziPdKNsN6wGY9m5YB2BRYSMBthY98ohNRdrwKqLCGK/DDkK1vfbsc1YBaIQMoN2HbZCpgFAjRo1w/pB6sUT5JWVyAdtsx6D/yhdecCmvYmxuPAi4UwRQlo1odgN0Y+ZFXmUTQBQI7SeUPrYlaBLrRtXEy2DTAft5fKTd10wkpuqqa6wc8GYFuydSdCyizpc98TiuzUvcEYDUSrXYEYgjld0tAMuSCr1KHkdgN2T6osV277w5AGSAnUg/CjBehwvy9wGOmrCRcO8Tj9ncrz1m9wwtAURF1B6ekAprO/FdxW7G3t27bc+atUh+7rVBSJguwEvUvYCaspEhHpFk3dtvvGX7t2wEfK6xmY89bjPmz3fpsyZ4QuylBZzq9Bw0PkZ7WPPqazjVqLF+2F5c9v3v4/lqBDYjCx1MV/2KjzVx0P9O7CreSQ7RTkfqvfg3OfRMr0P/ch213bN2qqwcF+5lGIZotvaGJjuy+wMcypzFDl+BzURibtqC+2z4mDEYuWbYU6OArgLu3DmJsgJYeezAIdsNEL8XGzLdkDoaOmumPQyfyP6e+F3hpUrahi2LDbTRXavetxO7d1rvYcNt0Tefoo94yNXTJeZfj6SIbGbUnD3jAP1kJOruGTQIiaY8Vy9gRHbJSQGCZ9gEOLhjp6194UWk8K7AA3jR+sXPUWMfjuQv6am/ofwqcxaL2m6oHNRWnrXX/ggwR3/XB0Du6WcA5hKJuTapjdA0w9QxUu3unkN1dQFzd4vKYn/tpmqMksScLPpclcTcJfqPmjM4fzhnlzEFoP5CKiuS6nZv1fTV7pAntjdlEP4P9RWuk9akEH7E/iy5/RKdyHksRCrXoT9OlTf1/Ct2AV1U6+FfvIYG6gvFD4zzUuuSqpC8V8t+lbw36qcFn0sJROIqvPrPSEL6J/f4rTGU8ecKUsDqT7PphHogbVXSp78NwV5wESqshQKN+V7AXKyTeI7R/bWfI4t+3uX+KGCuibqVIZFbbMz9NUnMJZMK0T0Ccx/Ka95eNGcL8zYBc+6xEc2L9cyv1iHxouYgEzYl48cGVU3mr/ls8k5gvpiHyRVvW0qABNXudHjfGhkg1QJ8KuTvP+pPB78QS8fNR33yqd6l5lyf6uuP+eh2+U7R42O+/Txfp+czyV981LG5BW+EyvW69jltkkfR0OsavpHGEQsnt1l2cO8+q9i9C9vWVVaMNsb4mbNsBJocUs/UmiLyjI9aStcTvYlAafmJbxTMfzwQb8Tnt5Lsw9/cGvar98TJ7evajrKnKCba6p8H4trvU1Xm6wqF1apYGmmu1QPe0VB/xQ5hm/AaWnjd6D+6NbfbcYQcytBQ0zj66M9+DjD3LV8fpQNz4od0XkvliISVTjzSr/XM8xhffinOKkEXMCcaBK5KnTpX9Gepy7ho9jjEuRzqvFysFzBF9g/qMbZ/FSbV8z5IqvVBok0Dl4NyfCOpnkzOLfVIEyENtWn1Stu5fg07UQU2H6me+wDd5IxAwJyMnV48BzDz1nJbByAgaZS5jz9hcwFmBgCUubF30tQCVw4ILp49b6vfetcOb96Icf1zNvXxr9m9i7ANN2mS258S+FF3qtreIa4yGo+cDMxA1Hj2gvk2esxYa8KOlsrgAzVngRFHkLJZAfB0DaCpeNJ4+8bPf4ak3jCE3LCthvMC2THTIVBLEoHBmyalp9OW/bMeqPMKBJFr6H103JveftN2b1hvAyZPBFT5us1evBBpMlQJNCEmfOg8iJB7/bw61Ry5EDCne4Xy137NHbt9Sk/f52qyzbXsv2xmkrHp9ddwc15rs6DFfY8/7lKBGaQn5F62IrQTGHZgABr5zlUAmcgrIZ/sKUNK1OsbwImalSF9AZf7kHiTVGH14aM2euIUPPRMcK8+Ah6vXb1ki3/0A5sAMDoI4KgVQEG79YqnEbpfBDhqwHZZPmoChagNFCDuLQlM/5GejIRq8t9MGKmo7lj5nh3fs8sGAVgtQppsLpJibXR8zQBUpw8fs5f+9d/sXHkFzh2K7MmfPmPDx41DfRpnHU5Q+JRyyk6b1IrlQenl3/wWT08V1mNAf4xzfsdGwiP9MPTaSLqyU6bFjL5V0YP3JRG7g/5xgNRZdHdgjgHaPRzBEhlIEx4FlJNtuf3vrQDgKbWJ8OL8pUswdooEJ3HJmLCUSGTbQXZODu8/BJi63g6uXoPDkL54vroXd+9f93YkT7VyfrJ2+Ru2hjrtcc9Am/fEEzYHSc7s3j18oa4BQ6qsAqS0eKpGSnMDkqZbV7xNeTKh25N2P21HhkzlTOUM6uSvPve8VSFlmIOR1Cf+9hkbNXmSe6692gY8QF1lUSc5SBC2Qrdzlafs/dffwPDwHiYkrTYTYGvcTKljlzqoIFsjzrOiuQjHIdrcfIhmOjreEYj//jh+pwCk34R0hjyRtTAAZjQBOKLevOqtdwDqDmOAtTt5+AYu6efasNFIzEFVb41iM/65oVd4uuVqo+3Ztt22vf++VW3aYP3HjrdJgNT3P7jMelIX6r+aAEgLkRBU/Z1AonP9m8tt65uvk6kMm4t3tvn0E/egog77A942edtvRKVL+S4ClMuWpCdZlhFctUV5psrvno3Nvho7tveAvf7b39KXnbH+I4basp/8BI9R4zxt2QmJwFw2oIuA5gsnz9jrAuZOSZV1oD2VAHPi9y5gTozx13XA5nAu4BwNQACdwDkH5pCUK8fA9wmcEtWhWi0paplHcJss9E/q0zqBKopIrSj856wWlbrhOrQLhfoyHJ7PJLPhOul/vZzqHr4sJbm71FYtq57dezp1Hs++08+9xjKBcponyb6UrqU2r77dv6W/cjUr6OkbhPQzkbKSCmjGps8NNgzakKxXf6bNp15IypX0H2DFfe+xQTLgzXxINuV8PPPceKZC/OHyK/E30i3UyedX5PS2oGvNXTQW6vxVAOa6xUkFZYbjQ9f3EeSXxIsW2d0Y+x2gY/6uOZOEANa9+me3DV3Yr58VMid0swHMXXtj8H7Jt75lvXCuFes5MHisbfqnzi88B6lHH5Gf1KuOSVHqUdfF3aOA11ysIG7EO/FwgNdv1R/GQLxNKtnbGXxz/ix25Y4dYyN3N9pQdb6WnbLwfnf60Jc+8jqgbmTPGEtsr/E+laYubnrYkaMY6nbn+FE83y7MV/uZ122cEyWk6KBWRw3rWfgxHhJedRefqa9w77iKB3ziUk2NrWOzXw5J2hkjuzE2XjxZgRdkhJMYax955ue2GMEPzd215pPJHwflGCs1f4uH4vcD3kpdx2ec4zO9jddpr7/Al8rtFwCYu3Tp0l2hm++oU8RgyBWnA1cv24o9WywDo5T9GDDk9cP7FKdDyELoPz57RabYh2h1HZFenbVI0K6BDHxKYk4TOWZ5vugO6q50SsoIzzMJn88CVhJz7Veb7BKOItZh/2zje+/6QDoPoGreIw9bbo9CPHyhSoiEjIy1b0WKaPeaVdYDSaNFSMjIIGtmAVJEpOeqtCxoCwh/nThlY2w74ct3b7eh8xbY4seesNnz7gPEg3aoFp4BDHzx17+x6vIyH2Af/Zsf21h2wfoy2F4GfNOiWmqIMhovg56nsV324u9+Z+dQpeyHvacn/uEfUFubZL0AHGuxWaayiQaUODQZ7iVtpl1jB2kY/iX5J+OnBz7YaxvffMP2bFhrg9hNmfeNr6FCu8Da6Lj94Funr/7SkEUU1aQ3Ri5uC8zx3EE56CswNAePs1KvqzyOKiPA3J7338VwaYEtfPIpaPuIS2o1U18tACvyPKV0lIZARbkTb+ZbqVi6ai3PvTMjX7Jfp05JoI/szEidb9fK1agtr7M8VFnmIck4dvRYHHRgWH/HNtRZr9iSv/kRwM1MG4QKH8qHqbTaSEd9klQClOc2RH5byZPSzAIEEqDSAoiaz3sBFQfwxLR95QpsF26yXkNKbclTT9nCh5a5MxFJIlbs3mev/H//E9XX8zYE1cQf/9f/YgNKivHsRNyQVtIC8ngpvpQjibrq8/YiqoLHd21HTKXFZiERJcm+ISOGJwBTBOagDWX/UGCOMoh23teq7igD3G259OTdAb62ILG4dvlbVr1vr43CDtxMpK6m3XcvhoVz8MyJoV3CZ8E7bpMH8lykPUgq7c1f/doaAMKGoPL63f/0NzYYj7k3oLm8Zq2Hf7a+vwIJuGnYQHkAtdM5ZjJMS0cvnpX0XR6Ld4nXt8Dvawj7/p9ftkvl5TYfYG4RYNoQJFlll7GcycQrz/7BalDflNTd9/7xvyDJNRTV6ixrJG+qe3k0zSYutQk5Mln73krbs3Y1UpFlNmLJUpuzbJlNwjFHc7eg7qzBRxNZP8jP7eaZcZKiMPF98gUE5SMR1Q94DtA5m8FOvJDBbtSFU6ftpT8+j93B3fDQ9QDMIVVYOnoUCxD6oBgheVcbzGjDVh3SrFvWrrUty5db3dGjNmHRYptNvzBx+lS3CSfDtLK7l8/iMi8DT1W01bVvvWnvPf9Hu4K9t4mPIlGLrbnxU6fCo4C7rXi/pc+RdKfTnWtJ0slGYQuTe6mw5/GsEMPajXWXrQIbcy9TpxfOoppaMtjmffe77oK9P/YvxQPiL5Esk3oTv9eePA0w9zybCJXYlhsEMPeMq7K2Oz/Dk/C0t8uESnf7FOomSMxpIQPZTTbmsthcWTxpqhUjLaP+V+CyV70+SFXoTbn7sOc3Bfsq3YrlIZ3bQdVZaq0N9JFnsJV5srbaTl2oscsY+9YGijYunIT8EQ/4NRGon1I8OtS3+ttUQF2kjSV6nRypphYffJHOsUDkKY6BKqTn2c9cfaELcPeJ6VUsLqDhuZoq/ZOk3l3qDRq1APxLKlfmBmSDVAbWc9BgyJHUJf2Nq+TwPhh7px/SphRxRRqrX5NHxOtsxN1AmrgFTYeCzBx38jC8eIgNxNOfnEBlkQXvbymywLlkRnP3CfAFSyGyY6iXzy9zPsdOkvM5CxlQX3IzMHfgTIUdil5ZBdIijd+NsSqtN0l6lVAC71duV4w4iH/igkYK3S7ST//skwFzod9MShg2VZkb3mDutBMNnu1spDbi9VHvM5g7OkADQ/dlTvnQ3zIWYzYkVQqPJBKhow8WPeORChsffNQ5NX/5qEBd7+4GBUKNwRupqku/ToYar0z+JGe1j6No/xzF1MvFo8csB8/pA0eNtumz73XPp9oIkRfP1PhFxn0E8yhiPElpSNejjbfpN39RgfXBJ/7oL4r5yxooVZXeHmN9hvGt410H1WK35vXlhSas6iUJHIA5jW88wEzVpfPnbe3LL7GGqnTnIRmsv1vRPmtrusY6L9/mY0t+JniF1qKhP6Z+HJTTOJuMkvBQ6vB5TeoudRFDdOQr9eoLfqGcB+xAtNNI01BD34qgxfTSUVbaZ4ANyMUZJe/inEG0jhhTLHdC/k9d1m5/rcBcIEz630CjQDjYhYFNkzhNnDW2aMEqQgsVls0lAXPy0CKpnlwWtJIoqjtz1tauWGHr3lkOk5oteupbtgD1vDxUQgUsNFyst03YjPsAUK7qwD7rN268LcahwLyli10CSxJeUuWREwbZNunGwFp57LitxyvK5jfesMKSInvou9+z+7Grlol9ryvYtjux/6C9/M//wiL5lA0cO9ae/Ie/d+kqOZG40oJXHoCAHDIjG20yxF5dWWV/+Pdf2mkkdArxfjn3O9/F0ygOFIaUAOTh9ptJrBZLUgcVdbRscnVSzlJlzaTMArJqkWBbQVkPbFjnos4jaayzsD8gG3NC0+OOtejmg3ogtXez3hihWzowRyBPT7QOnoRYtEFx5V3egTZgb+0wqnsXKo5j5H6yzQNAmY4jBnnUuQYQITBCHn3kdcgBMtJtxcaagAJJAcqGUXdsXknFL3iuaXcvr7KtdxmPQNsxfH98yza7cuoU3lIX2X1z5tjg/vfY8tfesEMYPm1tvGL3//hHNn72bBw7DEGVAt5IMt1G/CqnAE3l312P4xzCPevAK1IBlHi47McJnD12YL9th3b7Nq63XqXDbCk7EIseehDbH5lWgzv4Yzv32Du/QiIJ1cISJOWe+b//G/b0Slx9VsCcA8hEqc5U4saXz9XYSqSuDpDPxksXbBqSmlNQ8RyGOqSM64OQuHSmS8yRv3RgLtaNd9YEVRh1OPp1AHPi71bbunGze6s9i4rt8BnTbcbDADyoisqu4TXsIEpaSlIL8mBXiFqQvNuW7T9gz//Pf8ZOXA1qFBPsuz//uQ2lzFLtLC87bhuQuNy1bg0qkQLmcMeODZSMHnmBT1jMt1O+fOounzhljHYjKtursElXg7j1VGwpSl18PM4g5IFLwNyrz/2RQeUEjk+K7Dv/+f9wT8HdaCtNAFXaU86iniTRV9gdj6GAhduxbbf57bdwyLDNShctQsr1cZsF7RoBxVQeLQJdMhLaxDlCp7km9IJUnY74PmH58KECUWaBXDkgUXKmofZdh624F37/J6vYi9oy21PTJTEH8D5sNMAc/Ct7iOEIwJxsx4A4Q7e3bNULL9g11HcXfvNpW8CA2RM7enQe5Bse56e6yFN5iWIHvL0WNfBKJupFM1ADf+xRu2/hQrxLa0MAhzd8p0Ws2MUXQur/+Mm+ZQ7tJp+Frzw1qS2eYOL2EsBcbfUZ6zcchxLf+54NQ8VBDkxEL33vEp2kf0PAXOVpjMe+gP3Gk9YbG35P/QTnD7iRlzq4+Fnquh20Sop7F08hrS5g7i6S2NuEOFdsr024ZvjiKuD9BTbg6jCVoH5JtS5+0y82ItVN5AWfLOpe4y1HHDfCddo9r29ug/7BF/JPklNOX548f56EFFewGw9TSIPgOn2S/8hCE9K71bV4ta674BoDkpTLY9MzvxAv3vRPDsyJe+h/wi/wkvMPz+TYqwlvhXXMXVqvNFgefenwvkjI9e1nA3r1tb5IukuzIYc0ZcszAnLiz8iTnyclvghpRR79vMuvMSQePh6RAfUnXcBcpEo8J2MnfaQ2uCRjr41taZnofI6Nv7PMabVO6a65MXNB9aOCVmQyp2TECGwFs6HNIYqHrjbWNiFT1RCffcJ+K06GPIWuP58nBUKNwR8dVdeRvB52VG5apWJbjvWQ7MtdR5NC5ox6I2lZgDCEpNtlfsJhOb7t1EaJOf0+JqR1RfrxidjBA6cYMD2ar/R1oKEIy496TJE4vaJVP6ISL/2c3Gss1H1c7+lbX+8pIGtUaY41oP11CRMPDVfQANNGB18Il5Ckeg8cIvXo3xeQLgjBqE92CVxhIKn0ScETTZ0I1flIXhMsyWfn11/gO6deao3cBcxRVaEf+WwVGTuGwEMplk4xgjodP5z+YjstHALz61t9IUk5MbEDcwx+uagU3sBeybGDh2zLypW2e9M67FT1AJh72hYi1ZWDWqOkzOrP19o7ry+3Axs32CUWqUXsQixEfW82dtkaAXLcvTGFVPwFApKIu+7MeVvzysv2PtIu8sb6yI/+xpYgJSS7ELJXdgyptVf/1//CA2mdlUybak/9/d/jSXUEnpdyHbASMIe8mC+sJQFSc7rafvfLf7cqpJ5kvH/Wk9+0KffNtiHDh1sDE1/vSElfwJwW9lJhE5gkD7DdAATk6fF8dbVVIMG2B4mv+vPnLK9XT5tBOcYD1gwCtGpBBEXgj+ilxho7A9EyNkTVZWdgLoQXfePEQrbAlGfZDnsX6SA5V2i9dNFmASpMmz0HpxSlVod9rPPsBtYh7adJWy6TjIKehdYDW2+61mREIIUkgyQVpBoVUOaeq7i+BHhUWVZmm5HEunz2HF5P+9rSx79uo0eMdOmkN1591Q7ipbblWr0tBJibCBgoG3MCbhSXOkaVNapjiUnViSlN0VJpBbVQgBLKLFVVOTzYD8B0Gq+mAwEZF2ILbc79CxyYu3juHNKRSJn9268AMqpsMHX5w//zv1KnI5GqzAOsCYCgFg1yWCHPW7IzuAl10V1r19iZ8mM2eukymwtIOoH6cLmUTOogGVSd/qK98u7crHoI97HuVQc+sYPHxTs5EDYD4GvP9l22Ef6uhB6DAAynPrDUFixZ7K68BcyJZ6RSVIA0n9rEVYDjcpyJSKKz5kwVUmxD7VuoTg8bRlmojyp2ZTa++zZ2ClfZgDHjUekG3F0I0Ef9tQIWyd07hLQCJB4V5w28C2/FccJqJCersG84hvYzB09jMwCaxGMnKsrt5WefRQVY0qP97Ymf/sw93ub37hnaAvwkaTlNUvMzsrzNHgAE3YiK5+6NkvqcaYuffhrvx4vd8YKkzgTOuZtyaOJegjmnH4GO6U/CtWioX+xO/BKecGAOmoqvJYV48cw5e/73f7QTH+AkA2BuGm176nxszFHfAZijXehjDp9cIzF3oxnbfK+95rb+2lEJfuAHPwKce9JV5rWTJRfmUuFWWto0yAVY20c/sXnNajuCakvfMaNs/LJltmDpEl/YageULDkNPS36HR1qozIGLJrl8ZP05EXsDKrPef3Xv7YrgCyDcUyy+Affs6KRI2h3PdxxjXrLCMy5xJyAuT+96DYVeyNV982f/BQP1ABzWcGArPOdp/j5/An07ALm7ja1xUX6qceK4FwDY0gDauWuJi4+IYD42uuEP3GM1b34SO2a/36IH+Por3j1E5PG/ssDdf35ElMgVLTqXfYJ9dN2iqtEU8+N11vsRPVZq8Czr+Yqch4ibYR85kIyxRGAOfFE4CfF5nM3vhUo18jcpQFQuAkV1gwWIH1QzZ8yZLgN7tvfeuJ9VZuX2hTV2HozKBdy9iUm7afMurcxvv28y5++yO8C5jr6wFurUa0FlqeTdDCbubfPuxnD1a9ea7jm/O5ehRnD2UH2flPrHG3EySu2NuJT9ewVHWubvjW+SOOA1KNbM3PrE2Wm6/gPoUCoxY/iHQ2foX4CF4VsajNWGj6qfNl6zhNwC38ppMZfXyPzroM39NSD31LOzza3CyneEulX+EFsmT7p0dwoaZeh2abeemWEmoVYPPZ65oHmU7rX2lV1w3//SdKchav/tP5uZgOrmXWG1phxAubf0n+4QzjW1epzPA3O7ozJY1Ll8DSVuO5vPeJrcU68vjXUF/GJctslMRcqOOkBwumzVWQcJ8SQgY/8yjnAeTNhE2dYMRyB9I3sNkm8XLunAloEzAms0uCnBWsjanGb1iINB+gmabR+o0faQtlBww5XdyaQUsu7fLbG/vz8i3Zs2xYkT+qsFL39+YARU2fNsgbswQmYEyDC2hoggp1b4r1ad9FWvvJne+cPv8MIfos9LGAO1UcBT3WAOId27bHl//5LN/Jfin2sp3/xMwdAmKW6eqHikyql1AFzEUu9iKjq73/1GzcE3x3QYfrXv4b6IF5ByW8j91LdI6u+uJeUX3ckAc+fOIkaIVIOAHT12Bw4hmTSiSNHrInd656opg1FRXEeQFD/IUMsGxBSKngikdNQf3WRHIrbGyMXorcOPfOwuuAuAnNSM+3einMFJMLeeuUVq9i+1TIAQB9HHXIo+dXEvOJEhZ08VWnna865+nEuu+d9+ve3AYjnD8BWVm9UenPYWZetvDApQZ0RUCgH4EgShMePHsOT53bb//57lsvO+SgAv8eQSOzDTpGcHbz12ut40dxE2ettwY9/nALmrqm+lG/fQVLUkjgKEnPXAVkFzKk+3eaH1yt8Q1lk0+a9N5bbyV07oOllG4lzkFn3L0StcDJGVbPsGqqCpw4ctlf/9y/tPABTX9SNv/6Tn9hoHC0I1GhAMlAEk+RXthYSACdXEaXdtGqt7Vi1EjXDfVY0a7YtAaiRPcIGSV2JaelQyaDTXPl2iquThvJeH1ypPvQTz4uvxeOZALsCFAUiHcHhwE7UWQ+99671KC6y8QsW2rJHHrIeqEG3AqC1UW4tbvJY8EiqqxYV27JDh+xtaFhbddL6kP+v/exnDjoVIFF3AXsGG95+B2/Gb1vBgEE2/7FHbM7SxVbAjkwrCykNDmp7AuYEDLXUN7jDkfXvvWcnqRM5AJn50MM2f8E81Htz7HTVKXvhj3+wM4cOoOKcZ4u+8z0HJwcWF0M31DUpr+jmNhVAC1qw93cAxwtbyMPejWus77gxtuwHP6BNPuxedyVlp3zIdqGI7upRXIlqsS/iMhzE7S1HdONQGD+4dzJzI7V4pS0bjXKokQWoKjuSL/wBYA57g8iP2TQkaF1iTsAcaSsiH35JUB6Ku8FDraikr6VPeP/3v4Nncu2hH/0nux9JuxtILrYT3uuTRFWH2XydBzB3BPXT7RuwD4ituR6A52MWLwKYW2oF1J3avH6wste98h77SZXBwVneiBdOlgHIb95ia//wrN3Iy7JSvBQv+s637J4hxeyi5TmvKb9aCMgYu5w/XMD5w/I/PY+tuQrrjRdq2ZjrN6TIPTu1kFBCMiX1uRwqn6jq4LP6dRpElyrrXSI9lasNEwFz+rXCw4wODtaFsZS+JWkj4hvxgvMfZ3+vu1BhPBFvJwfPdK12mHoW33Wdv/QUYJSz8LvhqtDim4YbrXYcUK7sdJVdagFcY7zM6wGgBjAnz77yKq8+TPM0sYzGMfGQ7AG3oLZ6Bbu4cmyVw/jfExX/QXmFNrm4FEm5npbHGONgnL73bwP/6Toe6dfx2V/7Obatz7vsETAQfbuAOfj5YyrAbSvD7DJ9oaDaeNamR1hsax7KjSLhvx5rnhc4XefwTOeQjr/0p/osHPFZR9j45iPP6ROJjwzY9fJOUyDUGH87qi5cqpKpWLWrWL0Kol5TZ38qduG91BMF4Dof6DP9cwaKX3Y8Czx1J0vhCd3JCL/0cal+whFqLNRaUsXpnURa3Ybw0JL/XvXUsupK1PWNzxij6luLQ539+/BN8jomwrcIoyTJKw4discl5vSAbz/uiCGcnz4u8BfqvRfQ+1b1r10Sc1ROqG/vNj51VWmccObu+ONxRUZNdS7OOSxWk85KwR2w4F4ejwyJHtlwkGqZFtqXUI94BdWyCnTzr2M/ZzLgzrT77rPRY8cgHQCIojDnLiAd8wcP097UYCMWL8Gm1VJsQ01DAisB5khXE8QCgDQtiBsEzKHK+u6zv7c2PDjKc+RiVNf6ojZ2DrXH/dt32nvP/hEwrN2GYffrO3/Hohcwp42G0oTqkEAuAXMCoiQ5cwXps+cIfxzVzVYkXiY+9KjNAsAZM2kC0lXQVgXlp/Z5HRCsrvKUrXruOdQDq1ArRF2RCW4zkkzdsa1XOmmSTbl3jo3HgUGQgMm0a4By2ulWD+DScoqMI9aayOqNkYuYlt57KJ55/UBf5VvSehmoMtYiyfY2kmvlSEl1J09fA5hr5oMTJ8utcv9eu1Z/mepA7REQSjZGshGzlRrpUGztjQakmjJlqkvRqTNpAUDSQlDuwNvZNd+KeuwmgJ76s2dsMjb87lu2zEaMHEmYbnjQPGPLkXAs27mNum62+3/0Qxs3a6YNHFps6cCcyuRqgKSp/EtdViCMq/MKwBWPUEJ5yDwHHd/647NWffSI5eGFcyHg0ShAtwFIEIlPjPLVVlTZq7/7g1V+8AFAktkcVFNnojI6EuColXoWtXzBQbxNLDbOlJ+w13F6cOKDPW5/rNeIYfbgD75v81HzbMVYjhbCKrskBkVz5dd1rakjHWpX2gXTc70PfM4NeZf9xAwApCySPY3ttoM7d9vWl1+0G6hElOAR9yGcCJQAyOYjuaBvRdsMQLxWQLWDH+yzrQB5B6Hxtfo66z9ymD3+i1/YSFRsexf0sEYApjWoaK/44x+hb4vNxhHKvMcfs6FIdMl+kDImF92qi3a8BcsT6TakAnetXmWNSJ+WoDY9dckSWwoo3KOg0CU5/4x9hPId22kr9c7b85bSvgCO26iY65RFatJZtIMmQLnTFZX2HqDc0U2b7TLgc+/xw20ZdSz1c9mlk+3CVuysaYfI+57QASUcHegm+qUecOlUdAJ3PPbpjuhO2gKD9MumQrKov0sAcy8CzJ3csxO6A8w9/RRAuVRZAcqRUtQhkEv/HJhrwfYbNubW0h5WvfC85WKo/IHvfR+py0etSWUUYzCRcvtMpCOJSqnFHzt0BEccG2zPa69aQUmxjQaYW4S0YY++vZ03xB/8d75S/+PtkLOYQoC+ft3Izza8Fq9++20rX7na+iEtN/6hJTYDxyW9BvSDTtjFJH3nH/IsiVepWl+ooh396YUuYE70/Codagfie04aTwTM+bhCOwq9WNJfEEC8pz96H3nPuZ5n/i55rxvdK06PV3/u9JFKkIi74r+Vup8DfdTzyWmIzvIAjiw/wFy7HQOYO3K60mqbrrIxJ2Cuh6uyyrNv7Ce9C4RLNHG+oT6cMbUe9Rw5YAp2gXNtKN5XR/Aryu3pphJ8Y43wDuwl5YvFjCyg+/jsVqL8dT5JL/vnWcIuYK6D02I/+FH0FzDn82o6T30p3ndgjrP6UyYfqa7Mwykyzf/040jVs9+GZ3qaTHkU2MOlh009+KgLT/yjAnS9u1sUCFXZUW+eDrfiJ29fVG6q3nmpeZv4wcEazlq/OXincAkjOCvFr+LH3GvcvvNHSO3Ox/vljbGjNnUVfvEqtmUvXVrdhtJCS5HT65dLzvFwSTjVL/8dwGfdkNbweax/SXhOKWBOEXCvNwHg00uBejHmDz/HICHuDw/3xXujnLOG46RfFzAncnhtqio//SGmESOl/fFrxamfFqiR2fQgTvL8Mb2PFtVaILNUd8CiHfW6RqTJTqOW9+bzzyEd8v+z9x7+dRxXvucBQESCAEiCYM45ihSVJUoklW05ju3nyZ7xfObtvtn/5u1+Nszumze7M89jS46SbVmJYlKkxJwTSIIkSDADRA77/Z3qurdxATCJYBBvA7dDdXWFU6eqq359Qr1VoUb30p//pU1F6qUKiRQtWKVSdhkJrH/7b//CIhxD70wy5wICPcICeQHATLvgE9JVYwvc0GK6hPyuog74AYvpPyEJ1I3XxLXYmFuN44Ox6P2famiwHZ9usQ9/8Trqo30247GV9mMk5kZjx0kTWanCCeCSSptAPnk4bcYL5Ot4SDz08cfWgdTegrUv2KNrV9siDMEL7OplVR6MLUuFFc+Vh1AP/K//FfXAI666K0+wsssmHfPx8xfYw6ues6Wowtax2O8TmAHBugUMaIZLvjqIrtrCkUGcembCFI3NYyuQn8ATN5IviRt5dcROxpu//KWdwGlCN0YW5ba7DVWWS1cuugpL3YTxNrZ2LOLXnXbqzGlrBEC6jBRdDZJSMx5eaauef9EmThN4NBLNOoGVlBFnAfXUTcZxD2/bRlvNt0eRXFtGfBmTFmgn+xy//93vXcKxt6PV1vzkJ7YICaEJpHWlCyVRKudqABReDjJiHfxlRh2cYUV/6iTpw5OAnHvxdPTJr36FU4oOG79wvr2IuvP4aVNQQ6wA7KKdBLY1XbSNb7/rKrTn6o/a+FmzbPnTz9iiRx+x0WNGWxngo5yTnKJ8J2mXY3v2AYJtt8unAE+RaiwdN9q+8Xd/j2OR71pPCZJmvDbdOy1gmgZmAWhhENeJSB4ndaGNnOcps0uG0tYyClrC9SUk847s3WPrfv5z7No1YmtgnK2AtvMWLrDJ0Loc46DtGAq9CGh24vARHD/sssO7dtoVnuvuvGpjZ0yxV3E2Mgc12FoAJQE3H/7xT/b2z/6HXdh3yKbidGEhzkPmLF3kDiKqMDzbCWDXhGRow+F67Cnivh3PsGcOHwQ07sJW2nJb+uyz9sLLL9pojHY3Y8h78+ZNOFZZZ8dR1R43f76tQCJxKXSrAcguQSqiF7pJUu/0kaNWv/eA7d+1B/AZQ6dIL1ZMqLUX/zZIn8FYSKzh/KC7y9WhnUPhW6eQDtr0kuMQLz1M1x6P28mNMNGhb8ETgbeRQqMRRqCWeqnxjL3x7z+z41sFzHUFYO4p7APOmQMwhzoBm3hLYJlU20GW8ezajCrr7+zDX71hFbTBC9iJfAbJRdnwkjqL7C66hKiAVSTsJG14aN8B2wKvb/3lrwDmJtmcNc/ZGsC8KugifpCknd7V9Dgff1yKiZpJXb+8CPtNRJJjks2oTG8CmLuC567FjF3LX3neJgEiyiGLOrsDfCSkMWcoYO7P/v6nNgaJOdmYk62xXPp5pYdxpzajtj6W550/DCOhY8NCcEbDzE/B+vm7mBO983wjXtJ1vF+JH0NbJbd1kQqI6cTHFSt9Hp66+X0qi9uSXm4J8unnUqT/tegj4NZBORpUknPMOqwFcOHAmQaAuWN2FpuvkpgfiRmNUfyiGqt4Su8vH3sZb/XxtAN1vguY/dD7bBSqe5Pq6mwGRpqnVdZYDR9pJJmvPJ0f/YSL1BZ5apBbqVhfz9N03e9kDeM7VHnqXO8VH0M4pr2yfr2dP4T5hZhTZ+K/3C22jy9wnUZaGLOOIKL3BQZR5Eh9YHU6cs9n38T1xbYfU6l6Jr4jkJgxg1TumaDUY0Oeekcc8m7+xjBTwEGX3DzUvGpE54VwUyOg3rfOaf7i7Q/MpeNGiNejk07CUZ5kSG3ofeSsoWOk73gh0wH584QCYTQQNbPjQr+2TrVtmmgeJzZCaHDv2ernWgV6I7LO8DFCaagJ2HzNqBPnjXCd3CJIZUjKwbMxeUUfaovPRt4ZKt69F66S54G5wCgJd4SDs8wtt1e/94RzEDv+ndwco+RIZDS94KR/ragCpApQi9CisxwJEk30Wi5espNI2+zfsdO2YC9LYM30pcvsW0ixjAJEkWMAGSkWKHYF6Zh//ed/dtCgGOmoBbKPhW2xuYsWus2y2OAy1q7FtI6SmFv3u9/ZOxi172lrtbU/RGIOu3BjxoyxBqSvtn/2uW1ksd3Fyn3mY4/Yj5GYq8ElejtT2w7AP4FQWmS7OmsBZQAkeR0JpUNICXUCzM0DmHt87RpbtHwZaSCyjB6tVDJlm01STBeOnbR3UFtrQqKsB4kjGeBvb77iHl77UFucCaAnwGg6xt9rJtZZafUo60FVJCLy6cEi0xmhm/9FukNb0dcnxkSSRGIE5mS77+zxE/a7X/zSTuI0oQ2jlCMqKlwCTh4e52AXbOHixTZ1yhRrQ5JvH8DRdlRTj332qUvSVU/Ea+S3voOnzRU2ecrUYLuML+cybrpx3ToH5eQE4IXv/8iWEmcKceSNsgPptiYBc7/9ve37aBP1vmLP47hgKWq/E2dOs4uo0miwKgRAVeHFIVpgZr44qIYC5QgXMKcFwvYvvrRPUDc99snHNhqwbR4g1LNrV/tXf5eEQzxOaoeFrZ12aMdu2/7RZjy3fmytqAxPWbzE5q981Gby3Cik07qwLbYLetQDyp0DWBqBLYir55vcGUDJmCr7BuCHHIt0k2YnC5o0MKfyxvaB7JTUh2Svh9pI9YATCCUijj5k503Si50tVwE9j7uUm2wUtiOpWLdgsc1ZtMhmUa4awLbL9Ifj9Udt19Zt1orargzISg1JXm1rxo+1V/7LfyH+QqsbM84qacfPP/nU3n/r93Z0wyZs7BXZaOz3TcNJxMzZs20CfNwBEHwAO3UHdu60dtpMHkY7AP/akYCYjKTp0tWrbc3zz9vY0aMdmD16+LB9vu4D+wK7dZ04P5k4f5HNRnJy2owZ0A0baACz+0mvnt8F+GpEcQV2E1FxarmEW/hyewF15Wex8yaHEfI068AckobiY9FGKu1+wnl4ITkB2cWNdicilPPNj8kLUJKUBdBSdgdLCBvRJRuSjfar//EfdnzbFvpqpy3/wQ9seQLMtSbAnMAy2XsUMNcLUO3eZN/8LVJzv7JR4yfZ2h/9yJ556QXAYnwFk6GAOUlHurQjQG8pzx+AT77YsNG2vfFLqwQInrMWun3zVatGbVjG1WUxUZMz5aWvQC75SD9EZNBB/W76odRhP/vgA3hyM+rGdfb4q6/YyrXPWQk2/OR0QuBeGD8DzwdgrgdbiafszZTE3J/99B9cRVvem91RjMocyHVH9iEv+qsmEWoTqplXZR0G0qvDaEsaFzLHruPHON5n3snEy3kkPurJ+EUOo8T4ipA+Dw/k9/crBcQrAuckZdlBw0qav5mfgLm9J+vtLBLRcjpUWV2NR/kanLQyRjPwOi/Rr/XO6ubdoQ+JGrs6mq+iwlpoYzFRMXf6DJsEKDe2GOcRpK/xzrc0A6V4Md7WMYf90re+lueRJHe63nlgLlmBJIT3Kz/v3xI+j/ZG0pnGwDCb87GVa33Uk91Evdv1QVwfByOwEuOLcWM7BwaPeYS5jO6nOT8TN9y49j4zuF87Wv7u8FAgvf7KzcH7WNKYiueqiEmkwGqRDwiEd5xfCIp9U3c11/XwyEGpR3Lzi1EGhF8jIJl1XyPGg3orGR/osMnyQmcZYsQ2ygRwortRYEZr1wxvOA8kDakHGCMyQcyP/VzhZOR5sVOYp5Hk6WlnHlLka28xTXFVPL/2E/fKXa95WCNzetck5i5evDgsdNOES19ENelq5XgStco/bfvECivLbSxgSi+SPt7mTodQBA0CGgbClc5vfhvwnnCOyjLaAGCOzLRYFVghb6T6lQHGaJLX19EFOHbctm5EEmX9OmtGskc2ylYCFCxb+TBej/B4iCqe9PQlsXb5zFn7N4z6yyNrGQv/ld/5HkDPY3hGmmktPXwXFpBDPmps9wzGzLT53EVbj12o9375c+sGGHke1cc13/muVSMh1ACwsP2zLbYJ+2s9SKsJmPshEj81SP6wRAeco1ORphb08kQpD6fNSDO9jprk4Y83Ww9SQote/SaqaM/agiWLrZeFsmyRFfKTswDZ4yqgvpKyu4oNvbYW7MyhUtiI99n6I0fsxKFD1gE4I7XDKah4Ln/6SVuwdEkA+KBrtqWcyFBRR3Vo7oieHONizWOI1tyQlJ9sZAi86MZgszzJ/uGXv0FibpvbtauZPdMWoSa89NGVeN+cbFVMzkdCa6myNF+64iqXG99/344AzjXjqGIG3jtX0SYrkYYrArRoRNpLQMXHH7xnbYByE6ZNt28hhTgVlcwywCK3DwfwdQGpoN+88Wvb/RHAHKrHL/30H2wJaoYTZk23FmzMyWi+q6tSePFVALQ4p/xa9KuSont3W7sdPXQYFcx1tgM+6QXcffjVb9hjALOTAUnEc+1Ss4En3DkBUk7t1OPAvr2284sv7CiqmVdptyL4oaKWRQiSX6TuUne1U6YhDTgDfqqwI4BlR/fscFtpz33v+zgxWOPe66JNp6LiRGKOZ32A9gGWNqCcag+1mP41iXNAmqPs86g+xcQtQHqqDYnF/YBkuwGEj27f6s4sZN9Ntv0EQHdDty4AXZjf5qPqOmXyZPto00ZrOowXYKSqXv2n/8VmL1pstUhqyctTM848jh86ahvefdcadm6nDyGJNw6At3IUi60i+OuKdaJCXcz1MlSN5Sn0DEDkwQ3rbRbOHx5F6msFfF/FggsxQoDBy3bowH7bjVrvQfil7dIl+IL+hB0i2YpTPQXsVddNdLpNnTgZpy27kTj8wkZNws4d3k2fevEFK0QitFMALRJ2cuyhl5BIFF9TopE2B2K5Ea4CHXUefx5JdBSvKx70dN4GaCsErBVo9euf/yJIzCXA3LKnnrLps2bhsEIGeCk7i85RFUikEb8d6VyB6xtQb9/4219azcw59hwOK2TjUVKzPd6nJLlGf/dxh9ELcG0vHw4+/2Cd7UZ1uGbObFv4whp7hmeq8eTaS38XeCselmSePjhoHHIHFbR5OyrHZ06edM+/x6CT+vy8Z9fY0qefsjmos2vMhkjOxy55x/PxY0ABEn4OzP2HVFnr3ebjn/3DT200Y3w3EqJdNE54wQdK3Yl90nJeTwfnGGtacLIz4kqbrV7ykE1mbK2U/UbC+c6S25j9ixgS6x+WvwoUgIeSjtGPIgr2zpQc0yT0e0lsD0/fTML9kApPP5OOkj+/fymgNtXcQL92LvgMZle42o8q695T9Xbm6mUbUV5qlUhVay7kEnPE0/ilOVoHDh4uIiXXyVGOfuZPnW5jKwHx+IA1diRSdnx4rGA+JOuhetflbmmeSrFabrSv/XWkw52mQXphqXPNkZwfOPaXmKu3fY31VlhVjgRlmf8KeF+qVeN8M5Q97JVWrFO/xtNLSFtyCBc3sh80tRt58Jpxgp1ECpMpVjiPddLDIecw59D8IobpEV36+5w5gNS0g31c6OiRRAPFT+91lWSmOL6FtMN59l7IKYlyvUNSrutFy98fHgrIpE//LbSj9y/6QuAiuErze26l+Usxva21S5o/POGhmZshrH8uX/0qyeOrJ/S1SiFpBq9TOI8rEYLSN4cin8cJ7R3jZx7zhlSf58T/Bybi/KEHArP04xcVSs96ekM8rzjawsgTjgNzCXHu3X2i2UgB9aZpYZ5RgGmkFdNn23Qk8ceVVbqGWex5vr4RvdhiXTM099Cb3xV8rYG5SB2IJoLp5R8XibolSbmwUOVcbzS+whpSc65SxlF2S75EJXQvUk2ncfhQNXGKPQYA9AjqdXUTxzvg0QZIEYC5AruCEwOprQnQEIiw5BW8iwJmzcB2WBtqp2Jqpa3JolQfRwDEXKbR17/5O3v/jZ8z4eyyF//8L2ztt78HEDHKTp08bTu//BJV1jdcJWz6I8vtB3/z10EahcW2wLkegACpIkpyq4zJyhXS+8V//1erB7QqYOG/AkBKEjqzsPslNTiXpGI1Kjq4Ch0DuxhM3h97AJgkQXUVcEaG/Q8idbQf6bSG/XuQdJppK1992Vag3laJpKBWtBnQCvqGv8Cq6pSR3mHCxRX/AgSCpE5c2FP0ljY7jUTiW//xBsAcBvJRYV32zW8C0jyBzbX5VoSUk/ISKCaPoKKfQMQd2ELb8u47dgAPoqPnzrUXvv1dW0W7yMvkTkCbT7G3dQyAdNT4iUhUPWSPSt0RiS+ppjqAovZFdW/dO+/bfjlqaGuxJ773PZuFvbLa6VMwAljm6rzFGJ1W+wq8cjVFaKUyuFQUacnodNOpU0jKrbN9OK+4dLrBpqxYaSvXrLHFAIsCAiWt5F5wAVdlA0fPCkw5h6OOBtRyT+Bs42z9cTxinkGCrM1GkKfcmNcBYM2aO89qJ0yyBgCenR9tBJzbahMASVdh++2xZ562DtrfAUTaU6Ck7I9p5NSL2NsAmusYNs74F+/r51Nb1YvA2A8k+XcFKU6pqh47cABpvRPWcvmytVMuSfGVAM6NxnbPjHlzbfrMWa5S+RY26U7DI3IS8Y3/6X+2WUjE1YzGtpnSJb92wNfdgH31u3fbqaOHSQvlW9Qwtelr75ixtTZl+nQH+hqhgZx17PnD2zYHHnjsO6/ZUiTnKgEG9aWnE7D8Ev3yzOnTdoQ+qXKeO3nCpSeKoFsZtu2kIjtrzlybOn2m94svNm2wzzdvsFpUXx999VV7DClWOTHpUnpIzcl+oF5EzqtMNJ2XEzoJ6fNJMGX1FxrHSL/A805SaqpJMQ+RpuxSCmxz+2u0228cmNtCu6PKisTcMjljAayXow+lJb6uRE1Yfa+zudVaUdkVMLfu16/jNGO8PfdDJOZwWGEAu6JaN20mb7L6gOC24WgzSTB++sH7tvcPf7RaVKgXAz4+uWa1VdWOdmDOXZmI76hPISCdvOrKUXqvpDcBOrdt+cL2IMHZjvH0GkC1p/Ek7FKySDVK8s3tI6rPq4uTjqRe5YladTzPOPWmA3M4f8AZy5/9A+r2SLvquTwwB72+rhvs7gw8SP1gs3BLcXI3MX3uNlhYKs5gyaRu50/vMwqoPeNPCv1XYZjLhOyXxNwpgLk2JOawc1vJPGgU47kkin3sYuxrR6paH5DakZIrZMysKR1pD82ZZ3U4zKrkg5FAd//wSbqaWA/FWsp/qHvceiC22K/uNB3iu1RE1nmYJ4Y5+oMMzA3kyOz8LfYXzdfinERzeJ+/0C/CX3aeoh6WfTqwc2zveKVxOmxZDsgExVvXOuaBuWtRZ5jvMQNl3tp/45p/AbWxjylGAOZC3PRePJLdxFmRYwhPbmXDwpPZ+F/lLEk8zz9ZIkKSNIW1vlBIOkyRE8plnov3Y3imrf0Gu9jJPQI7HZOw8EzYh3TYw1OBrXL5SzyVZMtJ+tlMYeJtPwbOiY/kxrl3r/PAXH8m8RaMDX5rzdavn0fGFG+SnF7+DsxxofWlA3M6h8kCaCTQBKkUVCMuAU4cP3LYNv7hLWviKFWKhc+tsUdXrbIlSI0VIJ3TwcK6HWkfvRhlU+pq0wX3ULnns89QUT2HJNfT9ogAmhXYd3ORvKDmhiAJi2pU11B1u4gU3odIxH3wi5/hhazEXv7zv7Y13/6OVQFENDU12d5tO+ztf/93a+1otynYqvvhT/7W6vC6qEV6Bzpa3QBMAvRQbENqrwA7d2ft5//tX+wENq1KAGqeRAJv6WOP2tSZ0wFxgpg762oHEZzcnLsoPIECnJSG1GJlg+3E0WO26e0/2kd/eBPC9QCYvWYPfwPj/XNnAR4Vg2EihUPd1Ykd5HGqBhprNBG9Xf2NPBzYcjqTD0dJq2jS3IPDCzlM+C128Y4DkFWQ7mv/9E82FzBmLLb02nA20dkJrACQU4bkViXqKUWcn+IZAZqb3nrTylFzfPUH2OZ74QW7gFdZSdNtoN06kaaaBEg0aynOF7A7I89uDlgJWJCk0OVm2w19Gw4fBvDBWcdjj1kdaqzVONeowkbWuMmTUCkeGwAv4stZgoAm97xJBaX+2YiDjn3Yf/sAdeNLjSetCm+xj+FBcx7qlROQJuumjURnSbBJos35TjTneQFdV1lkXKLM55C2bAKUuoiTkVLKWTt6jM2YMtWmTprqXmbX4RF467r3HCR1b78ATCuxh9eOxFcA5pis0d76gqa8lKd6ksDTIN5MgLbY6ArnUu0mUE4jrlhU/UeSjLKreAHVUtXvAhJcl5F8a2ExJMm1qdRrCeqtsul2+lSDvfH//qudqT+MJOcEV7GdPn+uL6a6SVOgk1QeBaadBsA8hWTWOVRgJSUn0Xqpn06jntP5jQU43bp1q617+092EMB0wXPPQMtv2YLlS/HMB3BFev5lGL6T1N4F1J6PHD1qx/m1InlXwhf1anhhomwMTZ5mYyjriSP19tE7b9sXH2+0yYCzK+CR5Y8/ZhVVla6GLq+scXLTJ+AJouhVKNro52+ihC4insgX7ymW2lNhTmvtxSMAc2WMD4jjuWMEOY1RfyxEYm7FD3+IzcYnbNrMGdYKKKi05LK8DFoWqC9j07IHKc8N8PV7r/8CydYiWy1VVoDYEsosYL2TPDppdwFz5QBscqCyfcuXtvn99+zQB+/ZhMWLbdlLL+K19xkbORqvrGSiH3hcAJc5kUp1MTbwBOR/tHGDffzeu0ifnrTaGbNtDvR5bs1aB9mkfuwfFRJpW0ncyi6d1Gg1ThR0IDEHMCfnJC4xh1fWrMRcHpijeb++W+wMg9VQ925mU0e4znazSV4nufztu0iBdFvyORD7cgBzjEv7z560faeDjTl5u9cHGf38zZnMdVr5MCfV1RLGsFHMB8bjdXUekuVjcBAk7/QyE1tOAnMAAEAASURBVCIJcP/YlNTxBtjrLlLj7mV9rS48nKWKoIHyyANzYc6hyUCYeWQpH/uJ1i3aIt00T1OQO2CD1z2e5kf6hZi+18wkveVeET3Zsj0kExRvXevYb8F1rYj5e7eVAr6QhVuyzeYt7fxDmANzCuFfURysSQoQ5ru5nBY4RdwT2j8860kkbRxWDLezFsop5HY7U72f08o2p9onXGXDQs0ixTJjQaxwwgzevpz7c9plHlBELvw6HMOtsI/5iamUlF8naXoWmbFFyaT4xG/234UUlVXUQep//96+ygNzgUmSt0M4xAa/taYb8J5IuNNVzUhS41lQSQxSWAFoYchhkVoigA0QQV6+vkBq56MPP7SmvbsAN3qtbvYcew27clNmzvRFvcCQ2DHEhCWIsrRduIz65Ie2jYXuiUP7bSLeTJ9EFe8RVMI6VDAqKLXZEQAA+qIrw+1NAB/r33gDg/u/sLK6Mfbyj//S1gKAVaDa1gzYcHTfAXv9//o/ATYu2GSM6n//735ik2bPspJRI121zb2VAsx5PQAELiJJ9B///H+jNrjDRhJn9V/9jXtVrZs80dX2WJu7nbn4hVJliuBMAJxQcYNIhdCjHdXW9e+9jw2836AK1mBzkEhb+tJLNn/pYitFEkx0kYSWSOxkpor+AkB9TQCR7PE5OOeDQQL8AP6prFKl07EQVbgLeGV9E9tYRz77BLCy017+z/9ocwEhxyCtIzpL9VTpC1gbxRdxqQo2nmiwdTjNkB2u8jFj7Js/wkvpc6tt//4D9gkA1lZAim7sn0nFsRxVWKk5yumFNtnW6+sE1AQYa73U7PFAj6wCtb+y6kobOWGcTUZ9csEjj9hcJA2LAQv7+DLfR1kFWpVhLF9AaAdqv5+sX28fAqKcEb2nTLJpeLFdDXg5nvOSijL3nOs8By2kciB1XJ9MUXeBjbKJJpCpi/R79JNqperKoqOcMleWjXTj1n/EIP/uzRtRBT1tCwHlHn76aZuHcw6psbrKMASSeqOkC/1HGq7CylHAnI+vIqI28vYfB0X3MkFheTV1YIlzb1XoLonAbsonez4CEku9TBVWLUcQgMXH6+vtF//y/wDeNdq4GTPs1b/4S1fdrqgaJWYIWcFjXdSzh/qqzl0ArT38lNcIgKcxSDpUw+9abG3YsNE+QLW7EQcqD70IuPS9b9l0JMDK4TfxqiREQzlVVsqHkw2VS+mKviW0TRl8IgC3C+cmX37yGSqe79mRfbts1tq1tvSZZ1wduwyJOdFHvBXBNb1CxK+Zr9AiE3GUr/+41iYyxp8/q9uEeX9QObgolzoygFnTiZP2m9dfB5iTxJyAuf8EMPe4TZ0x3VpRpdXLT8bJi/gJLJYkZRG/De/8yT7A+YNUdVehtvz0d76NCvBY71OS9JNNwVJKXkZfKAYc+5yPARvefccaPv/MpmJPcjkA5MOo0ZcBfIpHulUvyijVa5VPjl8uA7zu2b4Dm3KfuJRvMW36BCrYT7/8ko2fNJHIeLdF0lfORdSXXWrOgTnUn6G9PkYIfDyPHT0H5nCMI4m5H/wUO5g8n5eYg+Bf501Mr44wnNudyGM4y59Pe1AKqFl9LsJRqqxXeM9fwNzH4fOn7fCZU9Ykr6y8d0cKbMOUhsYb2ZNr5WOapLoFvI1CbXUyqiVTkLiuq6iycsYrvZ8jKOfAnDJKNn8Hxov88a5SIM6dVQidx/mojg+ExJy/+bODZ1gEazglLBs8oI00FQmzjTgHUXx+4YbTMrB8ZPx4DEnlXiWPcTObaf844bkh9wMWXEPGzN+4nRTQ3It2j2NaaDOFhZb0dg07gkLbhr0eU0i86l+oCMxl+qcnTGgS/aZ4o3/SQ1zd/hSHyOieD+7fItk26h8ee38YN9OVCoBraF8P93ZOYmTIzImfJziLXwpyTTjCM2Pn/4RFBlMy8FM2mXieCUkyCocYmgfm+pHlhi++3qqskQwJZ8eJoIAITdokxaVFqtuEYtnax4K4DccHRw4esW2oSO7f8gnM2GvTFi+1JY8+aksfWYlTgipXDZNqoqRZ3L4DcUqZUXQDLOz67Ev7DOmT3Z99bDWzZtuq73/Xnlq71j0UuvQSzF2C/RMBgL04WjiBtM/GX/3aPv3dWzZ6wWx74Qc/sjUvvuRgUDeG4BuO1tu//W//K0DUMavDVtr3fvL3NmPhPCRhagIwh8SPAB2pWQqIOoNNvF8CzJ1NJJhe+uu/cTtRo7H5dRV1RC2wC5Gs6gPEiJvIoyv9pJ7moBkggQzRf/jB+/Y+ANilwwdsOip4S1962ZauXOHAnECSwoQGEdzRm0KDuCR7eujGUVpHndolsiLNRXd+snPVjGfa9/74tu3HLl7buTO27Buv2Qpsvc1BjVMqewJd9KKQWqnAKk3STwHMbXCJud9ZaVWNvfxnP7SnVq+1BgBE2RSTGqbmDAUACZLqi5vCdCWJQE3yzxw/5U4Vurs7rAawZMy0yTYGicSaOTNtNpJHM1Cx1PPiDakaliKhJFtsreicy8nA9g3rXdW5BFBj3lNP2bJVeB1FdbkU73DdZNYBsCGaiE9UDEmPiWY+wlE3AUNpsXMNd6LLiL5C7BwSj3ybAC7fgkeObfuC53ps9V/8lc1FanMskmH+wlTCgKGOsjn9AdmgM5QL3ysI498H27AjA2068Ivtr6BInwzFAHspZPhRf4FIAiZ1bGw8jYrqdnsHb8Vq6yk4CvnmD39gtRMnoYZEO1H+ngRY7U7aMOQVXgI6B82yqtJyl/y6iGrvB0jLrcPxQTug9GOvfdMex4HBGEBOpSceUr3ECyqneM4l6ASuQQPRVnYTBc6B2Nm5U2fsXdLb+ykONi6dt6Xf+44tfORRVyNFBNZp4iA91St0+gVbcwLoyCJQj6PaRNeiYfL6Iv9QhkgnlamPcog3NbZUIN0pYO5MQ4MDc8e/RGKup8MeBphbgkTa1JkzrA1gLm28WRKkArvUJz585117F0m7i4cP21M4q3iKcaQWOhRQ7i4tUjXmwCNlsGUhvPzR5o9sPWDeBfrpXCQ/V6xdi8OXpYDD8CFlU/uIPhrrupHIU/85sne/7dvyOVJ9DeTch/ORR2w50nzirRJUud2ph/ofY0b4EY3+JH5QHxCA7zbmsNX42//AY3ViY+6HqLJKHbYLsDuvygrNvq4bfcIHj+Gsn/LQ5oNFOM3v738KqFkZivwnYK6Zs4sOzDU6MHcWZ0z6mDaSDzIVSEJrzOnCJEIzmgz6uFBdPtIm8/6bPKbOJvD+l/qqS/vDJxqTw8fXcB6p5SyU56NIDj/eiS7cL8PkIrPw51rneWAOxtR/dqLWj2yBbcM8JH0jzc4+VGoe4hHCXu/19JZ7RfRky6aUCYq3rnXURCy/3XkKaOIPrzi7kHtoBYWliqLG5V9BaYDFrwlNR9VTSkPc42llGcPnvh6a+4Ae+sqb5/aVU/n6JaD2CQTPJfv1KObxE8aIaUT6xNHB45BQvNb9bD6c6T81FoX4IWffJ+NM9pmYQ+AjXSntkH7quWy0e/gsLzEXWjEZBMIhzSrXb7sbeS+IecQa4acFqjwTAtiwkNZCVUCUO284f9GOHT7iknLuIfTyeRu/cKE9/vwLtuKJx0zgi9TdHHAitSK+0MpwvIAjSa8UoIZWv++AfYR002fv/gkPptX23A9+aM/g3bAYVTxfvFPJUlQ0BL60oZKxa9t2+xy7UPuxYzcVm1qrX3vNnn7qaQeU0LVjYX/K/vX/+N/tBGBTFUaQX/urv8UW1zIbM2k8zjUk8UNpmLT2AtBdZtJ6bP9++/1//xeM7l+wCdjUegUJphlz51oFhvkvYCtM3kEFDJVh/N49nbFYd4pTLk1mSwCeVDZJ7sjm3IfrPnCvsRcP7nNgbjHA3HJUKCXB5NJdSKGNYLEu4EkLf2HvYZKFlBTggeyrCZzT5pNl0tbEWXRXPpJS7LjSap9iy28nINcpJJvqUD995uVX7IknngKIIQ2Bj9RRX9AFCHXx5fw4ttk+Qs3283fftgrUTVfjaOOpNWtdtfgCapeXAXZGMLHXFunuNmpo+1KkmVS3y43nsGe2xY7jhKEdRxnTkWSag7TR5HmzrWAkRqRRyxuNaqTydtuDtIf45jIqlMcOHbbN6zbY2f17UT9stak4BXli7RpbgRfZQkAkAXLt3UiaiSLQR55wC2DWYmgvKTG9USX5FUHHCB4K8JWaq4Db8qIS2vGy1R84ZH/4RQA+xoyfYN/9x3/EscE0k6qP0kEE0/nQAUSlqxalPXtoawE4AkkhNXGTHyfeJMTh3+PqRFJbakuBRZJm83rzfAHgnFS89RN/qGyS7toNT36yaaPtQUJx5JTJNu+ZVfYKEqJVNTXe7jCa5y/PxapXN9KAqm8x7VLMQspBbdKuEA8hjXgK1en1b72F+vTvrWrCRHvy29jRe3GNjahG+o5+pzqpIiq7eEi1kCdlAWLiZalkq3ylAHOyQ3js0CG8/b5ujQf3WzkODJ76i7+w2UuWYLNvvHWKzsR3dUzqIi/F4hctFrvFtyqvaMe1MlS4k0/0VhmSsgiEC+cEUg4H5pg0jcQ+Ui/AXOPJBvv162+4mrbsJz4KMLcY1fIpUi2nffTyEz8obUlJyruzeHwjNgvfwbbkKST+lmEv7vHvfdtmIjlYjPRIaFvagnxKoFvHxWbbuHG9bXrvHfrSJVv67Cp79PnnbdrsWVYML6od9VFAC9peAMsLqE3v3LrddiChenbffqREUU9GNXkVHwQmAUpX8PEhAOrwkAoGT+goGujcP2ioP2pyyDghz7O/ETCHxNxo7CL+4KcAczjaECiXB+ZEtK/pps4h/hjOzTtgksFw5zWc9cin3Y8CalaNJ3xXcIm5Fq4uY7bi8PlGO4RKfWPzRZ+nlOujjd5zjKUyMSJJ35G8FyVlPWvqNOzK1Vh1STnzLz7ywR8+HnOueWEE55xtIu/EI3Hym7/KnAx3miz+Lk8aQOd5YI4W0L/mFwMGVc2q2eBpHw4190i2CLj0o2cmIpF8gaT4nkJ4Pj7MVTapcF+3sqlnIg59ciMLsKGfzt+5VQpo7gWvOLuQRmizpA11y9ubUBo4me1nclIshXnsmIDSIG7484tMfKXt/JUkn7lxW05uittuS473RyKZFgrtpEKn2irVcftXJx1Hj2Sf7h/Pr2L/j9yTamDSSSfld8ROei47aIQ00xFDSOAjj8cTPKxT8db9seWBudDSSUOHw4033428EyKriSV84GGxPcLBBUnNsShmdiiV0nbsZ+1CreuLjz6yo0if9LFQn7Boga36xis2C4BrrLwbSnIEJtQkwgGPDBNiwB2ooIh0LuDRdNOf3rXNv3/TOgEHnnjtW/YUzgwmAlzoC7AWyCUlI3C20G1nT5+yP775ltVv2YI31GZbihrZI9ilW4jKqqjgcUhPqnBHUVHrQ9Llide+jdH/VTaHRXoHgJXGZ6lGSo2uHum6HUjAfPz6z3FeUG7TAYte+d533daZelnDqZN2HltfPTw3A+k7eTuTPbNeATgssCV9VAb4JcBM0nJXAYU2fbjO7cxdOX3CZjzxpC0BJFgG+DQKuy/qbbJx5xNg0hdwIwClQEARQEkn4JTsU2nyrd7pUnWKwpXy0HOScOrGoH/9wUP2GUDbtvf+ZGVMuld/5/sABS8Ho/jEl2SZe8+kns3YP9u5a6dtQUJoP5KNdQ8ttacwVv80dJGdMZfSEgAEOCLgsgtbXlKjVHkFPpWh5ihvtJewx7cOyaQ9n34KSHoBqaTvO2gycdYM61ThiEu1HPgshmeKkVDqwoPll4B5n3z4oZ3ZtcfVZKcsXmSr174AEDLTRiHJKEDHnTKQtwMbJBLsvsF70KUEUEpHqXR2ApQIfCylrHL60Iz6sICZUmzlCAw8jU22fXjc3Ii9sV4AnKkPP2zfxeZYzdixgEdiXmcAL6cmaUFSDhAG22CigyQOJapHbTI/xVO9+PfA2C9gBOelcmiYAQ0Jkxc8lcVtoVGuEYQJ+PoYUG7z++/Y5YYTNgtJtOVr19ojSJaW4kG3HZq7pJZokJRT+YpXxRtAe5RBxIUXSL/1/AXbDgj1xfvvW/2uHTYN6cynXnnJliFd1lWCJJv+yFdlVr93MEzXpE1Kbu9OgBGZOXh6Fu+v+6HbO//fv1orwOnEJYtszV/8uU2eOQuD4iNdGkw8KHtwPajrSuKukD4vKM69vsHDUuEM9OUc/lFxBVr6WyYpixaCbqNSeasvcBRgVQrIJhBMHo5/jbSj7CcWAkw+/aP/ZIuQvJ00Y2omfQdlaUevB3v1ib2MRR+9+4Ftf+M3VgVYNu/55+zZF5+32to699orsHcEde3GWcSJg4dR3/7Avti8HqcW5fb0t79tTwFsyxmHwE+1K8zgKtGXms7ZbuginpdzlPIxY20eatvLabeZs2dbuVSQkagVsCybhfLGKslL1V9gZSFtJ+DYvbpylHq3bMzlgTl1pgdsg628Qw5ntZVH3HzAihf54/1MAYFy+mlu0EYbX2XcvQIwd8SBuQY7zftYm945Pn5hrqC8r8gW4H11dCUOIQDjqpmDVPCRTWNtCbwhiWP/YEN68ejvC8I1dvsWj8nlg36I3etOk8UX+gnxdZ4H5mgB/TujpluDFoqNJHpBq/Rlhn8Jj1v6LPkkG57JdIJszOxj2Tyzz8d41zjeyCLsGo/nb90iBXKAucA8pBXb2BuRHf+xZQNvpa4zd0IZfB3AA+FR3/vzOvPQmNAtFnnwx5J8Br/5AIeyRkvax8ke2zVNkWznDaE5ceLz6Uf6n9OqobFDXpn2DetDj5vbPDygoFTU9FUmecUKYzyxiax8wpOZKPfwSR6Y846fcEeGSXJ5YbAWvNH3QWQgH3RYxItBBHxIUksTOgMcaUGVct+OXbbr40/twOdfWDsSV3ORalmxehXqYA9ZNWqgRUieSPpJEnNSA5XUnMAwTS5VXi1WtciXR8WtAAxbPvjATrMAnjBnji1BAu5xbIKNQ9qpFLtjAlROY1tu/67dtvHtt5HcOmmV5PHEt75ti3AaMGnypAD8sehV2WTvbuemTdaAV8vxM2bZowBQK5DuqsUGmzthYMF/7sJ5244EzOcAaQ2oF9YtWmyLnl9rz65d45J2V1tRs5WK5549duX0aZtIOnORkpk+a7YDXgIFBJqM0GIcCaIWSfNR/h2oAR7Gy2khamkLnn3Olq5ebZOnT7MKJO7UBj3Ye7ly8ZKdBwhpOnzERo0djQOFmYCBk9wmVTuTbanECZiSAWdNmP3Hs8pT0ot90E1OD77AXtsW1IBbKN/MJQ/ZQ08+YyvwqFqN5GGhJug0pupxChXKzYBCBwErW7BtNmcNnnJXPYtTjqVWXIZtL/JQu0gyS0Bot8BBwAlJVglvk1OMAmzMyQvqO2/+3vZ9+ol1t7bYmr/8K0CTh23CtCnWyjOumpiUU2qJLdRzv/gEe15HcPig2ixCbXAlXm8FppazYOhDSsgBKQ1GnMsul9pbACGoYKAvZbkEn5zFGUIzgPDkKVOsduw4G4lUYyuSfA46AbrIKcTBnTttF2DrCdpgItJeS1bjgATbd2UAr7I1FuzHIcUJsHr+XJOdPlaP5OQ5qyHN8ZNRyx1X67zqEmDED6CYBt4wbHv/oI46SpqtHfqexBOrAL5K6jMeIKhcoCF9Rp5Gu1CbvERb7YcXd3/5OdKGewCDRtoSePKRp58BgJ7i/NRFWgK5LmAb8WxTk7U0NwMqjbOJ6gMAf0XQRe3UDSh3EQmukwcO2bZ16+3k4QMwRoGtQPJuMfWcOnsWUldE1AZN2nCWca7prF2BfrWASmPgjaoK3FdTNjkl6BbYiUr3biRRv8DW4xGcLtTNn2cLVj1ji5B6ramtdb7QS0LOTiSJcRXbkKePH7Mz2O8rxCbhFPhXkmO9gHO+YIA44uFCyuX2AVUWGEztpKL5Dx4WKCdJUEnhyYZkNxK0DQ0nAeZ+ZccoRxHA3bOAqkscmJvmzluUvsaUQgBxLUALGFMEW545jnfCLVttw89+Ds83W92c2fYkqr1zsSs4vm4C4D65kN6Vs+dtA9J1O7ETd7bhiI1fvMAee+Ele5jxoZg+KvARRkF6r8ulbw/v3edxG48ecUB4/lOoXz+60uYhGTwKUK6vGPAZ2gjQFe8KANTiWWCzAFa1qXghSk66qnVDAOaajuGVFfq6Kis25u4libmrp85a0ZU2W8O4MokPElJ9848yYvz443TApnv5bXAKiLeGmz7KI27DnVfMJ38cdgro/axxJQJzrYwrAuaOAswdPN1gZwDmZJdUH081Xyjjo1gt9lZXzF2Ak4dR7lRIH680l3MNCN5nGocVV2CE5iaaX8Suze2wxWNy+aAfYve602QJi7ZAfZ0PBcztOVVvexvrrbCqHHMWZf7zD2eZ1g1DUJ+ACtpdW6yTX8Sd3+fipis6aGox1Vs+ijMDd4Yk/JyyxWMmYbL3BW26GNArdwshA8P7USPTCeLTpJx5JEuYTFCMdq3jjS7ErpVG/t7NUyAHmGPEc97O8I83Ijv+1bKZcD9nl4SnM9Yj4jUdI2ME/kjCsiySfuwrnntuXzGNr+PjmVbz9ouAq9oxaY1MG2Vqn/Rvb+tM4LVO0m2dPJW0cUwjtk4IJn5gDhKN8QcyRXhGcXXGjyg6zZT7WkW6J+49oMDcGOwP9SEF4xznbadd5LMba76beR+IdSSV5EALmbqqoIA5qbzJ++qBI7Ye9dOjn3xurSwyK2dMsydewvA80mujxtYww0NiRgtTn+0x9RPgImYD4NGCugS1zhIkSwT6SM2y/uBh2wPAtwdj7K0AL7V85X0RVcvZSN5VjxltLa2ttm/vHtsOsHR0yxeu5joZqavHX37RpmDTrApVMlGhgFlrL+obsr312fqNbruupem8zV/xsD1M2Raj0lpG3q1I0h05csi2IDWzc/37gIhlthj125Uvv2QLkSaT04QLSMp9iZ2rves/tBMAPWU1Y2zZM8/ZQzgrGMlEtxh1kahSJ1t15wFL1iMVdmr/Puu82mLj5s9FRe5ZgKtHrBi7VSWAXqJjJ6olRw8etD2ffW77MLJfi9TNEuItQwJHHiG7oZWkxwQEufdXnlF7iFYBDIQNOO8G8Nmze5dt//gTO/HJp3jt7MSm1nT7xivftEmAPTLWL6k3SfzV1x+1zR/gRfJ8E+0z1h5BInEeoNykSZORahS+EXhIoELcFEITihF88m5IBMrpxJ+w7VcPwNSHI4M1f/VXthCJtLqpk6wFaSeBMMWFQbX3wtlzqCnvt/W//72dBpTqvtJiY+cvtKfXrLXHAeek2ivwS15SNcksQGKtEBpJ6khAh6QtR9VUoXKJNBI02wdAshdw7wLg3LIVK22KJBhRm3U+pZ5txDl94oTt/vxTgNGtbtNv5csv2yNrn7fRtWPdTqCAYUl6CdC5CvC1a9s2dxDRuA8Ad9lyWwGQunjFiiABJQk2fsVIs0nqS62gcZxi0RbwMzTrYSF0/myTrX/7D0hrdtkYAKBFCxa5Z9qRtHkRUoNS460/csQ+BgC+dPI4wE2BTQQ8lW2yJUuW2ogylIpIU21Ate3w4UO2F+nGs0ePAujOsoULF+OJtcqK4Enl2dp21Y4jLXngy212GGnVEtp5CjbOnnzlFaTKpttI1CxFT7WFJPfONjbabujWcOiwzZgxy6ZDtzrUJ7VIE3+0A2xeQSV9y4YPAebWWTlSY8ulfr1mtVWPG+tSiQLY3NkCvNDdjL01JE2/oF/sAdiuqBvvcR8GsCqjvQRO4RM4tCl007POwWIm/qm+S48VAox20q/7AOPkvEFjSwf96DTA3B9//wc7hXSavAk/9hpeZrHFJ/C3E4+9PTRfAX2vqnaMt6XU0iWBJwnexiPHcADxG2tAgrCnq93mP7vGlqx81GZje7Gcduylj5xFWu3N3/4W+h6ibYtt/gvEgYdn80GgCPBJwJnUtlsAHw/s3GU76FuH+AChrlCLHcUnX3yJcWmujYOGfXRKqZ9LzVdjXQQNVb4S8iulbeV0QpKvsrWoDxtyitJ0stF+/bOfWVP9EaseDJgjL/HandpCVizoKavA0kIq2wIwNyIB5iZGYM7HIBUu+Q1WwDtY7sGyv6fDoJ/TbjgLqTzilm+LSIn7/hhBOR1lYy4DzJ07bYewE3vmygWfA0gKWlJxo7EpN6VqtC2ZNssdDxXpvQzzueQ9aej9EIE5vXj05vePT6TtQJ338TwD5TJO7F53mjJh0RZKo/PBgLk22nH3qXrb13jMCgDmpAGhua0Dc5rDJGCbyh6BuVif3HoGY7yE3nRFh0xxQBY3ExBmEdnC+Jwie9k/KYrg64EkFHKx3Wi5UvEGvIRJNXM7m3kmqH8pBr/S5D2/3XkKDADmVATaMNuM4ZTmSQV5OeN1PKYLr9b0X2QMb15xH9tgD6QfvqVzT/mWnvx6PyRi+6jgdE/O+lU5aZVM2GBxMjdTJ7EZM5SPbZ2JQ0oxUiYsOUn4IV22dJSYZojGPhkflEVuedPP3Vvn/YG5q6z9jfX+iumzbfrocTauDEEQ6hPRhdAVA8G83lRmKPLdaD0LLly4ENO60WduKB7KbyxoEUgh9VaOJ1su2zvbPrHCUeU2esIE60VtTKX3CiQlCPxxe4uj9MWwUt+URJDUIeV9VAu2XqRrpCa4df0mq9+42VoaGl31a9LyFTYSCTYZTh+BBJZPGiicJFt6ATd68a6odCuRCpEky6IVDyFtUgXwpKkiUl0XL9tJwLkNLJgbd++xbtQTx86YY6MnTWKxXw0w02qXUSu9fPIEQEKrLXh6lS1du8ZmzJvrTgMkBSUwzVXGWFTKscSXSGlt+NM7Vv/pZ64SK4mocTNn4nVUtt667FzDCbvUcBx1tatIzSzBxtRaW/bk46hajvIJahuA4Wny3I303X6k787t3QuYVGk1k6fghRTj+iy8XaWOerahVngVqbRmJMT4UG3VlHvlKy/jHZOv1ePHWQddrEILdRbmTQAl2z/cYFsBIS+fOI4XyEq3y/fkt79jU1nwV0HHdozcq1UFIkUnEWoTSRwJiBBgKpW7FqSgTh6pt0/e/wC67bY21O5Gj59s1UhZlUtFlPZrxXbelaYzgEINNm7ePJuNyuPjSMvVUq7i8lIke/gKD7ggYKgQUCVXfFuIhBbs3XhUPYf9vncBTY4DVPbRDk/9+Meosj6GuuN0u9LeSpmCPbWrgCS7t261rRs22cnPt1jbmQsAY4U2Flt446BfDYCaJpdtgHmd3QAzSG854gUzCLiqBBAaP3OGrXj0ERuFlznRdctnnyLx94WdoR0Ek40CEBo5brzzp1Q2O8j/CqBdK9JvRaVFNnvVc7byuVW2EADSJTdFUf5Fz1akyM6cwBnGH96yhm1f2hX4qris3FYAAi1/6RWbgNMAAYOSQNDkNtBF8n5qBTboL1CpE3D0HOrVH+Ah90L9MW+fqvGTKP84q0AyTeVqBRi9TJyLDccchKudO8eeJh+BaJX0ATknUJpShe0AMN69E7VJpLkad+xwgKRyTK1VA6YVV4wkPdrz/Dn49gRlPuXeVRcCJi5/nr4wf55VwEvuKIP0pN4pb8n76E/bN2+yw9CvEHBoFLxRBYBYQX0FarbR1y7WH7Ur585aASrjM55+ypavWgVwu8R6aIte8lQ9yok/Aim3Zgbd9//wth1EJboJQLOwpNQWrF1rS154Hu+ti20EY4B7BqYfCNAUD6t+spcnG4vF8IEcpnReuWrbAdlP7TtAuzVCf7y+Up5WJE/PIKnaSj56dgwSb1UTx3u/1EureFQFfXCSLcXZyVgAV6mWCyAtps93NV9FKvFL277uQzv46WbAMcbOGXNxTjIDkKwce3KXkX5tROLwtBVRztp5s/Cq+goSfzNcTV12DdWusqW4d9sOO0j5jm/dhoMVDKjjybhmKhKVjGEC3Zxf6Rz+8tTLlPpqPJC9xJGAbbP4cDCXDwvlSBTqhaQFs2wNqsOdQ133V/9DwBwSc+Nq7c/+Xl5Zg425blbLsnGYcBpP3pnNc9QYQBsImLt6+qwVX26z1UjMCZgbqQ8LGgu8A1CmeMwt3lDhufEexGvYZEi63S56KI+45dsiUuK+P+YCcwJhmpkxHj2LV9bTJ6wRiTlJgUtKu7ZmtE0cNdqBuenVte4ASmOQ3n9iifBjTseJzn184iTECUzKEJTfBqFA7F53mjzXA+baKat4wiXmzhzzdUOR3tm8Hwtce8Jb2WuksT4Cc4NUMQm61RpGCg2d8q3dGfhGHBgSUh5sQXsjpQo1TsfMpUE65dx7N1qrdPo3+kw+3lenQNJe/Zqt30WShdpnYLimeLmh3pIEZrjijjTtHcnkq5P7jqeQtE6/RkpfDEW3dJyBhR5wlznyoFvuC5No4dl0/AGp6ZWc2jIPJR8A+t9NRfzqp7G8A7IYEHCdvBTf5U/9o5+s1Adg7qotR7AkA8yl1g73LTB3lcqdarli727/xAoqy/oBc7El1cSBR26WkDx4jU3p6oUXgTkZeNdXVknfCHDZtnGTfYlH0HN7ZMS/20ZgXL0SlbtCAAxW0jytYSoMVe7NFHtsEpWRHTUtsOUc4lEW8fKQGYzHI1HCgr8LhPXQjl22n4X1McA/OXoQYCNJoZ6uTk9XknYTZ8622SuW24wli3xRKzt06gERQBQyK+mUxgZ5G91rh77YhqrhMXfuECYp+k4saSdssCCJVAtgt2DlSgzFL8CL6xTU8UiMegj8amWhf4pn6/fss5O7dtklpOLaAU8Efjg7AqYIpOnp7HBgpZT06gBcpuNldM5Dy60aO3vFSM0IZpMDBS3M5SVti1RQsQ938cAB6FZi4wBrln/zNVuAGvAEytPWK5kjFsnEl6SS2sMnzaIkYJvKJrXTQvJvF90Aqw5u3WHHKGMb13JEoK/ePaiXSgptBCp65QBcM5E8mv/ISqSD5gKiYluOOK5GKmCOtF3N03PjXOAAlfSv5xy7AOaaAObW/+ldwIovAOZa7AkM80sicOLUKUjMtTvQKomgY0cO287NH9u+DRutBZpJpbJQ9ecrviTQijhX5xTgo3oWAQgJdBKIS+Ft9OzZNg3w9qk1a1C9BGBEgun40aPOH4cBri5hR05gTAHt7DbMaA8KyvPd7olzGgDnZIAlgV9jcEihvLSJhoKCZaPvHKrEG2iDo58AMCGNp23O8wBM8qILf5XA13KiIeBMgKNLzZGOaKI27wS47UYC68q583gU/tydiFyA5wReu10xACtNiFUv1bKENpgAKC3PtTMXLYT3AICREiRxyqXCI41GeqeQGDt+4KAd377TLgI0tje3BHUU2qNP7USbqm3KkNqcNW+BzVY9AagrALDlkCG2meC+HsrShF3AvfSp3YCpl1FFdo/E1EcApbzAiv5dqONWASZOIZ0FgNMTZwJUAUR10hZua5CylVKfIqlsI4H6ESq0+zYDuiKZKNByMjw1B9XcJ9euBkCrCmqcENuBOfjX+xs8qbQkQSfV1bZLzfbJ+g1uL/LcfvrBCLyyErMHurYBPoqHC0fg9IU+7yqmpYC31L1sTI2r2j7xrW+5LUg5ZREvyBmNyifJ1YPYm9u3ZYudPnjQAcERGESXarfS1MhUBmg6CSBzJkDijEXzbRTXJdQPwjqfn4fPNyIRfAyHDxeRDpSH4QLGIUm+uk057//qMeogvJTEF9TL7djR30cBHM5/HI/M0GUsdJXkq5xuROcPTYCDv/mPn2eBuZ/+1EZPzDp/EDuQonZ3bFNuAv6jevFVpGNHXGm355cstwn0wQrGYtnJG7Jod7a4d4wutzUjMXiaTroeri2dz3DlkU/3jlFAo02YSYWPt+2MPS2E1J85aQdPHrczmBMpYgyqrhhlk5DmHY+Th/F8SBxXOtK1E8J7gUQSHhR76BfBucgu8cit/DYIBWKXvdN0GgqY09xGEpQBmOsFmDtme8/UA8xVWBFzLc15mUjT2HoPayap7X4E5kK5b4Xusc286tfcZWNGOvWPHtY12bDhLU02n/zZV6dA0lb9mqzfBVlk2z83P5/35wbqiXQSQz8+yJO3EjTsGdxKoe6hZ2iMdHvcrpJB9uFIdkDxkkwyrTwUCDjgwZsM0IIl2bJnIeDmMSWVlnUDe+FEwl+uIiBkCF48NIN1/Ohaqy2tRFgqzDWUi/eZVBkUllsOhd3MNqwSc4KfOlUxKnqypdne3/EZTglKrGb8eFRZAwCVGTtEj8zFzVTh+nFFJJeUAVAReCbgoAfpJnkx3YsR9H2bNlsHUmIFSIAV4higm4W9JGeKAOf6AEc0idBCVQAFCIAvWAXSlSCNNnbaVHto9WoAE9l7K3F7ZuUswCtQZevGqPzenbtsJ1JqF7C/1oVKqIAo1bMUKavReBVbsHy51SK1IpW9YsClEYA8mpBKZUzqnjK0L7tVMizfBqhx7DCADl5Ejx88gETNZTcwrwV0MQ4T6mbOsmnz59vixUtsJGp4vdiL6obDJDkjVQ9Jp7UjQSNVv4Zjx7CrddwuAAq1IwXV3c73SUAogWSFqAvIAcNI6jQDqbQZc2e7UwOBYm5jDxrIDlYRZezDxtwe7Hlt27zZGrdttU5UE0di+27xCy/aQgDCqYBJrQBzgYaANg4+hQmV2kXgjDqPvJVKHVGdoaOl1Q7t348Nvj1I45y0VjpGJ94mBUSUMUmvmjzZxiLFN3PeHJs+Y6aNrBrlQFynpCKhbaCwUG/+kh4SgTl1OGUo6SsZwt/66ed25sB+GLXdlj3/ok0H4JCjjzbSEojYhUrp3j17UHfdao179/AwIJCDHkhJXbrkAJQAIdFGRvPFIwJNepES7EU9to90qmbNdGBtOdJ4Y/BYKpCpu1WeZevtyP6D1njiBADRWdK7THzKDbA3Ag/AJUiMzXloqUt7yakE4llO/xFS6aBikvwTMKf0WuGNLz//zA4g+dWAam4LbVoHcDnrmWftGQDBUfCXwzh6DmDFAUBo4y9otQEgkICvDnj2YqKuKnXR1rOUC1BdYE4RIE1x5Uj3NlwF/echRTVr9hwHqaVCLMkorYxEcl840ZbdAFPNSJAeQV31TH29XUQyrp1+1wO9FW8EdaygTeumz7BHAEVrANCKkFLt1gorSUf9IIIsOj+ONN8B+KMR/m0W3ZBAVH8W6D0Cia7iykqbDM/OAjCcvWA+/bLY1TNd0hD+FrOI3+QApONyi6vS7geMPICDhrarzUjCTrLJAKlrAcsk8ekLSOrnPJQAc+JnqRKLl8SzbYDeO5BIO7lzh12kr7uoKffUnxyUo1+LN0RjAkkMUBIeKUHKsHb2LHv41VdtHCr+cgIiD8tqVwF+BbRLE21wHEnSA1u/RNL2NP2e9iBd1VXOG2oo75ylS3BSM9eKkBoV0O7QNyTskU1AgLNNSIaehf7d8AmZu3prT1cHUo+AfAJAeUaN5v3FG5C8NXbA6yMBg2cB7s5bttzGA7hJMlhjhTzQyvNsE6DXm2+8bufgZ0nM/eAfkJjDq65sA2r80ZihPIfaIN9Nb0Onlk3K+0di+68VQLekud1eWLqSRX6VlVFftZunM1hig4Vlk86fDUaBW2nIwdLJDcu3RS5F7vtrRkB/Z+sYNCtQ3yfk2JlTdgRgrunyJezYVlgd74OJteNtDBLWVXzsGCXbkBqkYt9NKOGvHs7FKnl2SYhyAwd12btBr6GBOUyfUKgOytUKP/SXmOMjKB9+eZFS6PCB16vIykizPWcKDxhsd6u1HK5BLZYxzJfi1fWON16agTEDBdJ0yAXmlHv6/vVKo/sD87mRp/JxvioFknZKDrEVbrT1bgiYUxFjwl+1uAOeH7aEB+R0Xwf4hPw21+ArkP56/DVo0v4Qdwa9+RXrltDneuWKeQ824vUvgQoZ1nT6cC9grgWcwHCy99DM2Ta1RsBchZXyzmFp48OlV2uQdrpumfpn3O9q2IA5Tbb0i/ZDTrHgXbfzc+stL8bW0zjEwHjBxsqwgB32zQEgwC6BErx8pIrVjWRKJ2qAUi0V8KVXpBbfstnl76ekfJnScSugoyotF1KVBAxwlTsBM8RXwyt9LY2Vh9JuB9zpRQpNElVh4ygpHwCDMi2MOfrCWKCf8kzy1eAZQAmCtMgHmOrs6HDppk5Pj2mtaKf4LH4FDJawsFeasm0m9UUHS5JcxXBa0PcCsnSTjlQbBRQoXZ8mq3j68VwBEx8BCSUCY0hT56p7qH+w7aKFrdTBVMdmgX2HDmFYfrOdPXEM6bMfARYsszqkClsFbmoaTppFAt+gm3uLVNmVHzsHPaCZFtMCECXBJSP+Kp8kxySBJdrqa6noJWmqYgDEEvhIUk5Kxm3LqYxKcsBGnQjTz2lGWyjddlR/JdklsKS0cpRLggl0VW6yYSLgsAMJty4kC3soj8JiWSU9prS8zZSy5x3uq6wOOipP2kWAiaT8JG3k5eA52dHrArzrBtzspS0kiScOIkHADOLxk3SVVKpdWlNFT9ra+YI6aHBwmtGmLZcvAzaetYbDh23zW7+10tpaPPM+ak+uWuUOQBxUVTGVBr9AkYQegYDOH5JC65JaLjzSR7mkkulbkjcN6EBkGV+wpQbpfKFkVHbxjiKrGjpQRklXqZ5dSPapnuK3zMSc+OKtYgCgCqT6BBLRSYM3W553gE9HEvRz0u2Cbh1Ot8Ab3g5iL2XIs/oJvCyB5pJo1HXCPRSIBPg5sKlnoJscwFzG0caJI0ds83vvuGRm7dy5tgapz2rUS0M/Uvqhbd12IWnIQYYAf68vbSfHFN3wiaQMPYziiA7aOVVUQJ7zIA/lXH0Bfi5F4rAYPtH4pM2roiNl1Dil9pBtwj5JYZKv14O4ERTO1JX0fFNZdaIyQq+rV654udSPIl97GipT/PmDlEk00gbBVWcfp1gYS421jPHAbfvQhqqnAG7ZufvDb35r5+n3NXVjUWX9B4C58dZFURxgVZGUR5YqSp0t0CLSIylxuHUDe6/fNeJFYK6IOrc1NllZS6e99NAjVgettbj3PpRJJJZCpVJxMzeukUP+VoYCWfJlgr7SSZ78X4l89/rDYhf99GbRTzME2fE9g3TxaUxVNPOuHTt6rE1Ae6GC93wp7xxJ6BfTL8Uaehdo83MdE37Js42T5b7a+UcuSiwP9uIFrRuCCRyAudPHbF+iylrIu1xzPyYMNLjmUTzg71O1evxxetu32z243fYC3oUERe/8djcpELiSfb+muPFZlD+WYm1f26lCcf53NyuXzztPgRuiAFwc/vt3g9SzaXbOrr5SEfqdqkPg4I5DMS+jEtZPLWiRGRp2D82abZOrcThYUmGsVLXU7beFJ0NQzi0PHCysXwKpi2EF5iQxpxesJOZOIy22btcW6y0rwtB5rRUgFaOXq29pyqUKd3tPKQj/vrgmYZ/YKV+nVmowi0FJ5lxmN+LG68wxoXa8jpEV7Le4Ec9z4yhuDOujQE4Gf8h3GUbLnYTGPOKz8Tp91L0w0CqtLDuGlJP6cydep5/NPc+mFdIRtbSoLSaDMgBGdPrsElJLW7/43PZs3IBUT4Ot/uu/s1lLFrujgjbABC+rQBuBOloUayY9WLsTMZZJx3iuMnkaSeHieTwOuJ9+MHkm0lGXMe0YLaaTe4z3BxxjxCTtzCGJGG/nHmO83PQUni6fCph+Vufpa/UcAQ8FSAQJBBZI2os6c+OZM6g877KPfv4zq5k50xauXmMrkUQrR4rMbe+RbgAd/ETZ9ks4litzg3RjvpmoqbLFsAyHKVkVPm48rKtMSHKdSTPeiAFc6zReKpkYJXPOzXRYJlwnbJnnk7RiZE9Tz4puxJN3US30ipHixNamHUaFet3P/t2l+KavfMSeXvu8S7LKI6mPwp4ptUtWgZ6P7iVbut792jJGyDnGiVAoV0yHTDwfIqusmbSzD+emrXQ8DT0SH/DoKhF3knTic/2iECNehzTCXqGqpvMZJwFbg88EHBJFKqICkrsArRsFzP36N3buRD1A5hj7/t9LYg5VVp7vYXjwhJRYQrdsTQLXhBx1O5YkG2PIMx66XuwIzI0QMHfmnFU0d9rLKx6xsQDwKpbXKZNBKMUtlSWTRv4kT4E8BW6UAuprGvU0DdBHtS5U+y8gNX4JiWB9eKvGlEa1+ipjjs8ZiKsxLN3vde6/dCBh+e3+oYADc7SfeEGfiPVZqxN+kEOQvafxynrmuNuYk3mZAgFzzCGZSBKLh4gX3ubOBUNW+pbZI2RBuvHNMGQWD9iNW6boA0an4atu4MicATEnu2u2Up6lc6iVv7z/KJBdvwy5fIDPs6yePVNd+1+FEK2ZtJ4u4WUkYO4qwFyfgLk5cxyYG11Sbnwi8jVEml5KK53eYH1vsLB0GvH8jgFzjQLmdn9hPaWF7kmzEGmWAMxRlXRtYsmG8RiJo2MuMYcx2+smHacY/aee133srkRwYI6JUTkSbJIUOnn8hG16/x2cNuxyqbDn/+YnNhWpI6mZdqCa54CBIGaBsUkDxHbINL9m6JmLu1KtezvTSDBKqQWKVJwLAB0EMAloaEcF+xCeY3ehOn1w3Xs2E7tgK7/5qs3FdtsIFjpdsnunGgog5S+QOpWo7iVbBNrUJGqv6zaLT5CJ6skNnmZM+04ec0vi1wmfyaGEvO5Kqkw2/3ajCvzlb35pdYsW25IXX7ZlqJlLAlUq0pKO1Zamg0svxgAS9rTZxaCbqqcecmLf1FNfObKXOZTc09K12jAshAMgJ4BLtU9kFFyCTyrKAubkMEQ25t596y1sCDZgpmCcffcnf+cSc5KWk9dZT1CJpt6cykc85tXmXNvtBOaUfrAvx0uWPtJ+5ryNvNplryx/2Maw2Pf7ylMZe4NFjlfATZYlPJLf5ymQp8BNUkD4vjZJH8sOcDMS7B1oA1Qi1VqOFLW8mPs7izjZvuqP+M7DdC+eZG/lz+4TCqSBOekMSGJOwJw7fzgdgLmCUVJjRfrdgTkk5pLPKprF5AJzA1hhMOaI79rB7mXoljAn1yF69joT5YE9GUDlB5YSd6vigRvZX6sp8ix7t5onn+9tpsDgbB6AOb83eASf3w/eDfrP+UNx9TZBYo5JRwm/UuYlDsxh4knA3MSqsTa6BFNf1wDmlFcsSjymSTFYWPq+zu8oMPchwFx3SaFVYrdJXgR9NhVfkLklewCvI5tkMeB7lwi+3kZaqw91thPYqzuAba2DGz70ydPEJUttNTazqseOBZBjcc7U2kEMzoNET2DNOCcSI0f1uRth2nuXKsNfskgf8YjAOBngl1qeAJLj2BDb9dEmO7TlM7dRtxRpuYdxYlCNIwWNFF1SlZXEE+dOcx0HG7EITEteDRYlt6bOu4pIWWIZc+PcC9deNlWafwFNUme+cO6c7d60yfZsWu8qo0vXvmgPr11rtUj2ig6dUvUGZHLC8WBYCAQaxr3XLTL0zRIgEnjQxrjzVFPxHZhzGnHOUbzmDjag2amGBjuGE4lmbCzK824L0rKywdiDzby62bPttb/8SxuNfcpuVPMztgdFm4Q+kTyipKqeWXhH+l2jyvFZPZg5z4kfwwVcF/FiLaL92s+et1Gt3fYywNxoVMoVR2NY3OL4E69vCiSMD+WPeQrkKXDDFIh9TmOAv4jo/52o7XcjYV8KCONmHwjTff28X3vkzFCiJ/Pb14ACegdIWk7NmwXmDFXWett39ji2qTGngiprAaZL+vQxzbkhjvQ6xl/CJ4T4NtQ7Jb5rh7ofn/cSBfYMpcvceMBPIu0fcDLcxeqHoZD99ZoiGTPvYlHzWecpcFsoMJDVhw+Yk4OHUjCOq/gf6EMz6KHZcwHmRlvNDQBzquzAsmbDBruXJtCwA3PR+YNUWTfu3WpdxQVWgeODESPLA1gTX5DpUj2A55lBlua8XqPdC+RRGeVd9OSJE3Z0xw47gaRc+5Vmm7Jkic3HyYGcAkjaqBtpFYEbvXqAn+PROg+X4YR9nKQntzLh+ZM0BQJ1Io0iMCfbY+caG23HxvV2Eg+yPYBN8qI5Dxt/U6ZP937mNtbU1wCYAugWFzyB8wLXBaBEOcZ3eTYkXY776TxSK5RZtZaNNIFMsqF3EVBOThWOASzLkcTUpQ/Zokcfs5kLFqItE2wXihaBZmm6DEIDsop0G+TuNYNUSn/2VhO4Zur9b/anSP978SpIxyEpR3n8R+cVNQRk7tm+zXZ88oldxPlGDzb6egB8W881ucfZCfPn26s//jHA3HjrBZiT3SAHNXVMTBfE/MVbqm6mytddJGVfbHoophPLHI+eFRcBmOPtis3E7nOXrKqjB+cPy20MwJy2NDDnAfldngJ5CtwxCsR3fjzKvmYP8wXZNB2BumIaHM+MEcnJDQwVd6we+Yy+OgUEzMUf8unuNE4Sc/sa8coKMNddhtMPbNDKAVWPPi766B/fAGHE9znMIIyR5iMvKTwU5zXhGQ/tv8vECQwXlikJ8/WP+YBeRdo/oNW/B6qdcObQE6F7oIz5IuQpcNsoAMMPHHXCytXD2cVjOs9BISbSiu+AdNxw3oeZrj5XZRUw14qzQkuAuQkAc9XFZdiYC9pE6WfVH+MvHR7PVTYvXxKQPo9x4rHg/PnzSuu2b/7Vi1S7SP0qx8bWFtu8f5t1gCaUVVdbSWVFAAwGpVoojl6oTrykhLkViQWPx9teiXyC/SiQpr/O21tabM+uXXZ861a7JI+MM2baEgChhQBCBSNYxtO2GYcMtKVPvNKJ9Es9XFzn9iBPPGBBMLtopF8ETS4juSRAbtsffk9/67O6OXPtaTzM1uIddwRe7NpQDVI7eH9KHnapL85j31GwtnidPe8/fKXv+wP36C7WJwzbqUImdJAKazP8K4+42z94365eumCVGBt/CCnDSXhLrsRbbDvAnUClIhaJ4l1t6foPeh4zDtHvvX260KnS5RZb1/qJx6Q27SAWC2Y5ZNn95Re2Y9NGu3TosNOjGO+J8kpciifncTNn2DMvvYw32+A0IysxpwSB+0g02rpTUQR4Zoqkm9fZMjFUrlTceK5j/LmqN6Bhn5yYXGy2al5Ka5css9E4sohxUknkT/MUyFPgDlJA84MIyinb6PhmyCLQ56X6GoeJeBwyfv7GfUOBCMr1wBOSnOtkhG7neOj8Sdvf1GAtBXgqB5iTI6we/8CjN/sgoz5MkQ0ngRgllxLxpXON+2HmEyJSLN5T8aHcxB7E66EI9yDS4u7UOeHMoXn87hQrn2ueAsNDARg+PeqE8zDa+zm7MCdIx6IommcMViICB4a76JA7fygBlIvAXAEaactYV08YVWPVeIdHdnvAh32lFX+DZadSpUuWPs+NP+zAnCTm2sj1bNtV+/jgTkC6Hith0VuG7bE+Vns9mmnFTSVNLtNfuRSUqYTekNloMbqH5XfDT4HYDjr2oHZyEaP5V/h14Zm1ZsxYqx1XhwfQatQm8TSqr98qEpGlyiqpOV2nWlx3s22bc+4387ssBSBcmv7+3Zgw9y6LSuEFbH0pQgU2tOowvl+Mt1NJIKgtvNsAMnm/UnuQavxlM+h/pvva4jFc3V/7SK9QB/b8K0yqmt1If7Vh00g20uTZtaKiArpNdElPxe9GBbJANOMLvSQOk/9sIzgpQg6Kn7qMV/fmMVPY/sWLtEqHKsx/PONOMxivrwAEXz5/3tqam31hXYidyVKkYyUhKy/CFdiHKoL3XDoT2rm0IYnEbPUq9RcouwAWp3L2G+kS9D/PxFR5Urd03v8HoKo4kuhrb7WCy602prfInl2IQ5oEmIu80C+hVJr50zwF8hQYXgqkwblrAnP05Th+qETXGSaGt9D51G87BTQvFCjXgTmEXj6a9eLcQcDcmY7LdrzlvB05d9r6sC9XhmfwXklTsizKLo2SkT9hCr1f+m05l34vMtOdZZTmAABAAElEQVQ17uWBuX5UzLkYjHA5UfKXw0qBwMLs800xrHTOJ36PUCCH1QPbZ0d7v2aX2x3iUO+1SF2kTlMV7A/MCZxrQ2KusKPLHsJm/oTKGqsqKjU8JNzfwJwk5gTMNXW02pfHDtqlLl63eGQdid2rCMw5gZygSMglwFsamEtRLZmdpV+Z3M1tiX4P5C9uGwVoqEhqPwr06ezkx2QKYKMMuzBlLM61KO9FUkV/aluBcuE4EJiL6XkZnRF8lymyd7t+kTK3HrwTSBNJoWNaoaMPwERtocVNMdJLxUjK9eExVGqGvRyd/jwjoCmkktOHCO1PeQKSbajweP/+OYaaBAqwZ6zpAXzrkmQcm6QLxb/a5MHWxyJ4V2NRoKDfyqGTUkvRLlyGiPfbfpCGjtXRUcBcX3ePA5qim4uvJHWUkfZCFkziPw3hAYyj3/uDof/7alrX3PeDdsmWyTpZXMXw3GPmkSSNeF/hGiuUkztGIR2ZCBcwp48GdqXVavtG2KoFi60GADamE48xnfwxT4E8Be4cBeJ8TzkOOeeLxYmDRL7TRop8bY49jNvdvDjkAZ3PYUjFFSE1Z3ahp80a2i7YnoZ660ELo0xmCJjfFEr6OlkaufS/RvTk3eFzxmtQJsx8shGGih9nSHqf6aUV2S/75IN8lu+Ed7v1Az+yzzfF3W6KfP53ggI5rJ5m+3CejOTpG7nlIo3Qb3Qje9Y/mlRZ5ZUVdVbWO20XL9sIMI7lOFGcMLLaKguLrYQHXIso9aBSi79UcOZUxUoXLX2eiZScDKvEXDDiGoC5C53ttqfxuDW1NRuKRTiAGO0SFXoZO3kopZM1vAUzL9ncAofI2ZdkfHZAvHzA7acAxI7MlDmqvbwRsveUse77opwzHRXFpeXig4qUu8W2T4UPNWlKRXmgTiP5dBRtHIjwo7oT1wAjhYBvAup6AU/0FVpb7DFx8aPQpNn8/mC7eD8eB4tzv4UFuoVSO/0ICAOs7kAlKqv6RnqFuif3ksrGe2mOv+9opCrdzOZEYdHEC0pbEXxWUlwC5wVaCQxzGiTpBnqIO8OmMcDXTdp53EBrqVhnqa1IyQNDHPx2kmg6qs6jXTyVRYbjRyjtzm7rbGm1QtydjysosafnL7RqVG8VP/38ENnlg/MUyFNgGClwU8DcMJYjn/Tdo4B4QHPDbgZuDe1djMzdvDBka+5iX4edar9k2+sPWhcDdgkSc8VyAlE4glhxBE+N5uElc83KeC7+3iGa5kxDxPbXDPHC3xCRHtjgoaj2wBLkjlfc+TMzw7rj2eczzFPgjlMgd9SJ1+EYr26sWOFtE9YB6kvZpxNgrhvvrKx3urCdL++sy+cusHHlo2wkH42KiX/fAnPRiGsrg8eFrk6rv3zWTl48bxdRLaoeN9btEkkSRTYl9D71hXIYbRzMSdZwyYovIXZ4W/pFEjUMTVmqJhHzh9tOAQieJrOfxwmO34PVNZHhJwDIQSBv2LBAV3t5m6UTSRfSb6YD8udpCuSSTde5YUKW1I+KcFygzSe9ahO/ysb3a8J9UxsNssVn4nGQKPddUKxp9ig+TdElqWygWBZYyoJH/YejNAHuOzpFIqQrMci56iWwy2nAFyTnMYgmcE6UUzL69au/B4Y7Hh4jEU+b255MHshyJzeG4EV/SLeTZ5SctpBsuPLSJPxfTNkKKGsPKt5ySlOJHv1kXqorZ8y2UUj25hQnJJbf5ymQp8Bdo4DeVf230K+vMyT0fyR/dV9SQMCcq7NyjB/0dX0FeK6xvdm21u+zq6i5ylRCGaY6iopYGsEYgWfSoznn12EYf99keC3w2OBEEz+Gt1MuZw4e/0EKvRbdHiQ63N269ps73d2i5HPPU2BYKZA74qSvw3k6ZPCixP6iVUv2nLjxfcC7Q3ckMVfc2WO9fNAvxnlcTUmpLZg6w8aWVlg5wJy0ce5LYE6gnH56ybZS6UvdXXay5YIdPXvazjRftjETxlsRrs97IUQPqy2RYwBZCYgvxLggCwGRpMn95MEYlyzz2zBQIN0+mfZQg4jwtLEmSfHnqgayz6VWhYPVwh5N5UonlC5n0oBD3U5HzZ8HCqRpFXuFwoKqR9I/1C6EaZ/tZWqrJI1BJrLJLY+QPg9P3L970SZLsyw1PDBT0UhJ1TPGDjczUVIkGCwsdfuePM2UOVbvGqVUXKdb0r9j33cJzcg7CY9lkvF0k/7PueeX5KVz8V6ayoEXCYzpZRLKnvjjipINcik5XSrMfyQkybkSVJ169bULG4JXL16yCeWVNntMnS0YN8EqSoKkXzqdVJL50zwF8hS4CxQYCphTUa4xLNyFkuazvN0UEAjn7wWOWjN0cKEP9leB6862t9jWY3vtClo3BdiZq6iusSLUWTXih2fi6K8QzmEWhQ+9ae5z7RjZZ9NvqeQ9lr35AJ/l3573RuPfKB/fG6XNlyJPgdtBgfTokz3PnqXz6D+Chzux1+gJ/aRhpjmGCxTx9ijmhTSivcvasZ8vZw8Ta8bYDNYOVQB0ZawtAjAX8gv77PsrnXfueYwbj7n34/WwqbIKlNOLVT8Bc1cwQH+27YodbjzpUnOVtWNxf15G7WXolUhQJU6+RLQ04bgMG4ExPAbpOFhY+n7+/PZSIMNUmclNaJg+OXtQGD9J0xTIDkjStmqjOPnynjBYkYiUSXuw+/mw61Ig0E97UTxsmqxqcNJf+n6M4ZPZGDk5xnsxOPc6ht+rx8H4KIYNVt/+9cgO5Vl6JTFiIlwG9g+Uud/oo9rcSJk9DnUWGBerrqP//E0WEooLnXSamThJXrqOau2ZoYMwbZHi6efDnbDXsyqwHznNHkNrisMVpnIWkkkJHwW6WzusEwcVrZcv28yxdbZowhSbWlllpTir0BbT8Iv8Lk+BPAXuKgXiGJItRLaH+ln2Mhslf/a1oECcG0oKQbblBMwprI1xXfapd508aOeuNqPuWoB96iorYoGEzQ4+6qv62gX5BX8bxIWEbg2y+buG9OM7Z5AoqaBsLB7JbxkK3JnOeGdyyVTqvjoJ/Jjnyvuq0fKF/UoUSI8H6XO9A/pfh2zUOzSCD9VL9Ix+wi50lLCBniiWGmtbp7U1nbe6UVU2o26CTageYxXYIS8hSrHyU6KcSxAgbkPlE+/rmI2dDu1/XnDu3LkbSav/UzdwpZeqwDkdg2fWXmtGFP3YhSY7fv6sXenuxP05nvwwxN1XXOQvWaE47iiAVVskpSohkg+ctHGDLcYLV/n9naJAhrngHj/3NlODBHaKHtZivLAg1/JZbZbfbg8FhqZkur+4SnFsn9uT8X2RSuA99uE/KfPgA3isUGY8iaRNGHiop2K07PPx7FaPuSneajo39tyQuSX9OHTumFaWCjpzRyJJAnIwEjcFiWweJwamjrqfoXMSPmQ5Us8lTRFAQr9IyqOy8i/nFPLXJ+tDOnY2t1gHwFwJRZtVO97mjp9o44rLAO3iIi6VeP40T4E8Be4qBdLvrFCQ0OOvg7Pc1TLnM789FND4H98gcd2g63bG9MusFRqam+xYU6Od5SNLedUoPuqXu/Rct1iE8bwQT668kDLvHAUP9k5Jh+k89z1EUOpB3U1i6RDY0aPcPzsKPRzl1oR+mDd/uw9/NsNci+FOflAOHu5M8+nnKXDHKZAeCtLnKki8Dsewz47eoaj9e0qIo5WAznpRV9VRb5AemUxAjbW0o8cqAejqqqpt/OjRNrqiko/6si+XOJZL3glDrXNCrre2H1ZgTtJyernqRdvJC1Zfv85cvWKnrly0k5fOWxdSDUVlePEEoJPknEA5Sc9pnZV9OyaLL70iPVz3whYucwLjzfxxWCkQ1dkCeydZJQ2kMAeDnNXDvYykzLCW6uYT799Zb/75e+GJwXtANlQ9KPYVHWPbpZrnXqjG7S0DFXXezFnV9ePXnByzFMveyMaP41D2Xjwb7Ll471aOd5snvT65g22GEOHEqQFtdaUFdShzmhJZemUeTYihWP3rmBtjcKopVuRdNavnQN7+NInqpeqgHF+/OtvaHZgrwM153ahqm1FbZ9Orx+LqHPsQSbkHzyUfmqdAngJ3gwJ5YO5uUP3eyDO8E8KsX2sG/RQmYO5qX7dd7Gq1hnNnreF8k7XgRb14ZIWVVle5p9Y+/9CiOY7eCPwxvruZhSQNDv02past5BmvQpgCQ0gMj1fxOol3Gw/DlXJ4q/qL8jaWNtIppH77E86mGNYQyTwuG5w/gwJZnsme5QmTp8CDQoH06DPUuXpG/EW6hHWHrxzCe8Ij4OcAszfAUb6G6OL90ofDuAokyuagaTO+utqqAeXKkZYrJpLAPOncRIm5kNog49SNdM104WMhlfZwSsxFogigk3i6wDlJyjVhM6IeqbmLGObuonalo0ZaIXZ/0H8ExEuWbL7Yo4BecHZeyf7Luf5XqVrdgdMbofkdKMYdzcKbIql45Ccd47naKHMeGi5TvlxgbjD6ZZ5NnhosTibB23ISchj+fG5LYW9DItkeE2idS/HbkMV9kES61um2T4fHaihMA286Xrw33Me7xZ2eL+PvgM0JlEslXStuNr7OQqz0PqQWYiVx4yMeLcQNsQbfK0YGmNO5opGGjgLlpD5fhK2IPsC45osX/ViJ2uqsCRNtSs1Yq8POXDlxka1IysdJfstTIE+Be4ICeWDunmiGu1IIvQrSvwjMdRHq4ByW585hm1rA3OGTJ62wotxGjh1tfeUlJmCuV17o/X0SgLkRfICJbxQPJn1NSeNrLeaVfm+p4vG+zrMl4kyJxYT83u3YZedjtyO13DRC/eOyMffuLVyLfvGxtMRczlw/Rvmqx1D+r5rK1/f50BbRPM3Xt575muUpMBgF4vgQj4qTPs+O8dlxK4D9jLvc9I83HAtkgguJuUJ/oM8629us+1KLjUKi7HE8sY5HYq6suCSAcZ6HNHN4TvHJMI6w6by5k83UL4bYDXgoxBs2YE7Jez05RltzkpxrhwiX8NB69NJZO3auyS60NNvI0VR8VCVu0Et5/fZaD3F6MqpRetEqNS2Oh/dFplxuZFO9ct7gN/LY1yJOmo/EmFoQxy2exS9dMVxHvcfjZCu2otMxiRSZO/2MP5edCuTeyl87BbJU1Flog9gSaRJl4zlJPcpg8dLPfI3P+8/A+w2w6VoPTc90rOE5Dy2WarfhyWZAqp5jLn0U67o8M7DEuRyWW5v4cssUIjdCvMFLQGllgTki8q/xx0E57o9AlamHr11tzVet5eIFq8GD0uTRtTazbhLelMpsJIs1rJoSP/aTmHj+mKdAngJ5CuQpcDcooCE//pS/zsNcsS/RtmHdwMrgKnaqz2Nnbn/9UWtRrApG85Fo2zCuS40188riXVDowBxvjOQF5AdfSIRFmfLxjMLOL9M7lSEkSPwkjYHAcfqJe+9cxR5qXn2rpY20yBAlEDaS+VaTzT93kxRw/vRnsmc3mUQ+ep4C9yUFkuE4M+ZkrnO7AjcUlAnWGkGamcKXevTrcTBOWjbFWjsgNdfZiV1qgLkS1Fjrikpt5cy5NrZipJUkH3pCXnIwF7a4ftE4G8uRIWom40zIwJMBDyXpDpfEXLoEEZjTy1b25lp6u+00UnNHz5y2hqYm68XGXGllBW7QR7rdiF5IKScC/ucFZ8d/PyKnM+BccW9ti5TJeT59GaPEDNL3Ytj/z96ddkly3Xd+v1lr7+gVjW50AyABkAQIUiI18hAgCa6SxpIoUZT80O/AT/wKfPzID33GHlszY3vsGW/neM6xxBEXSZY0IsGdAAkSIECsDXSjdwC9d3XX0uXf7964mVG51dKVlZGR3+zOishY7/3cGxEZ/7wRMWZdV0izpAqagPw39sUvQCtB2gNz7YRdqnVcwMbLdeX66/tJkgWmjVsl0Z7j1nTFRJogTd0+5Vh8bn6LL3Ib7Tr1cr00cWIusGXnqVP9zFNtvtyw6v+G1hurWMvHQN5PpCGdS0zaySz2N2ctjymNV6/HWDvvfya1r/FB1cG5Jf3qdev69TB//UaYWFwKRxWUe+jQ4XB4x56wQy3nfNNWncYRmEuk/EUAAQSGLuDdfn77HCG/fMRY0nF6Xt15hep81c0NnTydvnQxvKMfXi7e1n5+944wpattJvSU1saELjDyd8/mOy8pDUpHjjQsBtny8aY1mfrSwNYon41oaPv3hRXzVPNDPlZuZuq2NjDnHNTh1apNm5Wb1hJbfZu1bJaDQJUF8l6hV7ec9rx1uOvpfVWNd+i+J/YdB+Z0wJnUZ19pc0tXcN6+ORcmNeGhbTvDg7v2hYf3Hwq7dHzxeUaKdWji4ljg5cUzP/XEc5Lyit3vla728kK6vAbaYi6vzwdbvx2gi63mlLGr6jupFnNvnz8X3tevYMvTOnHauT3M6t4RE+pf1rW8zpfn88HA/fmt3tbL4+7moBlh4kJay2z2eY1xguYQepJAPjFec2BOpXdHlTuVYedJerHUgtdT8VpdQE4lKqvGHcVqMxZ1PtXs1BK11yxrWl6vmYc8PNKUN9/mfmKlW0ympst5bXVzBnJr3RK2Rnm6XJPzPHmOUe7mPLXnIQ5fSbByksI3u2eTPF/zS30xVywaTVwuorzAPG/rc+qL+x3NUG4l54uWGjqw3tCDHhyUC/OL4ZDuK/fAwfvC/fsPhF0aP619j5uf64YJBOYyKl0EEEBgyAI+XvidzxOcnHRsUGBOY3x7m3gljfbc/mH/5tJ8ePXc6fDahbNhfnYqTM7OhJnZ2TCtVtENP3FbM5fPGeLy4gJ1nHA3rtB/iv7UFwen3jQu/VVgTse1YupiyrvrxKTc3SLWPHdaV/reveaZVpswGha5iKD5GL75OSsWv1qKKju++ZVzAClMtVI1c/PZB5BaFonA3Qn4u3/5lat97pbHdevPMQtPH88v1HJuQucNDf2If0c/+MzduKErbhbC3p27woMHD+me1AfDAf3gMxN/+C82s7hBa8tT18vI9zP1+jrS0ZbebmnqnClNtSWBOacvB+bi/eY0wL+CXVazwYsKyr1z8Xx4T5e03tTTMNxyzsG5mW3bwh1FffwwCB+c/ctZPrEr59cw5c9dM7+OgRm3/zKLsf0nWsdaR2/SZiUXWKMomGwXc1M+ohZlFx/soZGrsZW/BsUNSHOsNs/oCa4/xSt8PbtR4o6iMFV/bGlaDPMknqe584hl4gKLA2On3TV6xxX5T9oBqWd0X231MEop07FGqZuyqr/xf/yUhinHqe6lrLc7ZZDa1kxvszmT5W6uW11HFhMmxvJcqT8ObxuZy0Bra40p+op1uRzyOHe97/EvX7501Q9yWJy7rZZyN8KNq1fjDVoP6fHmDx4+Gg7qnnK7fcNWzT2lGR2Ym/b8xVsdXggggAACQxbIQTmfH+T9s3/0jVfPqLuoFg4LOh4saax/3H9v4XZ45/ql8MLJN8NtXeI6Pat7zu3erdvhKDgXHySnY7wWlAN0+XuNjx3xh/w+x680Kn/38Sf3p2Xpw129ct5SLu9qUV1nXpmtnJOuk254YLpVTfmo7Nw4Z7y2VMD1u/u3tC1NBitDYCsEOvYwcfeW9nEd49oS5P3+hHbiy7qENZ43uOWc+oOCcssKyt3W7W985c3u7br9zeHDajG3K+zVucMODXMLutQAKZ2jpDVqn6cerzftD1P/itXmCVcMbPvQI+FbEphzUpzGfEmrD6x6Cq1u6nonXF9YCBevXwnnrlyKN3e9rvvPTehXsCkdYKf0S1hDref8OPRFK+jlvzlA588NwbXOvXvk0hN2vNLyPLjVl3HLyymPbU2bW+mVp+xYRQ0H5Pw2K2VxQPbw5pcfm7YKJX4R8gEkffkqDiV5QTYycWl6D2q9Cv+VxdAaPeZ9kbFkE3Wbn1NP/ApV8m2Olt3KbSl/vUpdT1eU1ggrt1e0lVkpditxYCSKOMq1us05S3apN49J061cYj0+raxHefMsWgjGitG7Znh0FnI3kpql7OjPpVe5HJpzFGUQD3zqz09N8k1bfVBdVsu4xfn5sKAm6Hduzcdftu7duy/c7ycpKTi3c3I6PqXVQTk/RckH13jTVnVz+kpJoBcBBBBAYAgCvQJzToqPNP5hPv5Arz33Hb2v6+/F+bnw63PvhPeuXtElrvPxktbZHfpRXw+G8I/Afpiczx18/Ggeenww8gmaOr2OAWmS+Det3V8GYl/s3NUfpyN/y9qsZZYTlFLaGpKP4+Xvea2x6+9LZikH8a9tC8lenutfy8o52vO0cmy1Pw3KxLnevFpZbUNSh0AWKG9P3eu/hrbtMDyPH+wQ324ht6RYxOJiWLh1O96TemLhTtg5NRP264edA/fsDQd0tc1utZTbrjMG3/rG86e3FqIe9+dXPG+J+8CVw+P4tnTkeVZ0ywsrjdiywJzX6YOvg3O56+Ccn7p0Q9f6vnvjajirJ+md0tNa5xY1dGoiTG/fFia36f4RU1PhjsKW8QRNZ2eePwIZJL/TAI9Zw0uHqxVo+pD+J/SI1SnmIc0Dnfq1av/1n/hq9eUh9evGPMrK3fzOuWweojWizGvr9Mtn0aIrLiTN1QxwJsw4MJZzsdA8Pq+DbqvGNb0zireF3J+75YructDnWIeL8c1yituRl60lFMuJ48rz52WOQDem3VmJ+VGCmxlNPVnKO9a8TTcnKg2L1TL9Kbb3lcssW44Ay8aSaLJSxfI22d4yMy846boK5XsyeMZCKTquWFSeLS0+z+w5tA7Pmd8uP1++6uCaD7BLDsjd1C3B526Fhu4tt033Fzq0b184duBwuE/dHZrWrek8v4Ny7sZ5i3514jB3eSGAAAIIDE+gPTDnfXVz36/+eMxRN06n/fpNHR+u6F7VF3SfuTPvvxfO6z2nlg9TOl+YVmBuSQcJX9Y6qR/24/3niuWpE5flrl9eR/srfedMB6PYr4lKh6b2ydf1Oa1Pf90TF7q53yByOvPiW99t1pXM3hPHpHvphZ0bRjgzPtZq5cUhvvf86xyTvn7mXK1z5hpPHss1FUONc0nWEFhdoLkZaGfRbX/h/ZLPGSZ18Ggo1uRzh/k5v+d06eZC2NaY1G1vdC9q3fZmn4JzszpuzOoHnW3amU15n1YkIUWetJ/LAzQ8Bubc9f6vPalr2W11zJQW0rh48eJaZm9f5do+e8ltK/aBNQfn3O/gnO8h4eDcVbWWO38ttZy7pEtbb8zfCkuavzGjFnS6h4Rb0E0W95DwWVYK4LRWUA7o9E9gLsDWVOlgnD73Wk4e7m7sL5VQHtdaYv36snQ8AOfspS0hfclQf7ypYh6Wp1E3fbGSe9MujYzDNb0fwqu/zTnsGR9nrEBs07s5djx7sr+Zmv2msF8mKdu73/9Lw+J0K2bW+FyPNdy7l3JdLs+bVzFK3fa8xPzYyxARw0DOUfzTzH8ksUeur56kOTA5RfU0mwfU61UyyXXIdn679ULPAK+ZdFCLJ1byciu3qKVuDLXZNA7x8D4vrT8mQb9wxcJSd9kH1ds6oOoGrT7A6mgQ9u/cHe8JsV/3hdi3Y3fYrRu1+kEP+Wat+QQvd2NytNo1paFP8hiFAAIIILA5Aj4XyO+8by7vs3289vEgTqMJbunDDQ25fmcp3NAP+dduz4ULl98Pl65fDVfVv6QDjwNyDtTNFOcN/j65pPsKpYO/jgE+tjv58bjuHr28kvRHw9OPQ3FQnNDjNuNVXlhc+mYsNKe6uSx/T09L37x1pIWX0q/AXB6WbipUGtdMyUZ7iu9pG5291vPJJkcFap1PModAp0Cu+vHqmc7RK4Z4j+SHPAT9gH/9ip5ucEs/5qvV3K5tO8KBXbvDoV17wr179oVtOm+Y1nHDXd9Xbloz5mOQ96ANHYTi3i3v4ordahpWHEvKa17Lbjcvqzyf1zXwwJxXWFq50+p3Ds6532a+qasvbb22cCtcvnkjXNL7ytyNcFsH3Vv6Zey2mh66KXtclk78YgDIJ21adj757kKjJa/yKqUtJqz8uTyrE+pXx3gPKEaWD/Bx4pr9ydlUtuLvZMpvrLj+glMwpBP2lO80eeuvW80Vi4hkpU9pTHNkmj+Wq5ddM8bNyk50yWYF0ophXlFpvMfljx5l3/w5dz28/CqXUXn4qPSXa0/MS/6GX2Sg3SvW4ziuBdru5tFNLy8vTlpMXyx35DvOV361UJRxKyowp3GpL0+UTBJFUvdff06zb9CnKC8fFOPTk5Qs//K1Xfd/uEf3gzi4Z2/Yq+6eme1hh2/UqmNDbiEX1631t04dUlpzSnK3lQP6EEAAAQS2WsDHk3RMSYfTvO/OXR9wfUSKbw30E1rn9EFtHuKDIW7pPnPXbt3UecN1nT9c14/6t8O8hsV70zkYp3ni9x31exnNc4Z4cPJayi9PkY5vK4aWj4nlERXpT6lOiYk5isfqQSfOsMU6ygnYpNWO+g/Dm8TQdTEE5rqyMLDGAmlXk88v8q5Hn+NuSA0C1JPON/K4hBHPHdTbUEswnyPsmNkW9ikgd8+OneEeBej2zKjhl8ZPaZ857YYFWki+7U1cgvZtrbPltMz812nKx5M8LHbXsj/M+84VM2p5Wx2Y8/qdXr/zgTj3+95zMUCnQNxNBeRu6Jevm2pFd0MRzveuXA63dLBdVEsJ3zdiQeN94E0HhaJgMkSPzGrx8ZWKNX/oPXE8kS8mc6d1XNYYH/Ty+laMK82w4d7Sgje8jMHNaDFX3AmVw6Qiy+6WI9fl1DcPrJrJw22WW9Ul31QasWLnLcq2OflFfxxSBs/jx6zbqq3FDkhQtkvDi2H6FD8bUT3uzzuO6Foa7pG5JZMp2+u1x9aLPQqkHXhSWulTZDY6JJCiLtqhqJe56/F1f7n+FJerxHoW60tr3+3sJ9GVEK5zfjmg1v7qHLJyijyvp4sHSO9j1O8m5tumZ8JOtZ7eu31nbCm3Z8cu3R9CwzVN+UCaf+nykr288jLLnz2eFwIIIIDA8AS8r8/nA05F+/67fJDx/dIWNI2Dcz5f8I/8vurG79t6gNwNPRjCV9xcn7sZrt9M7yW1rPPLgbr8it+StKz87SkPL47y6btqXHEako5b6W+ediPdLofEjSxm1Xl87L371K66moFOsFVWA80EC0cAgU0TcJjALebi93jHIbRkn5vEmIQuS3U8wuM0sPm9f1J9s5OT4R5dWbNHwbg9O3eG3Tp38DmFnmSgoFxDreRat72J83sRXo5f3pHmZnpxQPGnSEd5Xc3Rq+18mwtvztHsGWxgrrmazh6nOb89Nh+U3fWlUot6L+kgeket4tKvXmovp/7U6qoRbilgd2txPjVN9wL0cj5jXl1y/V7FAasZrNDkrSLsN2NrXPOQtxp+a5aVfVpnzH/7kScub6MLXbmKwX5KYom6u157LuINaIuBTb9mIstl1j5ncyJ6egi09FJf63OPGUqDO24MPCb87Ub+3Jl1DSkNLPWWB5c0a9ZbIKVOSyw6tD72zHS3SZrB+h5zNffLat0wpZZxs/GR5bqdQXHw9RNZ/QQl/7I1rYNtHK5l5ZM5r7Pberutbq3TdZuXYQgggAACdy/g44nf/v7vfXLel6u368thNr89fX57/viQCJ03+Af8JZ0v+BzCl6/6mOP+m7dvrQjOaZaur/ydKKer60SjMLD9/GIU0kwaEUAAgT4CxZ0ti2OF4g8+N9D06WGg+pFel6T6PCE3GvL0DtxNaZifyjqhBwNNTSiIp3k8zj/qx34dfMrnBOX+Pslpjlrv9M0Z23qGFphzOnzQy6/cnw+E7Z89XXmYD7wLalnnVurl11phvKw47VpnKK9kk/pzXsuLi3nMGS2PoB8BBBCokMCquyntW32wa3+tdq6Qgv3a32tCH1wnpxR805Jyq9x4ANZCvevu9vb68m49p7H9s6fJrzwuf6aLAAIIILB1Ank/nbt5v94rBZ6u/e1p8/y5m4fFaXU8ua0f9H38KY/3NOWXg3Ll8XHe8gT0I4AAAggMRcDHhvI5gBORjhe5sVARqFMAzgG7+E8TeBq/4rT5g3bu8XP78Djlxv7kRW9s7jTXUANz60l4+UDp+fLntrhcXORqMHne8nR5mBdQHh4XOKA/Xmd5vV5N++cBrZrFIoAAAgMXaA/MrXf/1r4v9uf8duLbx6+Wofb1r3f+1ZbPeAQQQACB9QsMat9cXm6v/nJqPU1+5+Hl+fIwuggggAACWyuQv//nbl57vrzVn9t//I/f84sv++X+PK+n7zo8T7CObrGadczROenIBObKSU+3b9UQYaYDZvmw6WhpeerO/lRoeR5N3Jw+9TQ/atZyf+eSNj7Ea88pKC+l27DyePoRQACBURHw/jPvQ+M+TzvfzsvYV+bGv3Cl/7Fv5cjiU3m5XSdgIAIIIIAAAj0Eun3XzsNy17OW+3ssisEIIIAAAlso0H4OkM8z3G0PzDlZq8WFtjDpq65qJANzKw6UGymB0jzpXhKpSHPBltW6DSuP32h/zkPubnQ5zIcAAghUVaB9/xn3d6X9b9d0a6beIbk0R/tyuy6HgQgggAACCKxRoNv38W7D1rg4JkMAAQQQGJBA+3lA++cBrXbgix3JwNzAVYa0Ar4ADAme1SKAwKYL1OUguekwLBABBBBAYHgCvb5s9zho9Zp8eBlgzQgggMB4C/TYXY88SuPChQscc0a+GMkAAggggAACCCCAAAIIdBVY79lOXc/8uuIwEAEEEEBg2AIE5oZdAqwfAQQQQAABBBBAAAEEBiew3sDc4FLCkhFAAAEE7kagpj+cEJi7m0rBvAgggAACCCCAAAIIIFBtAQJz1S4fUocAAgisR6CGwTkCc+upAEyLAAIIILB2gc0+EarhQXjtmEyJAAIIIHDXAms5LnGsuWtmFoAAAghsukC//XcN9tsE5ja9xrBABBBAAIEo0O8AuhGiGhx0N5Jt5kEAAQQQ2ESBfscmjjObCM2iEEAAgU0W6LX/rsG+m8DcJtcVFocAAgggUAj0OnhuFKgGB92NZp35EEAAAQQQQAABBBAYe4Fu5xc1OEcgMDf2NRsABBBAAAEEEEAAAQQQQAABBBBAAIFhCBCYG4Y660QAAQQQQAABBBBAAAEEEEAAAQQQGHuBxvnz57s1Bhx7GAAQQAABBBBAAAEEEEAAAQQQQAABBBAYpACBuUHqrmHZREXXgMQkCCAwUgI1uM3DSHmTWAQQQAABBBBAAAEEEBhdAQJzQy47AnNDLgBWjwACAxEgODcQVhaKAAIIIIAAAggggAACNRMgMDfkAiUwN+QCYPUIIDAQAQJzA2FloQgggAACCCCAAAIIIFAzAQJzQy5QAnNDLgBWjwACAxEgMDcQVhaKAAIIIIAAAggggAACNRMgMDfkAiUwN+QCYPUIIDAQAQJzA2FloQgggAACCCCAAAIIIFAzAQJzQy5QAnNDLgBWjwACAxEgMDcQVhaKAAIIIIAAAggggAACNRNonDt3jtjQEAsV/CHis2oEEBiYAIG5gdGyYAQQQAABBBBAAAEEEKiRAIG5IRcmgbkhFwCrRwCBgQgQmBsIKwtFAAEEEEAAAQQQQACBmgkQmBtygRKYG3IBsHoEENh0AYJym07KAhFAAAEEEEAAAQQQQKCmAgTmalqwZAsBBBBAAAEEEEAAAQQQQAABBBBAoNoCBOaqXT6kDgEEEEAAAQQQQAABBBBAAAEEEECgpgIE5mpasGQLAQQQQAABBBBAAAEEEEAAAQQQQKDaAgTmql0+pA4BBBBAAAEEEEAAAQQQQAABBBBAoKYCBOZqWrBkCwEEEEAAAQQQQAABBBBAAAEEEECg2gIE5qpdPqQOAQQQQAABBBBAAAEEEEAAAQQQQKCmAo2zZ88u1zRvZAsBBBBAAAEEEEAAAQQQQAABBBBAAIHKChCYq2zRkDAEEEAAAQQQQAABBBBAAAEEEEAAgToLEJirc+mSNwQQQAABBBBAAAEEEEAAAQQQQACBygoQmKts0ZAwBBBAAAEEEEAAAQQQQAABBBBAAIE6CxCYq3PpkjcEEEAAAQQQQAABBBBAAAEEEEAAgcoKEJirbNGQMAQQQAABBBBAAAEEEEAAAQQQQACBOgsQmKtz6ZI3BBBAAAEEEEAAAQQQQAABBBBAAIHKChCYq2zRkDAEEEAAAQQQQAABBBBAAAEEEEAAgToLEJirc+mSNwQQQAABBBBAAAEEEEAAAQQQQACBygo0zpw5s1zZ1JEwBBBAAAEEEEAAAQQQQAABBBBAAAEEaipAYK6mBUu2EEAAAQQQQAABBBBAAAEEEEAAAQSqLUBgrtrlQ+oQQAABBBBAAAEEEEAAAQQQQAABBGoqQGCupgVLthBAAAEEEEAAAQQQQAABBBBAAAEEqi1AYK7a5UPqEEAAAQQQQAABBBBAAAEEEEAAAQRqKkBgrqYFS7YQQAABBBBAAAEEEEAAAQQQQAABBKot0Dh9+jRPZa12GZE6BBBAAAEEEEAAAQQQQAABBBBAAIEaChCYq2GhkiUEEEAAAQQQQAABBBBAAAEEEEAAgeoLEJirfhmRQgQQQAABBBBAAAEEEEAAAQQQQACBGgoQmKthoZIlBBBAAAEEEEAAAQQQQAABBBBAAIHqCxCYq34ZkUIEEEAAAQQQQAABBBBAAAEEEEAAgRoKEJirYaGSJQQQQAABBBBAAAEEEEAAAQQQQACB6gsQmKt+GZFCBBBAAAEEEEAAAQQQQAABBBBAAIEaChCYq2GhkiUEEEAAAQQQQAABBBB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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `inputs` keys\n",
+    "* `input_ids` vocab dictionary lookups of `raw_inputs` \n",
+    "* `attention_mask` indicates where padding has been applied with 0s\n",
+    "  \n",
+    "> # Stage 2: Model\n",
+    "> ---\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "API used will determine what portion of of model is instantiated\n",
+    "> ### `AutoModel` outputs model body only → without classification head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73695575812147c3920bce6d2efc39e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 16, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoModel\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "model = AutoModel.from_pretrained(checkpoint)\n",
+    "# download & cache config of model & pretrained weights\n",
+    "# AutoModel API only instatiates body of model\n",
+    "# --> part of model that is left once pre-training head is removed\n",
+    "# --> Output will be high-dimensional representation of sentences passed that is not directly usable for downstream tasks\n",
+    "outputs = model(**inputs)\n",
+    "print(outputs.last_hidden_state.shape)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `AutoModelForSequenceClassification` outputs model with classification head\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "* There is an `AutoClass` for each common NLP task\n",
+    "\n",
+    "* outputs are not Probabilities yet, don't sum to 1\n",
+    "  * model outputs Logits! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[-1.5607,  1.6123],\n",
+      "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoModelForSequenceClassification\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
+    "outputs = model(**inputs)\n",
+    "print(outputs.logits)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> # Stage 3: Postprocessing\n",
+    "> ---\n",
+    "* To convert Logits into Probabilities a SoftMax layers must be applied\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[4.0195e-02, 9.5981e-01],\n",
+      "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
+    "### Last step is to know which positions correspond to which labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.config.id2label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
    ]
   },
   {
@@ -94,7 +426,8 @@
     "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
     "    \"I hate this so much!\",\n",
     "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
+    "inputs = tokenizer(raw_inputs, padding=True,\n",
+    "                   truncation=True, return_tensors=\"pt\")\n",
     "print(inputs)"
    ]
   },
@@ -234,6 +567,23 @@
   "colab": {
    "name": "Behind the pipeline (PyTorch)",
    "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/course/en/chapter2/section2_tf.ipynb b/course/en/chapter2/section2_tf.ipynb
deleted file mode 100644
index 98fc2ab1..00000000
--- a/course/en/chapter2/section2_tf.ipynb
+++ /dev/null
@@ -1,245 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Behind the pipeline (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'label': 'POSITIVE', 'score': 0.9598047137260437},\n",
-       " {'label': 'NEGATIVE', 'score': 0.9994558095932007}]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "classifier = pipeline(\"sentiment-analysis\")\n",
-    "classifier(\n",
-    "    [\n",
-    "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "        \"I hate this so much!\",\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{\n",
-       "    'input_ids': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,  2026,  2878,  2166,  1012,   102],\n",
-       "            [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,     0,     0,     0,     0,     0,     0]\n",
-       "        ], dtype=int32)>, \n",
-       "    'attention_mask': <tf.Tensor: shape=(2, 16), dtype=int32, numpy=\n",
-       "        array([\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
-       "            [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-       "        ], dtype=int32)>\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_inputs = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"I hate this so much!\",\n",
-    "]\n",
-    "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "print(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModel\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModel.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 16, 768)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs = model(inputs)\n",
-    "print(outputs.last_hidden_state.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "outputs = model(inputs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 2)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
-       "    array([[-1.5606991,  1.6122842],\n",
-       "           [ 4.169231 , -3.3464472]], dtype=float32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[4.01951671e-02 9.59804833e-01]\n",
-       " [9.9945587e-01 5.4418424e-04]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "predictions = tf.math.softmax(outputs.logits, axis=-1)\n",
-    "print(predictions)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'NEGATIVE', 1: 'POSITIVE'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.config.id2label"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Behind the pipeline (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter2/section3_pt.ipynb b/course/en/chapter2/section3_pt.ipynb
deleted file mode 100644
index e3bdde2d..00000000
--- a/course/en/chapter2/section3_pt.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Models (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = BertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, BertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = BertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertModel\n",
-    "\n",
-    "model = BertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "model_inputs = torch.tensor(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Models (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter2/section3_pt_models.ipynb b/course/en/chapter2/section3_pt_models.ipynb
new file mode 100644
index 00000000..488675f7
--- /dev/null
+++ b/course/en/chapter2/section3_pt_models.ipynb
@@ -0,0 +1,294 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Models (PyTorch)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "[![Video Title](https://img.youtube.com/vi/AhChOFRegn4/0.jpg)](https://www.youtube.com/watch?v=AhChOFRegn4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install torch datasets evaluate transformers[sentencepiece] -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ## `AutoModel` and `AutoConfig` automatically detect class of checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'transformers.models.bert.configuration_bert.BertConfig'>\n",
+      "<class 'transformers.models.gpt2.configuration_gpt2.GPT2Config'>\n",
+      "<class 'transformers.models.bart.configuration_bart.BartConfig'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoConfig\n",
+    "bert_config = AutoConfig.from_pretrained(\"bert-base-cased\")\n",
+    "print(type(bert_config))\n",
+    "gpt_config = AutoConfig.from_pretrained(\"gpt2\")\n",
+    "print(type(gpt_config))\n",
+    "bart_config = AutoConfig.from_pretrained(\"facebook/bart-base\")\n",
+    "print(type(bart_config))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import BertConfig, BertModel\n",
+    "\n",
+    "# Building the config\n",
+    "config = BertConfig()\n",
+    "\n",
+    "# Building the model from the config\n",
+    "model = BertModel(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BertConfig {\n",
+      "  \"attention_probs_dropout_prob\": 0.1,\n",
+      "  \"classifier_dropout\": null,\n",
+      "  \"hidden_act\": \"gelu\",\n",
+      "  \"hidden_dropout_prob\": 0.1,\n",
+      "  \"hidden_size\": 768,\n",
+      "  \"initializer_range\": 0.02,\n",
+      "  \"intermediate_size\": 3072,\n",
+      "  \"layer_norm_eps\": 1e-12,\n",
+      "  \"max_position_embeddings\": 512,\n",
+      "  \"model_type\": \"bert\",\n",
+      "  \"num_attention_heads\": 12,\n",
+      "  \"num_hidden_layers\": 12,\n",
+      "  \"pad_token_id\": 0,\n",
+      "  \"position_embedding_type\": \"absolute\",\n",
+      "  \"transformers_version\": \"4.44.2\",\n",
+      "  \"type_vocab_size\": 2,\n",
+      "  \"use_cache\": true,\n",
+      "  \"vocab_size\": 30522\n",
+      "}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import BertConfig, BertModel\n",
+    "\n",
+    "config = BertConfig()\n",
+    "model = BertModel(config)\n",
+    "\n",
+    "# Model is randomly initialized!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import BertModel\n",
+    "\n",
+    "# can override config but model instantiated is randomly initialised\n",
+    "model = BertModel.from_pretrained(\"bert-base-cased\", num_hidden_layers=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.mkdir('models')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save_pretrained(\"models\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_sequences = [\n",
+    "    [101, 7592, 999, 102],\n",
+    "    [101, 4658, 1012, 102],\n",
+    "    [101, 3835, 999, 102],\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "model_inputs = torch.tensor(encoded_sequences)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = model(model_inputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "odict_keys(['last_hidden_state', 'pooler_output'])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Models (PyTorch)",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/course/en/chapter2/section3_tf.ipynb b/course/en/chapter2/section3_tf.ipynb
deleted file mode 100644
index d1e1bc85..00000000
--- a/course/en/chapter2/section3_tf.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Models (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "# Building the config\n",
-    "config = BertConfig()\n",
-    "\n",
-    "# Building the model from the config\n",
-    "model = TFBertModel(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BertConfig {\n",
-       "  [...]\n",
-       "  \"hidden_size\": 768,\n",
-       "  \"intermediate_size\": 3072,\n",
-       "  \"max_position_embeddings\": 512,\n",
-       "  \"num_attention_heads\": 12,\n",
-       "  \"num_hidden_layers\": 12,\n",
-       "  [...]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertConfig, TFBertModel\n",
-    "\n",
-    "config = BertConfig()\n",
-    "model = TFBertModel(config)\n",
-    "\n",
-    "# Model is randomly initialized!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFBertModel\n",
-    "\n",
-    "model = TFBertModel.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "encoded_sequences = [\n",
-    "    [101, 7592, 999, 102],\n",
-    "    [101, 4658, 1012, 102],\n",
-    "    [101, 3835, 999, 102],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model_inputs = tf.constant(encoded_sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = model(model_inputs)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Models (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter2/section4_tf.ipynb b/course/en/chapter2/section4_tf.ipynb
deleted file mode 100644
index 15343d71..00000000
--- a/course/en/chapter2/section4_tf.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tokenizers (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
-    "print(tokenized_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import BertTokenizer\n",
-    "\n",
-    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'input_ids': [101, 7993, 170, 11303, 1200, 2443, 1110, 3014, 102],\n",
-       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
-       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer(\"Using a Transformer network is simple\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
-    "\n",
-    "sequence = \"Using a Transformer network is simple\"\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "\n",
-    "print(tokens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7993, 170, 11303, 1200, 2443, 1110, 3014]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Using a Transformer network is simple'"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
-    "print(decoded_string)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Tokenizers (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter2/section5_tf.ipynb b/course/en/chapter2/section5_tf.ipynb
deleted file mode 100644
index c6608355..00000000
--- a/course/en/chapter2/section5_tf.ipynb
+++ /dev/null
@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Handling multiple sequences (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "InvalidArgumentError: Input to reshape is a tensor with 14 values, but the requested shape has 196 [Op:Reshape]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = tf.constant(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<tf.Tensor: shape=(1, 16), dtype=int32, numpy=\n",
-       "array([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662,\n",
-       "        12172,  2607,  2026,  2878,  2166,  1012,   102]], dtype=int32)>"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"tf\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: tf.Tensor(\n",
-       "[[ 1045  1005  2310  2042  3403  2005  1037 17662 12172  2607  2026  2878\n",
-       "   2166  1012]], shape=(1, 14), dtype=int32)\n",
-       "Logits: tf.Tensor([[-2.7276208  2.8789377]], shape=(1, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = tf.constant([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor([[ 1.5693678 -1.3894581]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor([[ 0.5803005  -0.41252428]], shape=(1, 2), dtype=float32)\n",
-       "tf.Tensor(\n",
-       "[[ 1.5693681 -1.3894582]\n",
-       " [ 1.3373486 -1.2163193]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(tf.constant(sequence1_ids)).logits)\n",
-    "print(model(tf.constant(sequence2_ids)).logits)\n",
-    "print(model(tf.constant(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tf.Tensor(\n",
-       "[[ 1.5693681  -1.3894582 ]\n",
-       " [ 0.5803021  -0.41252586]], shape=(2, 2), dtype=float32)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(tf.constant(batched_ids), attention_mask=tf.constant(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Handling multiple sequences (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter2/section6_tf.ipynb b/course/en/chapter2/section6_tf.ipynb
deleted file mode 100644
index 8448455d..00000000
--- a/course/en/chapter2/section6_tf.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Putting it all together (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Will pad the sequences up to the maximum sequence length\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Will pad the sequences up to the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Will pad the sequences up to the specified max length\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the specified max length\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Putting it all together (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From a9869c1bbc22f6640cb20dd508fb22556e29fb89 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Tue, 3 Sep 2024 12:27:46 +0000
Subject: [PATCH 06/20] renamed to include content in name

---
 ...{section4_pt.ipynb => section4_pt_tokenizers.ipynb} | 10 ++++++++++
 1 file changed, 10 insertions(+)
 rename course/en/chapter2/{section4_pt.ipynb => section4_pt_tokenizers.ipynb} (93%)

diff --git a/course/en/chapter2/section4_pt.ipynb b/course/en/chapter2/section4_pt_tokenizers.ipynb
similarity index 93%
rename from course/en/chapter2/section4_pt.ipynb
rename to course/en/chapter2/section4_pt_tokenizers.ipynb
index fadf2b34..c1da1d76 100644
--- a/course/en/chapter2/section4_pt.ipynb
+++ b/course/en/chapter2/section4_pt_tokenizers.ipynb
@@ -7,6 +7,13 @@
     "# Tokenizers (PyTorch)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Video Title](https://img.youtube.com/vi/VFp38yj8h3A/0.jpg)](https://www.youtube.com/watch?v=VFp38yj8h3A)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -172,6 +179,9 @@
   "colab": {
    "name": "Tokenizers (PyTorch)",
    "provenance": []
+  },
+  "language_info": {
+   "name": "python"
   }
  },
  "nbformat": 4,

From 04223047069764bb3a8803d1c4a0299beebe5c82 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Tue, 3 Sep 2024 14:15:04 +0000
Subject: [PATCH 07/20] ch2 sec4

---
 .../en/chapter2/section4_pt_tokenizers.ipynb  | 186 ++++++++++++++++--
 1 file changed, 168 insertions(+), 18 deletions(-)

diff --git a/course/en/chapter2/section4_pt_tokenizers.ipynb b/course/en/chapter2/section4_pt_tokenizers.ipynb
index c1da1d76..6e8bc92f 100644
--- a/course/en/chapter2/section4_pt_tokenizers.ipynb
+++ b/course/en/chapter2/section4_pt_tokenizers.ipynb
@@ -7,6 +7,22 @@
     "# Tokenizers (PyTorch)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install datasets evaluate transformers[sentencepiece]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -15,35 +31,39 @@
    ]
   },
   {
+   "attachments": {
+    "image.png": {
+     "image/png": 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+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+    "We'll look at 3 tokenizer algo's\n",
+    "\n",
+    "![image.png](attachment:image.png)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
+    "> ### Word-base tokenizers\n",
+    "\n",
+    "[![Video Title](https://img.youtube.com/vi/nhJxYji1aho/0.jpg)](https://www.youtube.com/watch?v=nhJxYji1aho)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['Jim', 'Henson', 'was', 'a', 'puppeteer']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Jim', 'Henson', 'was', 'a', 'puppeteer']\n"
+     ]
     }
    ],
    "source": [
@@ -51,14 +71,57 @@
     "print(tokenized_text)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### Character-based tokenizers\n",
+    "\n",
+    "[![Video Title](https://img.youtube.com/vi/ssLq_EK2jLE/0.jpg)](https://www.youtube.com/watch?v=ssLq_EK2jLE)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* character-based vocabs more complete than word-based\n",
+    "* characters do not hold as much semantic info as words\n",
+    "* tokens for character-based processed sequences much larger than word-based --> impacts size on *context* model can carry around (limited context-window)\n",
+    "* Not perfect but solves a lot of the word-based tokenizer issues --> consider for new problems"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from transformers import BertTokenizer\n",
     "\n",
+    "# using specific tokenizer object\n",
     "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
    ]
   },
@@ -70,6 +133,7 @@
    "source": [
     "from transformers import AutoTokenizer\n",
     "\n",
+    "# auto-detect tokenizer object\n",
     "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
    ]
   },
@@ -95,13 +159,25 @@
     "tokenizer(\"Using a Transformer network is simple\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "if 'models' not in os.listdir():\n",
+    "    os.mkdir('models')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tokenizer.save_pretrained(\"directory_on_my_computer\")"
+    "tokenizer.save_pretrained(\"models\")"
    ]
   },
   {
@@ -173,6 +249,66 @@
     "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
     "print(decoded_string)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### Sub-word tokenization\n",
+    "\n",
+    "* Generlly used by models achieving best-in-class English performance!\n",
+    "\n",
+    "\n",
+    "[![Video Title](https://img.youtube.com/vi/zHvTiHr506c/0.jpg)](https://www.youtube.com/watch?v=zHvTiHr506c)"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    },
+    "image.png": {
+     "image/png": 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+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "> ### Goal: is to find middle-ground\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -180,8 +316,22 @@
    "name": "Tokenizers (PyTorch)",
    "provenance": []
   },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

From 8aed5d8c43d7df0e81a54ef8e401264372b176b7 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Wed, 4 Sep 2024 07:12:44 +0000
Subject: [PATCH 08/20] =?UTF-8?q?ch2=20sec4=20=E2=9C=85?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../en/chapter2/section4_pt_tokenizers.ipynb  | 130 +++++++-
 course/en/chapter2/section5_pt.ipynb          | 228 -------------
 course/en/chapter2/section5_pt_batching.ipynb | 310 ++++++++++++++++++
 3 files changed, 429 insertions(+), 239 deletions(-)
 delete mode 100644 course/en/chapter2/section5_pt.ipynb
 create mode 100644 course/en/chapter2/section5_pt_batching.ipynb

diff --git a/course/en/chapter2/section4_pt_tokenizers.ipynb b/course/en/chapter2/section4_pt_tokenizers.ipynb
index 6e8bc92f..960e4f65 100644
--- a/course/en/chapter2/section4_pt_tokenizers.ipynb
+++ b/course/en/chapter2/section4_pt_tokenizers.ipynb
@@ -182,23 +182,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['Using', 'a', 'transform', '##er', 'network', 'is', 'simple']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Using', 'a', 'Trans', '##former', 'network', 'is', 'simple']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
     }
    ],
    "source": [
-    "from transformers import AutoTokenizer\n",
+    "from transformers import AutoModel, AutoTokenizer\n",
     "\n",
+    "# model = AutoModel.from_pretrained(\"bert-base-cased\")\n",
     "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
     "\n",
     "sequence = \"Using a Transformer network is simple\"\n",
@@ -308,7 +322,101 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "> ### Tokenization Pipeline\n",
+    "\n",
+    "[![Video Title](https://img.youtube.com/vi/Yffk5aydLzg/0.jpg)](https://www.youtube.com/watch?v=Yffk5aydLzg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Encoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> #### Tokenization: `sequence` to `token`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Using', 'a', 'Trans', '##former', 'network', 'is', 'simple']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
+    "\n",
+    "sequence = \"Using a Transformer network is simple\"\n",
+    "tokens = tokenizer.tokenize(sequence)\n",
+    "\n",
+    "print(tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> #### From `tokens` to `Input_IDs`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[7993, 170, 13809, 23763, 2443, 1110, 3014]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
+    "print(ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Decoding\n",
+    "\n",
+    "Decoding is going the other way around: from vocabulary indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a transformer network is simple\n"
+     ]
+    }
+   ],
+   "source": [
+    "decoded_string = tokenizer.decode([7993, 170, 11303, 1200, 2443, 1110, 3014])\n",
+    "print(decoded_string)"
+   ]
   }
  ],
  "metadata": {
diff --git a/course/en/chapter2/section5_pt.ipynb b/course/en/chapter2/section5_pt.ipynb
deleted file mode 100644
index 40b24d22..00000000
--- a/course/en/chapter2/section5_pt.ipynb
+++ /dev/null
@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Handling multiple sequences (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "input_ids = torch.tensor(ids)\n",
-    "# This line will fail.\n",
-    "model(input_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
-       "          2607,  2026,  2878,  2166,  1012,   102]])"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
-    "print(tokenized_inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Input IDs: [[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012]]\n",
-       "Logits: [[-2.7276,  2.8789]]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "\n",
-    "input_ids = torch.tensor([ids])\n",
-    "print(\"Input IDs:\", input_ids)\n",
-    "\n",
-    "output = model(input_ids)\n",
-    "print(\"Logits:\", output.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200]\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padding_id = 100\n",
-    "\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, padding_id],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n",
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence1_ids = [[200, 200, 200]]\n",
-    "sequence2_ids = [[200, 200]]\n",
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 1.5694, -1.3895],\n",
-       "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batched_ids = [\n",
-    "    [200, 200, 200],\n",
-    "    [200, 200, tokenizer.pad_token_id],\n",
-    "]\n",
-    "\n",
-    "attention_mask = [\n",
-    "    [1, 1, 1],\n",
-    "    [1, 1, 0],\n",
-    "]\n",
-    "\n",
-    "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
-    "print(outputs.logits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = sequence[:max_sequence_length]"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Handling multiple sequences (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter2/section5_pt_batching.ipynb b/course/en/chapter2/section5_pt_batching.ipynb
new file mode 100644
index 00000000..2dc713a8
--- /dev/null
+++ b/course/en/chapter2/section5_pt_batching.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Handling multiple sequences (PyTorch)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Video Title](https://img.youtube.com/vi/M6adb1j2jPI/0.jpg)](https://www.youtube.com/watch?v=M6adb1j2jPI)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,\n",
+       "         2026,  2878,  2166,  1012])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+    "\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
+    "\n",
+    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
+    "\n",
+    "tokens = tokenizer.tokenize(sequence)\n",
+    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
+    "input_ids = torch.tensor(ids)\n",
+    "# This line will fail.\n",
+    "# model(input_ids)\n",
+    "input_ids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`input_ids` are not all the same length → cannot pass into model → `model(input_ids)` will fail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
+      "          2607,  2026,  2878,  2166,  1012,   102]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
+    "print(tokenized_inputs[\"input_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Typically only truncate sentences if longer than what model will allow. We must pad with the tokenizer specified by the `tokenizer` padding id used during pre-training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+    "\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "tokenizer.pad_token_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input IDs: tensor([[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,\n",
+      "          2026,  2878,  2166,  1012]])\n",
+      "Logits: tensor([[-2.7276,  2.8789]], grad_fn=<AddmmBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
+    "\n",
+    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
+    "\n",
+    "tokens = tokenizer.tokenize(sequence)\n",
+    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
+    "\n",
+    "input_ids = torch.tensor([ids])\n",
+    "print(\"Input IDs:\", input_ids)\n",
+    "\n",
+    "output = model(input_ids)\n",
+    "print(\"Logits:\", output.logits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batched_ids = [\n",
+    "    [200, 200, 200],\n",
+    "    [200, 200]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "padding_id = 100\n",
+    "\n",
+    "batched_ids = [\n",
+    "    [200, 200, 200],\n",
+    "    [200, 200, padding_id],\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward0>)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward0>)\n",
+      "tensor([[ 1.5694, -1.3895],\n",
+      "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>)\n",
+      "tensor([[ 1.5694, -1.3895],\n",
+      "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
+    "\n",
+    "sequence1_ids = [[200, 200, 200]]\n",
+    "sequence2_ids = [[200, 200]]\n",
+    "batched_ids = [\n",
+    "    [200, 200, 200],\n",
+    "    [200, 200, tokenizer.pad_token_id],\n",
+    "]\n",
+    "\n",
+    "print(model(torch.tensor(sequence1_ids)).logits)\n",
+    "print(model(torch.tensor(sequence2_ids)).logits)\n",
+    "print(model(torch.tensor(batched_ids)).logits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 1.5694, -1.3895],\n",
+      "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "batched_ids = [\n",
+    "    [200, 200, 200],\n",
+    "    [200, 200, tokenizer.pad_token_id],\n",
+    "]\n",
+    "\n",
+    "# attention mask tells model which tokens to ignore\n",
+    "attention_mask = [\n",
+    "    [1, 1, 1],\n",
+    "    [1, 1, 0],\n",
+    "]\n",
+    "\n",
+    "outputs = model(torch.tensor(batched_ids),\n",
+    "                attention_mask=torch.tensor(attention_mask))\n",
+    "print(outputs.logits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequence = sequence[:max_sequence_length]"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Handling multiple sequences (PyTorch)",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 1a3ae1b82e14d10d000ad9b19e0880f271e51883 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Wed, 4 Sep 2024 07:31:18 +0000
Subject: [PATCH 09/20] =?UTF-8?q?ch2=20sec5=20=E2=9C=85?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 course/en/chapter2/section5_pt_batching.ipynb | 42 +++++++++----------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/course/en/chapter2/section5_pt_batching.ipynb b/course/en/chapter2/section5_pt_batching.ipynb
index 2dc713a8..f51c8071 100644
--- a/course/en/chapter2/section5_pt_batching.ipynb
+++ b/course/en/chapter2/section5_pt_batching.ipynb
@@ -197,32 +197,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward0>)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward0>)\n",
+      "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward0>) \n",
+      "\n",
+      "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward0>) \n",
+      "\n",
       "tensor([[ 1.5694, -1.3895],\n",
-      "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>)\n",
+      "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>) \n",
+      "\n",
       "tensor([[ 1.5694, -1.3895],\n",
-      "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>)\n"
+      "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>) \n",
+      "\n"
      ]
     }
    ],
@@ -236,9 +227,18 @@
     "    [200, 200, tokenizer.pad_token_id],\n",
     "]\n",
     "\n",
-    "print(model(torch.tensor(sequence1_ids)).logits)\n",
-    "print(model(torch.tensor(sequence2_ids)).logits)\n",
-    "print(model(torch.tensor(batched_ids)).logits)"
+    "print(model(torch.tensor(sequence1_ids)).logits, \"\\n\")\n",
+    "print(model(torch.tensor(sequence2_ids)).logits, \"\\n\")\n",
+    "print(model(torch.tensor(batched_ids)).logits, \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the padded output `batched_ids` is different `sequence2_ids` --> context aware encoding\n",
+    "\n",
+    "If we want the `model` to ignore the padding we must use an `attention_mask`"
    ]
   },
   {

From 77cad09c359425829af4d476725f8b6c7f4005c4 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Wed, 4 Sep 2024 07:35:50 +0000
Subject: [PATCH 10/20] ++

---
 course/en/chapter2/section5_pt_batching.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/course/en/chapter2/section5_pt_batching.ipynb b/course/en/chapter2/section5_pt_batching.ipynb
index f51c8071..08155693 100644
--- a/course/en/chapter2/section5_pt_batching.ipynb
+++ b/course/en/chapter2/section5_pt_batching.ipynb
@@ -238,7 +238,7 @@
    "source": [
     "Notice that the padded output `batched_ids` is different `sequence2_ids` --> context aware encoding\n",
     "\n",
-    "If we want the `model` to ignore the padding we must use an `attention_mask`"
+    "If we want the `model` to ignore the padding we must apply an `attention_mask`!"
    ]
   },
   {

From 4c5a3515f2d25688bf67dd66ffa19579c12ab23c Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Mon, 9 Sep 2024 15:05:45 +0200
Subject: [PATCH 11/20] =?UTF-8?q?ch2=20sec6=20=E2=9C=85?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .devcontainer/devcontainer.json               |  70 ++--
 course/en/chapter2/section5_pt_batching.ipynb |  93 ++++-
 course/en/chapter2/section6_pt.ipynb          | 192 ---------
 .../section6_pt_pulling_together.ipynb        | 388 ++++++++++++++++++
 requirements.txt                              | 128 ++++++
 5 files changed, 623 insertions(+), 248 deletions(-)
 delete mode 100644 course/en/chapter2/section6_pt.ipynb
 create mode 100644 course/en/chapter2/section6_pt_pulling_together.ipynb

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 0a7cecec..7f9ed7df 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -14,47 +14,35 @@
 			"customizations": {
 				"vscode": {
 					"extensions": [
-					// liveshare
-					"ms-vsliveshare.vsliveshare", // Enables real-time collaboration between developers
-					// intellicode
-					"VisualStudioExptTeam.intellicode-api-usage-examples", // Provides examples of how to use IntelliCode APIs
-					"VisualStudioExptTeam.vscodeintellicode-completions", // Provides AI-assisted code completions
-					"VisualStudioExptTeam.vscodeintellicode-insiders",	// Provides AI-assisted code completions (Insiders version)
-					// ms-vscode-remote
-					"ms-vscode-remote.remote-ssh", // Provides SSH remote development capabilities
-					"ms-vscode-remote.remote-containers", // Provides container-based remote development capabilities
-					// python
-					"ms-python.python", // Provides Python language support
-					"ms-python.vscode-pylance", // Provides advanced Python language support
-					"ms-python.black-formatter", // Provides code formatting using the Black formatter
-					// format
-					"streetsidesoftware.code-spell-checker", // Provides spell checking for code comments and strings
-					"yzhang.markdown-all-in-one", // Provides Markdown language support
-					"aaron-bond.better-comments", // Provides improved commenting functionality
-					"njpwerner.autodocstring", // Provides automatic documentation generation for Python functions
-					// source Control
-					"GitHub.vscode-pull-request-github", // Provides GitHub pull request integration
-					"GitHub.codespaces", // Provides GitHub Codespaces integration
-					"GitHub.vscode-github-actions", // Provides GitHub Actions integration
-					"eamodio.gitlens", // Provides Git repository management capabilities
-					// copilot
-					"GitHub.copilot", // Provides AI-assisted code completions and suggestions
-					"GitHub.copilot-chat",  // Provides access to the GitHub Copilot chat (must sign up at https://github.com/github-copilot/chat_waitlist_signup/join)
-					"GitHub.copilot-labs", // Provides access to experimental GitHub Copilot features
-					// ai tools
-					//"HuggingFace.huggingface-vscode", // Provides natural language processing capabilities
-					//"TabNine.tabnine-vscode", // Provides AI-assisted code completions
-					//"Blackboxapp.blackbox", // Provides secure and private machine learning capabilities
-					"Codeium.codeium", // Provides AI-assisted code completions and suggestions
-					// ms-toolai
-					"ms-toolsai.datawrangler", // Provides data cleaning, transformation, and reshaping capabilities
-					"ms-toolsai.jupyter-renderers", // Provides additional Jupyter Notebook cell renderers
-					"ms-toolsai.jupyter", // Provides Jupyter Notebook integration with VS Code
-					"ms-toolsai.vscode-jupyter-cell-tags", // Provides support for cell tags in Jupyter Notebooks
-					"ms-toolsai.vscode-jupyter-slideshow", // Provides support for creating and viewing Jupyter Notebook slideshows
-					"ms-toolsai.jupyter-keymap"	// Provides additional keyboard shortcuts for Jupyter Notebooks	
-
-						]
+                        "ms-vsliveshare.vsliveshare",
+                        "VisualStudioExptTeam.intellicode-api-usage-examples",
+                        "VisualStudioExptTeam.vscodeintellicode-completions",
+                        "VisualStudioExptTeam.vscodeintellicode-insiders",
+                        "ms-vscode-remote.remote-ssh",
+                        "ms-vscode-remote.remote-containers",
+                        "ms-python.python",
+                        "ms-python.vscode-pylance",
+                        "ms-python.black-formatter",
+                        "streetsidesoftware.code-spell-checker",
+                        "yzhang.markdown-all-in-one",
+                        "aaron-bond.better-comments",
+                        "njpwerner.autodocstring",
+                        "GitHub.vscode-pull-request-github",
+                        "GitHub.codespaces",
+                        "GitHub.vscode-github-actions",
+                        "eamodio.gitlens",
+                        //"GitHub.copilot",
+                        //"GitHub.copilot-chat",
+                        //"GitHub.copilot-labs",
+                        "Codeium.codeium",
+                        "ms-toolsai.datawrangler",
+                        "ms-toolsai.jupyter-renderers",
+                        "ms-toolsai.jupyter",
+                        "ms-toolsai.vscode-jupyter-cell-tags",
+                        "ms-toolsai.vscode-jupyter-slideshow",
+                        "ms-toolsai.jupyter-keymap",
+                      
+                    ]
 				}
 			}
     // Features to add to the dev container. More info: https://containers.dev/features.
diff --git a/course/en/chapter2/section5_pt_batching.ipynb b/course/en/chapter2/section5_pt_batching.ipynb
index 08155693..e43282ce 100644
--- a/course/en/chapter2/section5_pt_batching.ipynb
+++ b/course/en/chapter2/section5_pt_batching.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Handling multiple sequences (PyTorch)"
+    "# [Handling multiple sequences (PyTorch)](https://huggingface.co/learn/nlp-course/chapter2/5?fw=pt)"
    ]
   },
   {
@@ -32,13 +32,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
       "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
       "  warnings.warn(\n"
      ]
@@ -50,7 +52,7 @@
        "         2026,  2878,  2166,  1012])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -108,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -125,7 +127,7 @@
        "0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,9 +199,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -210,9 +219,6 @@
       "\n",
       "tensor([[ 1.5694, -1.3895],\n",
       "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>) \n",
-      "\n",
-      "tensor([[ 1.5694, -1.3895],\n",
-      "        [ 1.3373, -1.2163]], grad_fn=<AddmmBackward0>) \n",
       "\n"
      ]
     }
@@ -238,12 +244,14 @@
    "source": [
     "Notice that the padded output `batched_ids` is different `sequence2_ids` --> context aware encoding\n",
     "\n",
-    "If we want the `model` to ignore the padding we must apply an `attention_mask`!"
+    "If we want the `model` to ignore the padding we must apply an `attention_mask`!\n",
+    "\n",
+    "--> all done automatically by `tokenizer`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -272,14 +280,69 @@
     "print(outputs.logits)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### Try it out!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sequence = sequence[:max_sequence_length]"
+    "test_1 = \"I’ve been waiting for a HuggingFace course my whole life.\"\n",
+    "test_2 = \"I hate this so much!\""
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### Longer sequences\n",
+    "\n",
+    "Most models handle sequences of up to 512 or 1024 tokens, and will crash when asked to process longer sequences. There are two solutions to this problem:\n",
+    "\n",
+    "* Use a model with a longer supported sequence length.\n",
+    "\n",
+    "\n",
+    "\n",
+    "    * [Lonformer](https://huggingface.co/docs/transformers/model_doc/longformer)\n",
+    "    * [LED](https://huggingface.co/learn/nlp-course/chapter2/5?fw=pt#:~:text=and%20another%20is-,LED,-.%20If%20you%E2%80%99re%20working)\n",
+    "  \n",
+    "* Truncate your sequences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"I've been waiting for a \""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_sequence_length = 24\n",
+    "sequence = sequence[:max_sequence_length]\n",
+    "sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/course/en/chapter2/section6_pt.ipynb b/course/en/chapter2/section6_pt.ipynb
deleted file mode 100644
index 94af787c..00000000
--- a/course/en/chapter2/section6_pt.ipynb
+++ /dev/null
@@ -1,192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Putting it all together (PyTorch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "model_inputs = tokenizer(sequences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Will pad the sequences up to the maximum sequence length\n",
-    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
-    "\n",
-    "# Will pad the sequences up to the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
-    "\n",
-    "# Will pad the sequences up to the specified max length\n",
-    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the model max length\n",
-    "# (512 for BERT or DistilBERT)\n",
-    "model_inputs = tokenizer(sequences, truncation=True)\n",
-    "\n",
-    "# Will truncate the sequences that are longer than the specified max length\n",
-    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "# Returns PyTorch tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
-    "\n",
-    "# Returns TensorFlow tensors\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
-    "\n",
-    "# Returns NumPy arrays\n",
-    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
-       "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
-    "\n",
-    "model_inputs = tokenizer(sequence)\n",
-    "print(model_inputs[\"input_ids\"])\n",
-    "\n",
-    "tokens = tokenizer.tokenize(sequence)\n",
-    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
-    "print(ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[CLS] i've been waiting for a huggingface course my whole life. [SEP]\"\n",
-       "\"i've been waiting for a huggingface course my whole life.\""
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
-    "print(tokenizer.decode(ids))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
-    "\n",
-    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
-    "\n",
-    "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
-    "output = model(**tokens)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Putting it all together (PyTorch)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter2/section6_pt_pulling_together.ipynb b/course/en/chapter2/section6_pt_pulling_together.ipynb
new file mode 100644
index 00000000..12034e98
--- /dev/null
+++ b/course/en/chapter2/section6_pt_pulling_together.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# [Putting it all together (PyTorch)](https://huggingface.co/learn/nlp-course/chapter2/6?fw=pt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import subprocess\n",
+    "subprocess.check_call([\"pip\", \"install\", \"--upgrade\", \"jupyter\", \"-q\"])\n",
+    "subprocess.check_call([\"pip\", \"install\", \"--upgrade\", \"ipywidgets\", \"-q\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "\n",
+    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
+    "\n",
+    "model_inputs = tokenizer(sequence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Other models that accept additional inputs will also have those output by the tokenizer object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### 1. Single seqeuence tokenization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': [101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
+    "\n",
+    "model_inputs = tokenizer(sequence)\n",
+    "model_inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### 2. Multi-seqeuence tokenization\n",
+    "\n",
+    "* Creates list of tokenized sequences --> `[[]]` --> additional dimension "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], [101, 2061, 2031, 1045, 999, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequences = [\n",
+    "    \"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
+    "\n",
+    "model_inputs = tokenizer(sequences)\n",
+    "model_inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### 3. Pad according to different objectives"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], [101, 2061, 2031, 1045, 999, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} \n",
+      "\n",
+      "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2061, 2031, 1045, 999, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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+      "\n",
+      "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], [101, 2061, 2031, 1045, 999, 102, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 0]]} \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Will pad the sequences up to the maximum sequence length\n",
+    "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
+    "print(model_inputs, '\\n')\n",
+    "# Will pad the sequences up to the model max length\n",
+    "# (512 for BERT or DistilBERT)\n",
+    "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
+    "print(model_inputs, '\\n')\n",
+    "# Will pad the sequences up to the specified max length\n",
+    "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)\n",
+    "print(model_inputs, '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### 4. Truncate sentences\n",
+    "* Automatically uses model `max_length` or can explicitly state `max_length`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], [101, 2061, 2031, 1045, 999, 102, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 0]]} \n",
+      "\n",
+      "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], [101, 2061, 2031, 1045, 999, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]} \n",
+      "\n",
+      "{'input_ids': [[101, 1045, 1005, 2310, 2042, 3403, 2005, 102], [101, 2061, 2031, 1045, 999, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]} \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "sequences = [\n",
+    "    \"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
+    "print(model_inputs, \"\\n\")\n",
+    "# Will truncate the sequences that are longer than the model max length\n",
+    "# (512 for BERT or DistilBERT)\n",
+    "model_inputs = tokenizer(sequences, truncation=True)\n",
+    "print(model_inputs, \"\\n\")\n",
+    "# Will truncate the sequences that are longer than the specified max length\n",
+    "model_inputs = tokenizer(sequences, max_length=8, truncation=True)\n",
+    "print(model_inputs, \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### 5. Outputting difference tensor frameworks\n",
+    "\n",
+    "* Note: Must have framework installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequences = [\n",
+    "    \"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
+    "\n",
+    "# Returns PyTorch tensors\n",
+    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
+    "print(type(inputs), \"\\n\")\n",
+    "# Returns TensorFlow tensors\n",
+    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
+    "print(type(model_inputs), \"\\n\")\n",
+    "# Returns NumPy arrays\n",
+    "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")\n",
+    "print(type(model_inputs), \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### 6. Bringing it all together\n",
+    "\n",
+    "* `tokenizer` does a lot of this automatically"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102] \n",
+      "\n",
+      "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012] \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
+    "\n",
+    "model_inputs = tokenizer(sequence)\n",
+    "print(model_inputs[\"input_ids\"], '\\n')\n",
+    "\n",
+    "tokens = tokenizer.tokenize(sequence)\n",
+    "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
+    "print(ids, '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CLS] i've been waiting for a huggingface course my whole life. [SEP]\n",
+      "i've been waiting for a huggingface course my whole life.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
+    "print(tokenizer.decode(ids))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Comparing this to original input can see impact of `tokenizer`, esp with special tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SequenceClassifierOutput(loss=None, logits=tensor([[-1.5607,  1.6123],\n",
+      "        [-3.6183,  3.9137]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+    "\n",
+    "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
+    "sequences = [\n",
+    "    \"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
+    "\n",
+    "tokens = tokenizer(sequences, padding=True,\n",
+    "                   truncation=True, return_tensors=\"pt\")\n",
+    "output = model(**tokens)\n",
+    "\n",
+    "print(output)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Putting it all together (PyTorch)",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index 854cef3d..88998a64 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,131 @@
+aiohappyeyeballs==2.4.0
+aiohttp==3.10.5
+aiosignal==1.3.1
+anyio==4.4.0
+argon2-cffi==23.1.0
+argon2-cffi-bindings==21.2.0
+arrow==1.3.0
+asttokens==2.4.1
+async-lru==2.0.4
+async-timeout==4.0.3
+attrs==24.2.0
+babel==2.16.0
+beautifulsoup4==4.12.3
+bleach==6.1.0
+certifi==2024.8.30
+cffi==1.17.1
+charset-normalizer==3.3.2
+comm==0.2.2
+datasets==2.21.0
+debugpy==1.8.5
+decorator==5.1.1
+defusedxml==0.7.1
+dill==0.3.8
+evaluate==0.4.2
+exceptiongroup==1.2.2
+executing==2.1.0
+fastjsonschema==2.20.0
+filelock==3.15.4
+fqdn==1.5.1
+frozenlist==1.4.1
+fsspec==2024.6.1
 gitdb==4.0.11
 GitPython==3.1.41
+h11==0.14.0
+httpcore==1.0.5
+httpx==0.27.2
+huggingface-hub==0.24.6
+idna==3.8
+ipykernel==6.29.5
+ipython==8.27.0
+ipywidgets==8.1.5
+isoduration==20.11.0
+jedi==0.19.1
+Jinja2==3.1.4
+json5==0.9.25
+jsonpointer==3.0.0
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+jupyter==1.1.1
+jupyter-console==6.6.3
+jupyter-events==0.10.0
+jupyter-lsp==2.2.5
+jupyter_client==8.6.2
+jupyter_core==5.7.2
+jupyter_server==2.14.2
+jupyter_server_terminals==0.5.3
+jupyterlab==4.2.5
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+jupyterlab_widgets==3.0.13
+MarkupSafe==2.1.5
+matplotlib-inline==0.1.7
+mistune==3.0.2
+mpmath==1.3.0
+multidict==6.0.5
+multiprocess==0.70.16
+nbclient==0.10.0
+nbconvert==7.16.4
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.3
+notebook==7.2.2
+notebook_shim==0.2.4
+numpy==2.1.0
+overrides==7.7.0
+packaging==24.1
+pandas==2.2.2
+pandocfilters==1.5.1
+parso==0.8.4
+pexpect==4.9.0
+platformdirs==4.2.2
+prometheus_client==0.20.0
+prompt_toolkit==3.0.47
+protobuf==5.28.0
+psutil==6.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pyarrow==17.0.0
+pycparser==2.22
+Pygments==2.18.0
+python-dateutil==2.9.0.post0
+python-json-logger==2.0.7
+pytz==2024.1
+PyYAML==6.0.2
+pyzmq==26.2.0
+referencing==0.35.1
+regex==2024.7.24
+requests==2.32.3
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rpds-py==0.20.0
+safetensors==0.4.4
+Send2Trash==1.8.3
+sentencepiece==0.2.0
+six==1.16.0
 smmap==5.0.1
+sniffio==1.3.1
+soupsieve==2.6
+stack-data==0.6.3
+sympy==1.13.2
+terminado==0.18.1
+tinycss2==1.3.0
+tokenizers==0.19.1
+tomli==2.0.1
+torch==2.4.0
+tornado==6.4.1
+tqdm==4.66.5
+traitlets==5.14.3
+transformers==4.44.2
+types-python-dateutil==2.9.0.20240821
+typing_extensions==4.12.2
+tzdata==2024.1
+uri-template==1.3.0
+urllib3==2.2.2
+wcwidth==0.2.13
+webcolors==24.8.0
+webencodings==0.5.1
+websocket-client==1.8.0
+widgetsnbextension==4.0.13
+xxhash==3.5.0
+yarl==1.9.7

From 23c1182d5815a4cab837d7c427c80e6e170b84e2 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Tue, 10 Sep 2024 19:53:05 +0200
Subject: [PATCH 12/20] =?UTF-8?q?ch2=20sec7=20=E2=9C=85?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 course/en/chapter3/section2_pt.ipynb | 766 +++++++++++++++++++++++++--
 course/en/chapter3/section2_tf.ipynb | 341 ------------
 course/en/chapter3/section3_tf.ipynb | 202 -------
 3 files changed, 721 insertions(+), 588 deletions(-)
 delete mode 100644 course/en/chapter3/section2_tf.ipynb
 delete mode 100644 course/en/chapter3/section3_tf.ipynb

diff --git a/course/en/chapter3/section2_pt.ipynb b/course/en/chapter3/section2_pt.ipynb
index 2359cbe7..8b0e23d5 100644
--- a/course/en/chapter3/section2_pt.ipynb
+++ b/course/en/chapter3/section2_pt.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Processing the data (PyTorch)"
+    "# [Processing the data (PyTorch)](https://huggingface.co/learn/nlp-course/chapter3/2?fw=pt)"
    ]
   },
   {
@@ -16,18 +16,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98b45a0a361443bf8e6e78fef49f38ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "659c6d303d9a40ec8de6e5cbe0c0c75c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0db3b15930ba421c85120fec726369a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4007794e658f42d0875d736faea020c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1e899d4d4294f549ce3bed6d762ee75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "from transformers import AdamW, AutoTokenizer, AutoModelForSequenceClassification\n",
@@ -40,7 +129,8 @@
     "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
     "    \"This course is amazing!\",\n",
     "]\n",
-    "batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
+    "batch = tokenizer(sequences, padding=True,\n",
+    "                  truncation=True, return_tensors=\"pt\")\n",
     "\n",
     "# This is new\n",
     "batch[\"labels\"] = torch.tensor([1, 1])\n",
@@ -53,9 +143,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "627d60c5d47f450cb2968dbb9b0d5db1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/35.3k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "847c2f8249e043e3b8e7534c5b7ec90b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/649k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0271cca633a42d1ae047351092c6bc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/75.7k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a9dd5bf365e41d9bc01e5a007a0f3ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/308k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6bca166b6fe14333a38c795ce9fafbc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "65f5be92fbf647ed8efad03ceb9ce388",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61d9d102718f4402a9a001bb4c64a7d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/plain": [
@@ -75,7 +263,7 @@
        "})"
       ]
      },
-     "execution_count": null,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -89,31 +277,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_train_dataset = raw_datasets[\"train\"]\n",
+    "raw_validation_dataset = raw_datasets[\"validation\"]\n",
+    "raw_test_dataset = raw_datasets[\"test\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'idx': 0,\n",
+       "{'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
+       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n",
        " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
+       " 'idx': 0}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
     "raw_train_dataset[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'sentence1': 'Rudder was most recently senior vice president for the Developer & Platform Evangelism Business .',\n",
+       " 'sentence2': 'Senior Vice President Eric Rudder , formerly head of the Developer and Platform Evangelism unit , will lead the new entity .',\n",
+       " 'label': 0,\n",
+       " 'idx': 16}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_train_dataset[15]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'sentence1': 'However , EPA officials would not confirm the 20 percent figure .',\n",
+       " 'sentence2': 'Only in the past few weeks have officials settled on the 20 percent figure .',\n",
+       " 'label': 0,\n",
+       " 'idx': 812}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_validation_dataset[87]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### `.features` dataset attribute"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -121,11 +372,11 @@
       "text/plain": [
        "{'sentence1': Value(dtype='string', id=None),\n",
        " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
+       " 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None),\n",
        " 'idx': Value(dtype='int32', id=None)}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,9 +387,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'sentence1': Value(dtype='string', id=None),\n",
+       " 'sentence2': Value(dtype='string', id=None),\n",
+       " 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None),\n",
+       " 'idx': Value(dtype='int32', id=None)}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_validation_dataset.features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### [Preprocessing Sentence Pairs](0u3ioSwev3s)\n",
+    "> [![Video Title](https://img.youtube.com/vi/0u3ioSwev3s/0.jpg)](https://www.youtube.com/watch?v=0u3ioSwev3s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from transformers import AutoTokenizer\n",
     "\n",
@@ -150,20 +441,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
+       "{'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -175,16 +462,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
+       "['[CLS]',\n",
+       " 'this',\n",
+       " 'is',\n",
+       " 'the',\n",
+       " 'first',\n",
+       " 'sentence',\n",
+       " '.',\n",
+       " '[SEP]',\n",
+       " 'this',\n",
+       " 'is',\n",
+       " 'the',\n",
+       " 'second',\n",
+       " 'one',\n",
+       " '.',\n",
+       " '[SEP]']"
       ]
      },
-     "execution_count": null,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -193,9 +494,105 @@
     "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> #### ❓?❓ Not sure how to get `token_type_ids` ❓?❓ \n",
+    "> * Should automatically come from `tokenizer` if you pass 2 sentences\n",
+    "> * Says which tokens are from which sentence\n",
+    "> "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': [101, 24049, 2001, 2087, 3728, 3026, 3580, 2343, 2005, 1996, 9722, 1004, 4132, 9340, 12439, 2964, 2449, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer(raw_train_dataset[15]['sentence1'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': [101, 3026, 3580, 2343, 4388, 24049, 1010, 3839, 2132, 1997, 1996, 9722, 1998, 4132, 9340, 12439, 2964, 3131, 1010, 2097, 2599, 1996, 2047, 9178, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer(raw_train_dataset[15]['sentence2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'input_ids': [101, 24049, 2001, 2087, 3728, 3026, 3580, 2343, 2005, 1996, 9722, 1004, 4132, 9340, 12439, 2964, 2449, 1012, 102, 3026, 3580, 2343, 4388, 24049, 1010, 3839, 2132, 1997, 1996, 9722, 1998, 4132, 9340, 12439, 2964, 3131, 1010, 2097, 2599, 1996, 2047, 9178, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tokenizer(\n",
+    "    raw_datasets[\"train\"][15][\"sentence1\"],\n",
+    "    raw_datasets[\"train\"][15][\"sentence2\"],\n",
+    "    padding=True,\n",
+    "    truncation=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### Notice how the tokens are different for individual vs pair as input "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,51 +606,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
     "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
+    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True, max_length=128)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59827fb55f4e43abb27a6cff11a74499",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32d6ff358735418eb982ba79def27cdd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98cad7d5e9224adfb6ded0a3b384ed5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/plain": [
        "DatasetDict({\n",
        "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
+       "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
        "        num_rows: 3668\n",
        "    })\n",
        "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
+       "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
        "        num_rows: 408\n",
        "    })\n",
        "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
+       "        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
        "        num_rows: 1725\n",
        "    })\n",
        "})"
       ]
      },
-     "execution_count": null,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# `batched=True` dramatically speeds up tokenization\n",
     "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
     "tokenized_datasets"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### [Dynamic Padding](https://huggingface.co/learn/nlp-course/chapter3/2?fw=pt#dynamic-padding)\n",
+    "> [![Video Title](https://img.youtube.com/vi/7q5NyFT8REg/0.jpg)](https://www.youtube.com/watch?v=7q5NyFT8REg)\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -273,32 +721,33 @@
        "[50, 59, 47, 67, 59, 50, 62, 32]"
       ]
      },
-     "execution_count": null,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
+    "samples = {k: v for k, v in samples.items() if k not in [\n",
+    "    \"idx\", \"sentence1\", \"sentence2\"]}\n",
     "[len(x) for x in samples[\"input_ids\"]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'attention_mask': torch.Size([8, 67]),\n",
-       " 'input_ids': torch.Size([8, 67]),\n",
+       "{'input_ids': torch.Size([8, 67]),\n",
        " 'token_type_ids': torch.Size([8, 67]),\n",
+       " 'attention_mask': torch.Size([8, 67]),\n",
        " 'labels': torch.Size([8])}"
       ]
      },
-     "execution_count": null,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -307,12 +756,239 @@
     "batch = data_collator(samples)\n",
     "{k: v.shape for k, v in batch.items()}"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c1abade32c14b1486d256a87f205a7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoTokenizer\n",
+    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
+    "checkpoint = \"bert-base-cased\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "\n",
+    "\n",
+    "def tokenize_function(examples):\n",
+    "    return tokenizer(\n",
+    "        examples[\"sentence1\"], examples[\"sentence2\"], padding=\"max_length\", truncation=True, max_length=128\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
+    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
+    "    [\"idx\", \"sentence1\", \"sentence2\"])\n",
+    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+    "tokenized_datasets = tokenized_datasets.with_format(\"torch\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([16, 128])\n",
+      "torch.Size([16, 128])\n",
+      "torch.Size([16, 128])\n",
+      "torch.Size([16, 128])\n",
+      "torch.Size([16, 128])\n",
+      "torch.Size([16, 128])\n",
+      "torch.Size([16, 128])\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from torch.utils.data import DataLoader\n",
+    "train_dataloader = DataLoader(\n",
+    "    tokenized_datasets[\"train\"], batch_size=16, shuffle=True)\n",
+    "for step, batch in enumerate(train_dataloader):\n",
+    "    print(batch[\"input_ids\"].shape)\n",
+    "    if step > 5:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ### Dynamic padding means you defer padding to the batch construction phase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18e0040597274c4d9bf4cb3e7065226b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91d7d020d32a48c29db00826c055d0ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b20697b428414089a2582dc2364b4a2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoTokenizer\n",
+    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
+    "checkpoint = \"bert-base-cased\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+    "\n",
+    "\n",
+    "def tokenize_function(examples):\n",
+    "    return tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True)\n",
+    "\n",
+    "\n",
+    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
+    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
+    "    [\"idx\", \"sentence1\", \"sentence2\"])\n",
+    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+    "tokenized_datasets = tokenized_datasets.with_format(\"torch\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([16, 77])\n",
+      "torch.Size([16, 70])\n",
+      "torch.Size([16, 85])\n",
+      "torch.Size([16, 73])\n",
+      "torch.Size([16, 74])\n",
+      "torch.Size([16, 75])\n",
+      "torch.Size([16, 91])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from transformers import DataCollatorWithPadding\n",
+    "\n",
+    "data_collator = DataCollatorWithPadding(tokenizer)\n",
+    "train_dataloader = DataLoader(\n",
+    "    tokenized_datasets['train'], batch_size=16, shuffle=True, collate_fn=data_collator)\n",
+    "\n",
+    "for step, batch in enumerate(train_dataloader):\n",
+    "    print(batch['input_ids'].shape)\n",
+    "    if step > 5:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> ###  GLUE SST-2 dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "colab": {
    "name": "Processing the data (PyTorch)",
    "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/course/en/chapter3/section2_tf.ipynb b/course/en/chapter3/section2_tf.ipynb
deleted file mode 100644
index 377dc238..00000000
--- a/course/en/chapter3/section2_tf.ipynb
+++ /dev/null
@@ -1,341 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Processing the data (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import numpy as np\n",
-    "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
-    "\n",
-    "# Same as before\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
-    "sequences = [\n",
-    "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
-    "    \"This course is amazing!\",\n",
-    "]\n",
-    "batch = dict(tokenizer(sequences, padding=True, truncation=True, return_tensors=\"tf\"))\n",
-    "\n",
-    "# This is new\n",
-    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\")\n",
-    "labels = tf.convert_to_tensor([1, 1])\n",
-    "model.train_on_batch(batch, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "raw_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'idx': 0,\n",
-       " 'label': 1,\n",
-       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
-       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset = raw_datasets[\"train\"]\n",
-    "raw_train_dataset[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sentence1': Value(dtype='string', id=None),\n",
-       " 'sentence2': Value(dtype='string', id=None),\n",
-       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
-       " 'idx': Value(dtype='int32', id=None)}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw_train_dataset.features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "tokenized_sentences_1 = tokenizer(raw_datasets[\"train\"][\"sentence1\"])\n",
-    "tokenized_sentences_2 = tokenizer(raw_datasets[\"train\"][\"sentence2\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{ \n",
-       "  'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102],\n",
-       "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],\n",
-       "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
-       "}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n",
-    "inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[CLS]', 'this', 'is', 'the', 'first', 'sentence', '.', '[SEP]', 'this', 'is', 'the', 'second', 'one', '.', '[SEP]']"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenized_dataset = tokenizer(\n",
-    "    raw_datasets[\"train\"][\"sentence1\"],\n",
-    "    raw_datasets[\"train\"][\"sentence2\"],\n",
-    "    padding=True,\n",
-    "    truncation=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetDict({\n",
-       "    train: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 3668\n",
-       "    })\n",
-       "    validation: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 408\n",
-       "    })\n",
-       "    test: Dataset({\n",
-       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
-       "        num_rows: 1725\n",
-       "    })\n",
-       "})"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "tokenized_datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import DataCollatorWithPadding\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 59, 47, 67, 59, 50, 62, 32]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples = tokenized_datasets[\"train\"][:8]\n",
-    "samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n",
-    "[len(x) for x in samples[\"input_ids\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'attention_mask': TensorShape([8, 67]),\n",
-       " 'input_ids': TensorShape([8, 67]),\n",
-       " 'token_type_ids': TensorShape([8, 67]),\n",
-       " 'labels': TensorShape([8])}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch = data_collator(samples)\n",
-    "{k: v.shape for k, v in batch.items()}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Processing the data (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter3/section3_tf.ipynb b/course/en/chapter3/section3_tf.ipynb
deleted file mode 100644
index cd1f4cf6..00000000
--- a/course/en/chapter3/section3_tf.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fine-tuning a model with the Trainer API or Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "import numpy as np\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
-    "\n",
-    "tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=True,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")\n",
-    "\n",
-    "tf_validation_dataset = tokenized_datasets[\"validation\"].to_tf_dataset(\n",
-    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
-    "    label_cols=[\"labels\"],\n",
-    "    shuffle=False,\n",
-    "    collate_fn=data_collator,\n",
-    "    batch_size=8,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForSequenceClassification\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
-    "\n",
-    "model.compile(\n",
-    "    optimizer=\"adam\",\n",
-    "    loss=SparseCategoricalCrossentropy(from_logits=True),\n",
-    "    metrics=[\"accuracy\"],\n",
-    ")\n",
-    "model.fit(\n",
-    "    tf_train_dataset,\n",
-    "    validation_data=tf_validation_dataset,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tensorflow.keras.optimizers.schedules import PolynomialDecay\n",
-    "\n",
-    "batch_size = 8\n",
-    "num_epochs = 3\n",
-    "# The number of training steps is the number of samples in the dataset, divided by the batch size then multiplied\n",
-    "# by the total number of epochs. Note that the tf_train_dataset here is a batched tf.data.Dataset,\n",
-    "# not the original Hugging Face Dataset, so its len() is already num_samples // batch_size.\n",
-    "num_train_steps = len(tf_train_dataset) * num_epochs\n",
-    "lr_scheduler = PolynomialDecay(\n",
-    "    initial_learning_rate=5e-5, end_learning_rate=0.0, decay_steps=num_train_steps\n",
-    ")\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "opt = Adam(learning_rate=lr_scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "\n",
-    "model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
-    "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
-    "model.compile(optimizer=opt, loss=loss, metrics=[\"accuracy\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preds = model.predict(tf_validation_dataset)[\"logits\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_preds = np.argmax(preds, axis=1)\n",
-    "print(preds.shape, class_preds.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=class_preds, references=raw_datasets[\"validation\"][\"label\"])"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Fine-tuning a model with the Trainer API or Keras",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 0f18917521335cfbe3cc7d00a8282b008b9ec0c8 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Thu, 12 Sep 2024 17:04:13 +0200
Subject: [PATCH 13/20] =?UTF-8?q?+=20media=20=F0=9F=93=BD=EF=B8=8F?=
 =?UTF-8?q?=F0=9F=93=BD=EF=B8=8F=F0=9F=93=BD=EF=B8=8F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 course/en/chapter3/section3.ipynb | 109 ++++++++++++++++++++++++++----
 1 file changed, 95 insertions(+), 14 deletions(-)

diff --git a/course/en/chapter3/section3.ipynb b/course/en/chapter3/section3.ipynb
index e5000753..da26aad3 100644
--- a/course/en/chapter3/section3.ipynb
+++ b/course/en/chapter3/section3.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fine-tuning a model with the Trainer API or Keras"
+    "> # [Fine-tuning a model with the Trainer API or Keras](https://huggingface.co/learn/nlp-course/chapter3/3?fw=pt)\n",
+    "> [![Video Title](https://img.youtube.com/vi/nvBXf7s7vTI/0.jpg)](https://www.youtube.com/watch?v=nvBXf7s7vTI)"
    ]
   },
   {
@@ -16,18 +17,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
+    "!pip install datasets evaluate transformers[sentencepiece] -q"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "800046d51a0f48288118e40e3bb81252",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from datasets import load_dataset\n",
     "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
@@ -47,29 +71,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "from transformers import TrainingArguments\n",
     "\n",
-    "training_args = TrainingArguments(\"test-trainer\")"
+    "training_args = TrainingArguments(\"test-trainer\",  # push_to_hub=True\n",
+    "                                  )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
    "source": [
     "from transformers import AutoModelForSequenceClassification\n",
     "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    checkpoint, num_labels=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,9 +122,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='36' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [  36/1377 14:51 < 9:46:14, 0.04 it/s, Epoch 0.08/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "trainer.train()"
    ]
@@ -169,7 +232,8 @@
    "outputs": [],
    "source": [
     "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    checkpoint, num_labels=2)\n",
     "\n",
     "trainer = Trainer(\n",
     "    model,\n",
@@ -196,6 +260,23 @@
   "colab": {
    "name": "Fine-tuning a model with the Trainer API or Keras",
    "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

From 09822ea4acce20dee48975b615c3d367343076f5 Mon Sep 17 00:00:00 2001
From: neelan pather <neelan.pather@gmail.com>
Date: Thu, 12 Sep 2024 17:55:34 +0200
Subject: [PATCH 14/20] =?UTF-8?q?Model=20run=20on=20Colab=20=F0=9F=91=BE?=
 =?UTF-8?q?=F0=9F=91=BE=F0=9F=91=BE?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 course/en/chapter3/section3.ipynb | 6619 +++++++++++++++++++++++++++--
 1 file changed, 6351 insertions(+), 268 deletions(-)

diff --git a/course/en/chapter3/section3.ipynb b/course/en/chapter3/section3.ipynb
index da26aad3..15022f11 100644
--- a/course/en/chapter3/section3.ipynb
+++ b/course/en/chapter3/section3.ipynb
@@ -1,284 +1,6367 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> # [Fine-tuning a model with the Trainer API or Keras](https://huggingface.co/learn/nlp-course/chapter3/3?fw=pt)\n",
-    "> [![Video Title](https://img.youtube.com/vi/nvBXf7s7vTI/0.jpg)](https://www.youtube.com/watch?v=nvBXf7s7vTI)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece] -q"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
-      "  warnings.warn(\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/neeland/notebooks/blob/learn/course/en/chapter3/section3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "800046d51a0f48288118e40e3bb81252",
-       "version_major": 2,
-       "version_minor": 0
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v1P4mTbLzqu-"
       },
-      "text/plain": [
-       "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
+      "source": [
+        "> # [Fine-tuning a model with the Trainer API or Keras](https://huggingface.co/learn/nlp-course/chapter3/3?fw=pt)\n",
+        "> [![Video Title](https://img.youtube.com/vi/nvBXf7s7vTI/0.jpg)](https://www.youtube.com/watch?v=nvBXf7s7vTI)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from datasets import load_dataset\n",
-    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
-    "\n",
-    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
-    "checkpoint = \"bert-base-uncased\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "\n",
-    "def tokenize_function(example):\n",
-    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
-    "\n",
-    "\n",
-    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
-    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TrainingArguments\n",
-    "\n",
-    "training_args = TrainingArguments(\"test-trainer\",  # push_to_hub=True\n",
-    "                                  )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import AutoModelForSequenceClassification\n",
-    "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(\n",
-    "    checkpoint, num_labels=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import Trainer\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Lzai65-Kzqu_"
+      },
+      "source": [
+        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
-       "      <progress value='36' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [  36/1377 14:51 < 9:46:14, 0.04 it/s, Epoch 0.08/3]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Step</th>\n",
-       "      <th>Training Loss</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table><p>"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "Vhaxtu79zqu_",
+        "outputId": "51f23fa3-a4c1-453c-d1dc-0ed0aa57faee",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/474.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.3/474.3 kB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m24.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
+            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
       ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+      "source": [
+        "!pip install datasets evaluate transformers[sentencepiece] -q"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "1V_YUit0zqvA",
+        "outputId": "44dc4bed-4476-482b-c0a9-9ebfea643518",
+        "colab": {
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+          "height": 624,
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+            "aeac72b5dd214d7199081529ba520c72",
+            "83f9243714384d62be8fea29c006468d",
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+            "d526bfc0da11475193a3fa7855a09c98",
+            "d8413d03f4624aeca981c84eb7d38e85",
+            "973524eee8fe4f09b4db280ffdcf2960",
+            "f14768d685134e00856ffcea38e4f065",
+            "7bf315faef2649df9171f1109509a17e"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/35.3k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e374ce26d96a422283080b73134ad5eb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/649k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4735ad35076e4f9ab1890378a8efa3a0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "validation-00000-of-00001.parquet:   0%|          | 0.00/75.7k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4ff8dad5ec374ad689329081186fdbc9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "test-00000-of-00001.parquet:   0%|          | 0.00/308k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d040f0f26d574907a34efdaf9a2262e9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2dfc49fe42164b8fbf54f5cb14b83d2a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6844fc73cb5f49468dd41eecb717b0d2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b765fe2736cb417f8d22f27d217cf3b1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0a0eab2e9cc84b1f8e14f6e2b4975cf2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b2e0d49f92ab4707b0867c7ad7a15db3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fd8e5e6c3f194092a2eed8553c31f695"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "874a1c58a04b42ee942f3e62e4719b52"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8b1e93ba45594c79bc14650dc8bb8ee5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9307a47418c749099803d0aedd4ecf08"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e2ae25a71ce94b249271e3933b7b9f54"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
+        "\n",
+        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
+        "checkpoint = \"bert-base-uncased\"\n",
+        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
+        "\n",
+        "\n",
+        "def tokenize_function(example):\n",
+        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
+        "\n",
+        "\n",
+        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
+        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "(408, 2) (408,)"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "OP_Ly1i3zqvA"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import TrainingArguments\n",
+        "\n",
+        "training_args = TrainingArguments(\"test-trainer\",  # push_to_hub=True\n",
+        "                                  )"
       ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
-    "print(predictions.predictions.shape, predictions.label_ids.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "preds = np.argmax(predictions.predictions, axis=-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "76gBpzB1zqvB",
+        "outputId": "c565440a-c963-4ed8-9ec0-863a75b2a92a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 104,
+          "referenced_widgets": [
+            "f7578882f5e645859170cf64d70c1f8d",
+            "55456790e428489c9f28c85499e79cd7",
+            "1483a564ec674773aab6557a09370b3e",
+            "249b6ba601f74900be347d1b63586fe4",
+            "cd5fcb5b98b341ec8138beff37cc2320",
+            "f3f8d5cbe44b4bd897f622048f9264ba",
+            "feb262353351495f93619e6d746b9677",
+            "b9ed8df2b5904209a0e72411245a8809",
+            "ea9ad37f17dc4ea0bbd93b363e48914d",
+            "f5710baa52f74a77b9fba7b7b5fe1a1e",
+            "f18b3361ae544f73b0184bd1b7833dca"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f7578882f5e645859170cf64d70c1f8d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from transformers import AutoModelForSequenceClassification\n",
+        "\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(\n",
+        "    checkpoint, num_labels=2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "AFD3LTMJzqvB"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import Trainer\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model,\n",
+        "    training_args,\n",
+        "    train_dataset=tokenized_datasets[\"train\"],\n",
+        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
+        "    data_collator=data_collator,\n",
+        "    tokenizer=tokenizer,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "Z3Y77aMdzqvB",
+        "outputId": "8aed7ce8-e2fa-4ceb-d57e-971d9dea2d04",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 190
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1377/1377 01:03, Epoch 3/3]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Step</th>\n",
+              "      <th>Training Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>500</td>\n",
+              "      <td>0.504100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>1000</td>\n",
+              "      <td>0.263600</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=1377, training_loss=0.314528618329837, metrics={'train_runtime': 65.2437, 'train_samples_per_second': 168.66, 'train_steps_per_second': 21.105, 'total_flos': 405114969714960.0, 'train_loss': 0.314528618329837, 'epoch': 3.0})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "N9G9_DtyzqvB",
+        "outputId": "1c9d6e54-6080-47fe-f4b1-1cc9088ad232",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": []
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(408, 2) (408,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
+        "print(predictions.predictions.shape, predictions.label_ids.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "GefgKU_wzqvB"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "preds = np.argmax(predictions.predictions, axis=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> ### `evaluate` package used to define `compute_metrics()`"
+      ],
+      "metadata": {
+        "id": "DfzHHTEJ5n-u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "3ANhgp0dzqvB",
+        "outputId": "1ad3a973-f6bc-41f7-e4e9-165c2fe30f10",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 66,
+          "referenced_widgets": [
+            "d257a75548f1457b9240363ce13478e6",
+            "16a6ad2dd84e4161b3c3831479dc7c78",
+            "bd9e0943ddee4724946c0e976ab85138",
+            "2764b5a2033844b194faa5a1d9bd0014",
+            "a8384f10e2454341a1096010165275ef",
+            "bad13c84a4c94483877793091b4a8483",
+            "1c802c3e5f39458ba9f574c49a5a4008",
+            "dcccdcd063c14a77a693217ad3442568",
+            "2971f3f66c8b4bdb991ed47052502b1a",
+            "6e1322b5b04e4e8989a2295b92e8c2e6",
+            "113ef0c25a724d64b417531a8828f46b"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d257a75548f1457b9240363ce13478e6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'accuracy': 0.8455882352941176, 'f1': 0.8904347826086957}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "import evaluate\n",
+        "\n",
+        "metric = evaluate.load(\"glue\", \"mrpc\") # same form as `raw_datasets = load_dataset(\"glue\", \"mrpc\")`\n",
+        "metric.compute(predictions=preds, references=predictions.label_ids)"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "mFwhKAxfzqvC"
+      },
+      "outputs": [],
+      "source": [
+        "def compute_metrics(eval_preds):\n",
+        "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
+        "    logits, labels = eval_preds\n",
+        "    predictions = np.argmax(logits, axis=-1)\n",
+        "    return metric.compute(predictions=predictions, references=labels)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "b7x1l26ZzqvC",
+        "outputId": "9b5a4cbb-c0e0-4c54-c7fd-5dbd20162bf4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
+            "  warnings.warn(\n",
+            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ],
+      "source": [
+        "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(\n",
+        "    checkpoint, num_labels=2)\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model,\n",
+        "    training_args,\n",
+        "    train_dataset=tokenized_datasets[\"train\"],\n",
+        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
+        "    data_collator=data_collator,\n",
+        "    tokenizer=tokenizer,\n",
+        "    compute_metrics=compute_metrics, # <-- we need this to evaluate!!!\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "ac1oai7HzqvC",
+        "outputId": "e9d54ec7-295b-4aed-b59e-b55b5244dc3a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [1377/1377 01:11, Epoch 3/3]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Accuracy</th>\n",
+              "      <th>F1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>No log</td>\n",
+              "      <td>0.456646</td>\n",
+              "      <td>0.789216</td>\n",
+              "      <td>0.853242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.570800</td>\n",
+              "      <td>0.391561</td>\n",
+              "      <td>0.862745</td>\n",
+              "      <td>0.900000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>0.344600</td>\n",
+              "      <td>0.606528</td>\n",
+              "      <td>0.850490</td>\n",
+              "      <td>0.894646</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=1377, training_loss=0.3907704405275565, metrics={'train_runtime': 71.8423, 'train_samples_per_second': 153.169, 'train_steps_per_second': 19.167, 'total_flos': 405114969714960.0, 'train_loss': 0.3907704405275565, 'epoch': 3.0})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "trainer.train()"
       ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "import evaluate\n",
-    "\n",
-    "metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "metric.compute(predictions=preds, references=predictions.label_ids)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_metrics(eval_preds):\n",
-    "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
-    "    logits, labels = eval_preds\n",
-    "    predictions = np.argmax(logits, axis=-1)\n",
-    "    return metric.compute(predictions=predictions, references=labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(\n",
-    "    checkpoint, num_labels=2)\n",
-    "\n",
-    "trainer = Trainer(\n",
-    "    model,\n",
-    "    training_args,\n",
-    "    train_dataset=tokenized_datasets[\"train\"],\n",
-    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
-    "    data_collator=data_collator,\n",
-    "    tokenizer=tokenizer,\n",
-    "    compute_metrics=compute_metrics,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trainer.train()"
-   ]
-  }
- ],
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-   "display_name": "Python 3",
-   "language": "python",
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+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "2971f3f66c8b4bdb991ed47052502b1a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "6e1322b5b04e4e8989a2295b92e8c2e6": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
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+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
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+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "113ef0c25a724d64b417531a8828f46b": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
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+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From f7a27f760289294f778450930d6b6a5775bffc83 Mon Sep 17 00:00:00 2001
From: neelan pather <neelan.pather@gmail.com>
Date: Thu, 12 Sep 2024 18:09:40 +0200
Subject: [PATCH 15/20] added note for `EvalPrediction` object

---
 course/en/chapter3/section3.ipynb | 50 +++++++++++++++----------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/course/en/chapter3/section3.ipynb b/course/en/chapter3/section3.ipynb
index 15022f11..3b015399 100644
--- a/course/en/chapter3/section3.ipynb
+++ b/course/en/chapter3/section3.ipynb
@@ -33,11 +33,11 @@
       "cell_type": "code",
       "execution_count": 1,
       "metadata": {
-        "id": "Vhaxtu79zqu_",
-        "outputId": "51f23fa3-a4c1-453c-d1dc-0ed0aa57faee",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "Vhaxtu79zqu_",
+        "outputId": "51f23fa3-a4c1-453c-d1dc-0ed0aa57faee"
       },
       "outputs": [
         {
@@ -65,8 +65,6 @@
       "cell_type": "code",
       "execution_count": 2,
       "metadata": {
-        "id": "1V_YUit0zqvA",
-        "outputId": "44dc4bed-4476-482b-c0a9-9ebfea643518",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 624,
@@ -226,7 +224,9 @@
             "f14768d685134e00856ffcea38e4f065",
             "7bf315faef2649df9171f1109509a17e"
           ]
-        }
+        },
+        "id": "1V_YUit0zqvA",
+        "outputId": "44dc4bed-4476-482b-c0a9-9ebfea643518"
       },
       "outputs": [
         {
@@ -481,8 +481,6 @@
       "cell_type": "code",
       "execution_count": 4,
       "metadata": {
-        "id": "76gBpzB1zqvB",
-        "outputId": "c565440a-c963-4ed8-9ec0-863a75b2a92a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 104,
@@ -499,7 +497,9 @@
             "f5710baa52f74a77b9fba7b7b5fe1a1e",
             "f18b3361ae544f73b0184bd1b7833dca"
           ]
-        }
+        },
+        "id": "76gBpzB1zqvB",
+        "outputId": "c565440a-c963-4ed8-9ec0-863a75b2a92a"
       },
       "outputs": [
         {
@@ -556,12 +556,12 @@
       "cell_type": "code",
       "execution_count": 6,
       "metadata": {
-        "id": "Z3Y77aMdzqvB",
-        "outputId": "8aed7ce8-e2fa-4ceb-d57e-971d9dea2d04",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 190
-        }
+        },
+        "id": "Z3Y77aMdzqvB",
+        "outputId": "8aed7ce8-e2fa-4ceb-d57e-971d9dea2d04"
       },
       "outputs": [
         {
@@ -618,12 +618,12 @@
       "cell_type": "code",
       "execution_count": 7,
       "metadata": {
-        "id": "N9G9_DtyzqvB",
-        "outputId": "1c9d6e54-6080-47fe-f4b1-1cc9088ad232",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        }
+        },
+        "id": "N9G9_DtyzqvB",
+        "outputId": "1c9d6e54-6080-47fe-f4b1-1cc9088ad232"
       },
       "outputs": [
         {
@@ -675,8 +675,6 @@
       "cell_type": "code",
       "execution_count": 10,
       "metadata": {
-        "id": "3ANhgp0dzqvB",
-        "outputId": "1ad3a973-f6bc-41f7-e4e9-165c2fe30f10",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 66,
@@ -693,7 +691,9 @@
             "6e1322b5b04e4e8989a2295b92e8c2e6",
             "113ef0c25a724d64b417531a8828f46b"
           ]
-        }
+        },
+        "id": "3ANhgp0dzqvB",
+        "outputId": "1ad3a973-f6bc-41f7-e4e9-165c2fe30f10"
       },
       "outputs": [
         {
@@ -736,7 +736,7 @@
       },
       "outputs": [],
       "source": [
-        "def compute_metrics(eval_preds):\n",
+        "def compute_metrics(eval_preds): # takes on EvalPrediction object\n",
         "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
         "    logits, labels = eval_preds\n",
         "    predictions = np.argmax(logits, axis=-1)\n",
@@ -747,11 +747,11 @@
       "cell_type": "code",
       "execution_count": 12,
       "metadata": {
-        "id": "b7x1l26ZzqvC",
-        "outputId": "9b5a4cbb-c0e0-4c54-c7fd-5dbd20162bf4",
         "colab": {
           "base_uri": "https://localhost:8080/"
-        }
+        },
+        "id": "b7x1l26ZzqvC",
+        "outputId": "9b5a4cbb-c0e0-4c54-c7fd-5dbd20162bf4"
       },
       "outputs": [
         {
@@ -785,12 +785,12 @@
       "cell_type": "code",
       "execution_count": 13,
       "metadata": {
-        "id": "ac1oai7HzqvC",
-        "outputId": "e9d54ec7-295b-4aed-b59e-b55b5244dc3a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 221
-        }
+        },
+        "id": "ac1oai7HzqvC",
+        "outputId": "e9d54ec7-295b-4aed-b59e-b55b5244dc3a"
       },
       "outputs": [
         {

From d03d5a45ecf14198a08a65f2b695ba8aca820663 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Sun, 15 Sep 2024 11:57:25 +0200
Subject: [PATCH 16/20] compute_metrics()

---
 course/en/chapter3/section3.ipynb | 3730 ++++++++++++++---------------
 1 file changed, 1739 insertions(+), 1991 deletions(-)

diff --git a/course/en/chapter3/section3.ipynb b/course/en/chapter3/section3.ipynb
index 3b015399..3580a730 100644
--- a/course/en/chapter3/section3.ipynb
+++ b/course/en/chapter3/section3.ipynb
@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "view-in-github"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/neeland/notebooks/blob/learn/course/en/chapter3/section3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -41,8 +41,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/474.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.3/474.3 kB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
@@ -63,7 +63,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -230,220 +230,12 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
-            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
-            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
-            "You will be able to reuse this secret in all of your notebooks.\n",
-            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "README.md:   0%|          | 0.00/35.3k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e374ce26d96a422283080b73134ad5eb"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "train-00000-of-00001.parquet:   0%|          | 0.00/649k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4735ad35076e4f9ab1890378a8efa3a0"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "validation-00000-of-00001.parquet:   0%|          | 0.00/75.7k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "4ff8dad5ec374ad689329081186fdbc9"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "test-00000-of-00001.parquet:   0%|          | 0.00/308k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d040f0f26d574907a34efdaf9a2262e9"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "2dfc49fe42164b8fbf54f5cb14b83d2a"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
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-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
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-          "metadata": {}
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-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
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-              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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-            "application/vnd.jupyter.widget-view+json": {
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-            }
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-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "fd8e5e6c3f194092a2eed8553c31f695"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "874a1c58a04b42ee942f3e62e4719b52"
-            }
-          },
-          "metadata": {}
-        },
-        {
           "output_type": "stream",
-          "name": "stderr",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+            "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
             "  warnings.warn(\n"
           ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "8b1e93ba45594c79bc14650dc8bb8ee5"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "9307a47418c749099803d0aedd4ecf08"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "e2ae25a71ce94b249271e3933b7b9f54"
-            }
-          },
-          "metadata": {}
         }
       ],
       "source": [
@@ -465,7 +257,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 4,
       "metadata": {
         "id": "OP_Ly1i3zqvA"
       },
@@ -479,7 +271,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -503,22 +295,8 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "f7578882f5e645859170cf64d70c1f8d"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
             "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
@@ -534,7 +312,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 6,
       "metadata": {
         "id": "AFD3LTMJzqvB"
       },
@@ -554,7 +332,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -563,60 +341,14 @@
         "id": "Z3Y77aMdzqvB",
         "outputId": "8aed7ce8-e2fa-4ceb-d57e-971d9dea2d04"
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [1377/1377 01:03, Epoch 3/3]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Step</th>\n",
-              "      <th>Training Loss</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>500</td>\n",
-              "      <td>0.504100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>1000</td>\n",
-              "      <td>0.263600</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "TrainOutput(global_step=1377, training_loss=0.314528618329837, metrics={'train_runtime': 65.2437, 'train_samples_per_second': 168.66, 'train_steps_per_second': 21.105, 'total_flos': 405114969714960.0, 'train_loss': 0.314528618329837, 'epoch': 3.0})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        }
-      ],
+      "outputs": [],
       "source": [
         "trainer.train()"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -627,20 +359,14 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": []
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "(408, 2) (408,)\n"
+          "ename": "NameError",
+          "evalue": "name 'trainer' is not defined",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(tokenized_datasets[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidation\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(predictions\u001b[38;5;241m.\u001b[39mpredictions\u001b[38;5;241m.\u001b[39mshape, predictions\u001b[38;5;241m.\u001b[39mlabel_ids\u001b[38;5;241m.\u001b[39mshape)\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'trainer' is not defined"
           ]
         }
       ],
@@ -663,13 +389,34 @@
       ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "> ### `evaluate` package used to define `compute_metrics()`"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "ename": "NameError",
+          "evalue": "name 'predictions' is not defined",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpredictions\u001b[49m\u001b[38;5;241m.\u001b[39mkeys()\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'predictions' is not defined"
+          ]
+        }
       ],
+      "source": [
+        "predictions.keys()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
       "metadata": {
         "id": "DfzHHTEJ5n-u"
-      }
+      },
+      "source": [
+        "> ### `evaluate` package used to define `compute_metrics()`"
+      ]
     },
     {
       "cell_type": "code",
@@ -697,34 +444,35 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
-            ],
             "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d257a75548f1457b9240363ce13478e6",
               "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d257a75548f1457b9240363ce13478e6"
-            }
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'accuracy': 0.8455882352941176, 'f1': 0.8904347826086957}"
             ]
           },
+          "execution_count": 10,
           "metadata": {},
-          "execution_count": 10
+          "output_type": "execute_result"
         }
       ],
       "source": [
         "import evaluate\n",
         "\n",
-        "metric = evaluate.load(\"glue\", \"mrpc\") # same form as `raw_datasets = load_dataset(\"glue\", \"mrpc\")`\n",
+        "# same form as `raw_datasets = load_dataset(\"glue\", \"mrpc\")`\n",
+        "metric = evaluate.load(\"glue\", \"mrpc\")\n",
         "metric.compute(predictions=preds, references=predictions.label_ids)"
       ]
     },
@@ -736,7 +484,7 @@
       },
       "outputs": [],
       "source": [
-        "def compute_metrics(eval_preds): # takes on EvalPrediction object\n",
+        "def compute_metrics(eval_preds):  # takes on EvalPrediction object\n",
         "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
         "    logits, labels = eval_preds\n",
         "    predictions = np.argmax(logits, axis=-1)\n",
@@ -755,8 +503,8 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
             "  warnings.warn(\n",
@@ -777,7 +525,7 @@
         "    eval_dataset=tokenized_datasets[\"validation\"],\n",
         "    data_collator=data_collator,\n",
         "    tokenizer=tokenizer,\n",
-        "    compute_metrics=compute_metrics, # <-- we need this to evaluate!!!\n",
+        "    compute_metrics=compute_metrics,  # <-- we need this to evaluate!!!\n",
         ")"
       ]
     },
@@ -794,11 +542,7 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
             "text/html": [
               "\n",
               "    <div>\n",
@@ -840,19 +584,23 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table><p>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
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         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "TrainOutput(global_step=1377, training_loss=0.3907704405275565, metrics={'train_runtime': 71.8423, 'train_samples_per_second': 153.169, 'train_steps_per_second': 19.167, 'total_flos': 405114969714960.0, 'train_loss': 0.3907704405275565, 'epoch': 3.0})"
             ]
           },
+          "execution_count": 13,
           "metadata": {},
-          "execution_count": 13
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -861,12 +609,13 @@
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-      "name": "Fine-tuning a model with the Trainer API or Keras",
-      "provenance": [],
-      "machine_shape": "hm",
       "gpuType": "A100",
-      "include_colab_link": true
+      "include_colab_link": true,
+      "machine_shape": "hm",
+      "name": "Fine-tuning a model with the Trainer API or Keras",
+      "provenance": []
     },
     "kernelspec": {
       "display_name": "Python 3",
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   "nbformat_minor": 0
-}
\ No newline at end of file
+}

From 869ebd51f02fc2451694b471f549af96ac698ddd Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Mon, 16 Sep 2024 12:40:31 +0200
Subject: [PATCH 17/20] =?UTF-8?q?ch3=20sec3=20=E2=9C=85=20+=20trainer=20sc?=
 =?UTF-8?q?ript=20=F0=9F=91=BE?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 course/en/chapter3/section3.ipynb | 68 ++++++++++++++++++++++++++++++-
 course/en/chapter3/trainer.py     | 41 +++++++++++++++++++
 2 files changed, 107 insertions(+), 2 deletions(-)
 create mode 100644 course/en/chapter3/trainer.py

diff --git a/course/en/chapter3/section3.ipynb b/course/en/chapter3/section3.ipynb
index 3580a730..528791bd 100644
--- a/course/en/chapter3/section3.ipynb
+++ b/course/en/chapter3/section3.ipynb
@@ -263,6 +263,8 @@
       },
       "outputs": [],
       "source": [
+        "\n",
+        "\n",
         "from transformers import TrainingArguments\n",
         "\n",
         "training_args = TrainingArguments(\"test-trainer\",  # push_to_hub=True\n",
@@ -304,6 +306,7 @@
         }
       ],
       "source": [
+        "\n",
         "from transformers import AutoModelForSequenceClassification\n",
         "\n",
         "model = AutoModelForSequenceClassification.from_pretrained(\n",
@@ -312,11 +315,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 2,
       "metadata": {
         "id": "AFD3LTMJzqvB"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "ename": "NameError",
+          "evalue": "name '_C' is not defined",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Trainer\n\u001b[1;32m      3\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[1;32m      4\u001b[0m     model,\n\u001b[1;32m      5\u001b[0m     training_args,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      9\u001b[0m     tokenizer\u001b[38;5;241m=\u001b[39mtokenizer,\n\u001b[1;32m     10\u001b[0m )\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/__init__.py:26\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TYPE_CHECKING\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Check the dependencies satisfy the minimal versions required.\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dependency_versions_check\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     28\u001b[0m     OptionalDependencyNotAvailable,\n\u001b[1;32m     29\u001b[0m     _LazyModule,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     48\u001b[0m     logging,\n\u001b[1;32m     49\u001b[0m )\n\u001b[1;32m     52\u001b[0m logger \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mget_logger(\u001b[38;5;18m__name__\u001b[39m)  \u001b[38;5;66;03m# pylint: disable=invalid-name\u001b[39;00m\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/dependency_versions_check.py:16\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 The HuggingFace Team. All rights reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# See the License for the specific language governing permissions and\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependency_versions_table\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deps\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m require_version, require_version_core\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# define which module versions we always want to check at run time\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# (usually the ones defined in `install_requires` in setup.py)\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# order specific notes:\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# - tqdm must be checked before tokenizers\u001b[39;00m\n\u001b[1;32m     25\u001b[0m pkgs_to_check_at_runtime \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtqdm\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyyaml\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     38\u001b[0m ]\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/utils/__init__.py:34\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdoc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     27\u001b[0m     add_code_sample_docstrings,\n\u001b[1;32m     28\u001b[0m     add_end_docstrings,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m     replace_return_docstrings,\n\u001b[1;32m     33\u001b[0m )\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     35\u001b[0m     ContextManagers,\n\u001b[1;32m     36\u001b[0m     ExplicitEnum,\n\u001b[1;32m     37\u001b[0m     ModelOutput,\n\u001b[1;32m     38\u001b[0m     PaddingStrategy,\n\u001b[1;32m     39\u001b[0m     TensorType,\n\u001b[1;32m     40\u001b[0m     add_model_info_to_auto_map,\n\u001b[1;32m     41\u001b[0m     add_model_info_to_custom_pipelines,\n\u001b[1;32m     42\u001b[0m     cached_property,\n\u001b[1;32m     43\u001b[0m     can_return_loss,\n\u001b[1;32m     44\u001b[0m     expand_dims,\n\u001b[1;32m     45\u001b[0m     filter_out_non_signature_kwargs,\n\u001b[1;32m     46\u001b[0m     find_labels,\n\u001b[1;32m     47\u001b[0m     flatten_dict,\n\u001b[1;32m     48\u001b[0m     infer_framework,\n\u001b[1;32m     49\u001b[0m     is_jax_tensor,\n\u001b[1;32m     50\u001b[0m     is_numpy_array,\n\u001b[1;32m     51\u001b[0m     is_tensor,\n\u001b[1;32m     52\u001b[0m     is_tf_symbolic_tensor,\n\u001b[1;32m     53\u001b[0m     is_tf_tensor,\n\u001b[1;32m     54\u001b[0m     is_torch_device,\n\u001b[1;32m     55\u001b[0m     is_torch_dtype,\n\u001b[1;32m     56\u001b[0m     is_torch_tensor,\n\u001b[1;32m     57\u001b[0m     reshape,\n\u001b[1;32m     58\u001b[0m     squeeze,\n\u001b[1;32m     59\u001b[0m     strtobool,\n\u001b[1;32m     60\u001b[0m     tensor_size,\n\u001b[1;32m     61\u001b[0m     to_numpy,\n\u001b[1;32m     62\u001b[0m     to_py_obj,\n\u001b[1;32m     63\u001b[0m     torch_float,\n\u001b[1;32m     64\u001b[0m     torch_int,\n\u001b[1;32m     65\u001b[0m     transpose,\n\u001b[1;32m     66\u001b[0m     working_or_temp_dir,\n\u001b[1;32m     67\u001b[0m )\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     69\u001b[0m     CLOUDFRONT_DISTRIB_PREFIX,\n\u001b[1;32m     70\u001b[0m     HF_MODULES_CACHE,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     96\u001b[0m     try_to_load_from_cache,\n\u001b[1;32m     97\u001b[0m )\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimport_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     99\u001b[0m     ACCELERATE_MIN_VERSION,\n\u001b[1;32m    100\u001b[0m     ENV_VARS_TRUE_AND_AUTO_VALUES,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    219\u001b[0m     torch_only_method,\n\u001b[1;32m    220\u001b[0m )\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/utils/generic.py:462\u001b[0m\n\u001b[1;32m    458\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mself\u001b[39m[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_torch_available():\n\u001b[0;32m--> 462\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pytree\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_torch_pytree\u001b[39;00m\n\u001b[1;32m    464\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_model_output_flatten\u001b[39m(output: ModelOutput) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[Any], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_torch_pytree.Context\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mvalues()), \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mkeys())\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/__init__.py:764\u001b[0m\n\u001b[1;32m    761\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m  \u001b[38;5;66;03m# If __file__ is not None the cause is unknown, so just re-raise.\u001b[39;00m\n\u001b[1;32m    763\u001b[0m __name, __obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 764\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m __name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mdir\u001b[39m(\u001b[43m_C\u001b[49m):\n\u001b[1;32m    765\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m __name[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m __name\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBase\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    766\u001b[0m         __all__\u001b[38;5;241m.\u001b[39mappend(__name)\n",
+            "\u001b[0;31mNameError\u001b[0m: name '_C' is not defined"
+          ]
+        }
+      ],
       "source": [
         "from transformers import Trainer\n",
         "\n",
@@ -484,6 +504,7 @@
       },
       "outputs": [],
       "source": [
+        "\n",
         "def compute_metrics(eval_preds):  # takes on EvalPrediction object\n",
         "    metric = evaluate.load(\"glue\", \"mrpc\")\n",
         "    logits, labels = eval_preds\n",
@@ -514,6 +535,8 @@
         }
       ],
       "source": [
+        "\n",
+        "\n",
         "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
         "model = AutoModelForSequenceClassification.from_pretrained(\n",
         "    checkpoint, num_labels=2)\n",
@@ -606,6 +629,47 @@
       "source": [
         "trainer.train()"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Trainer\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoModelForSequenceClassification\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TrainingArguments\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/__init__.py:26\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TYPE_CHECKING\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Check the dependencies satisfy the minimal versions required.\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dependency_versions_check\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     28\u001b[0m     OptionalDependencyNotAvailable,\n\u001b[1;32m     29\u001b[0m     _LazyModule,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     48\u001b[0m     logging,\n\u001b[1;32m     49\u001b[0m )\n\u001b[1;32m     52\u001b[0m logger \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mget_logger(\u001b[38;5;18m__name__\u001b[39m)  \u001b[38;5;66;03m# pylint: disable=invalid-name\u001b[39;00m\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/dependency_versions_check.py:16\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 The HuggingFace Team. All rights reserved.\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# See the License for the specific language governing permissions and\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# limitations under the License.\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdependency_versions_table\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deps\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m require_version, require_version_core\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m# define which module versions we always want to check at run time\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# (usually the ones defined in `install_requires` in setup.py)\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# order specific notes:\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# - tqdm must be checked before tokenizers\u001b[39;00m\n\u001b[1;32m     25\u001b[0m pkgs_to_check_at_runtime \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtqdm\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyyaml\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     38\u001b[0m ]\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/utils/__init__.py:34\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdoc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     27\u001b[0m     add_code_sample_docstrings,\n\u001b[1;32m     28\u001b[0m     add_end_docstrings,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m     replace_return_docstrings,\n\u001b[1;32m     33\u001b[0m )\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneric\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     35\u001b[0m     ContextManagers,\n\u001b[1;32m     36\u001b[0m     ExplicitEnum,\n\u001b[1;32m     37\u001b[0m     ModelOutput,\n\u001b[1;32m     38\u001b[0m     PaddingStrategy,\n\u001b[1;32m     39\u001b[0m     TensorType,\n\u001b[1;32m     40\u001b[0m     add_model_info_to_auto_map,\n\u001b[1;32m     41\u001b[0m     add_model_info_to_custom_pipelines,\n\u001b[1;32m     42\u001b[0m     cached_property,\n\u001b[1;32m     43\u001b[0m     can_return_loss,\n\u001b[1;32m     44\u001b[0m     expand_dims,\n\u001b[1;32m     45\u001b[0m     filter_out_non_signature_kwargs,\n\u001b[1;32m     46\u001b[0m     find_labels,\n\u001b[1;32m     47\u001b[0m     flatten_dict,\n\u001b[1;32m     48\u001b[0m     infer_framework,\n\u001b[1;32m     49\u001b[0m     is_jax_tensor,\n\u001b[1;32m     50\u001b[0m     is_numpy_array,\n\u001b[1;32m     51\u001b[0m     is_tensor,\n\u001b[1;32m     52\u001b[0m     is_tf_symbolic_tensor,\n\u001b[1;32m     53\u001b[0m     is_tf_tensor,\n\u001b[1;32m     54\u001b[0m     is_torch_device,\n\u001b[1;32m     55\u001b[0m     is_torch_dtype,\n\u001b[1;32m     56\u001b[0m     is_torch_tensor,\n\u001b[1;32m     57\u001b[0m     reshape,\n\u001b[1;32m     58\u001b[0m     squeeze,\n\u001b[1;32m     59\u001b[0m     strtobool,\n\u001b[1;32m     60\u001b[0m     tensor_size,\n\u001b[1;32m     61\u001b[0m     to_numpy,\n\u001b[1;32m     62\u001b[0m     to_py_obj,\n\u001b[1;32m     63\u001b[0m     torch_float,\n\u001b[1;32m     64\u001b[0m     torch_int,\n\u001b[1;32m     65\u001b[0m     transpose,\n\u001b[1;32m     66\u001b[0m     working_or_temp_dir,\n\u001b[1;32m     67\u001b[0m )\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     69\u001b[0m     CLOUDFRONT_DISTRIB_PREFIX,\n\u001b[1;32m     70\u001b[0m     HF_MODULES_CACHE,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     96\u001b[0m     try_to_load_from_cache,\n\u001b[1;32m     97\u001b[0m )\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mimport_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     99\u001b[0m     ACCELERATE_MIN_VERSION,\n\u001b[1;32m    100\u001b[0m     ENV_VARS_TRUE_AND_AUTO_VALUES,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    219\u001b[0m     torch_only_method,\n\u001b[1;32m    220\u001b[0m )\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/transformers/utils/generic.py:462\u001b[0m\n\u001b[1;32m    458\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mself\u001b[39m[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_torch_available():\n\u001b[0;32m--> 462\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pytree\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_torch_pytree\u001b[39;00m\n\u001b[1;32m    464\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_model_output_flatten\u001b[39m(output: ModelOutput) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[Any], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_torch_pytree.Context\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mvalues()), \u001b[38;5;28mlist\u001b[39m(output\u001b[38;5;241m.\u001b[39mkeys())\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/__init__.py:2118\u001b[0m\n\u001b[1;32m   2113\u001b[0m         backend \u001b[38;5;241m=\u001b[39m _TorchCompileWrapper(backend, mode, options, dynamic)\n\u001b[1;32m   2115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39moptimize(backend\u001b[38;5;241m=\u001b[39mbackend, nopython\u001b[38;5;241m=\u001b[39mfullgraph, dynamic\u001b[38;5;241m=\u001b[39mdynamic, disable\u001b[38;5;241m=\u001b[39mdisable)(model)\n\u001b[0;32m-> 2118\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m export \u001b[38;5;28;01mas\u001b[39;00m export\n\u001b[1;32m   2120\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_higher_order_ops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cond\n\u001b[1;32m   2122\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_register_device_module\u001b[39m(device_type, module):\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/export/__init__.py:64\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfx\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msymbolic_shapes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StrictMinMaxConstraint\n\u001b[1;32m     44\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     45\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConstraint\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDim\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnflattenedModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     61\u001b[0m ]\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdynamic_shapes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Constraint, Dim, dims, dynamic_dim, ShapesCollection\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexported_program\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ExportedProgram, ModuleCallEntry, ModuleCallSignature\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_signature\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ExportBackwardSignature, ExportGraphSignature\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/export/dynamic_shapes.py:19\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_pytree\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     12\u001b[0m     _get_node_type,\n\u001b[1;32m     13\u001b[0m     BUILTIN_TYPES,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     16\u001b[0m     tree_map,\n\u001b[1;32m     17\u001b[0m )\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexported_program\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ExportedProgram\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msympy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Symbol\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/export/exported_program.py:45\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfx\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpasses\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minfra\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpass_manager\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PassManager\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfx\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpasses\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mruntime_assert\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m insert_deferred_runtime_asserts\n\u001b[0;32m---> 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_signature\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m     46\u001b[0m     _sig_to_specs,\n\u001b[1;32m     47\u001b[0m     ArgumentSpec,\n\u001b[1;32m     48\u001b[0m     ConstantArgument,\n\u001b[1;32m     49\u001b[0m     CustomObjArgument,\n\u001b[1;32m     50\u001b[0m     ExportGraphSignature,\n\u001b[1;32m     51\u001b[0m     InputKind,\n\u001b[1;32m     52\u001b[0m     InputSpec,\n\u001b[1;32m     53\u001b[0m     OutputKind,\n\u001b[1;32m     54\u001b[0m     OutputSpec,\n\u001b[1;32m     55\u001b[0m     SymIntArgument,\n\u001b[1;32m     56\u001b[0m     TensorArgument,\n\u001b[1;32m     57\u001b[0m     TokenArgument,\n\u001b[1;32m     58\u001b[0m )\n\u001b[1;32m     61\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExportedProgram\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModuleCallEntry\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     64\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModuleCallSignature\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     65\u001b[0m ]\n\u001b[1;32m     68\u001b[0m PassType \u001b[38;5;241m=\u001b[39m Callable[[torch\u001b[38;5;241m.\u001b[39mfx\u001b[38;5;241m.\u001b[39mGraphModule], Optional[PassResult]]\n",
+            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/export/graph_signature.py:22\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Collection, Dict, List, Mapping, Optional, Set, Tuple, Union\n\u001b[1;32m      7\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConstantArgument\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCustomObjArgument\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTensorArgument\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     18\u001b[0m ]\n\u001b[1;32m     21\u001b[0m \u001b[38;5;129;43m@dataclasses\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataclass\u001b[49m\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;43;01mclass\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;21;43;01mTensorArgument\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;129m@dataclasses\u001b[39m\u001b[38;5;241m.\u001b[39mdataclass\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mTokenArgument\u001b[39;00m:\n",
+            "File \u001b[0;32m/usr/local/lib/python3.10/dataclasses.py:1184\u001b[0m, in \u001b[0;36mdataclass\u001b[0;34m(cls, init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots)\u001b[0m\n\u001b[1;32m   1181\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m wrap\n\u001b[1;32m   1183\u001b[0m \u001b[38;5;66;03m# We're called as @dataclass without parens.\u001b[39;00m\n\u001b[0;32m-> 1184\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrap\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m/usr/local/lib/python3.10/dataclasses.py:1175\u001b[0m, in \u001b[0;36mdataclass.<locals>.wrap\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m   1174\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrap\u001b[39m(\u001b[38;5;28mcls\u001b[39m):\n\u001b[0;32m-> 1175\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_process_class\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mrepr\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munsafe_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1176\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mfrozen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatch_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkw_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mslots\u001b[49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m/usr/local/lib/python3.10/dataclasses.py:1053\u001b[0m, in \u001b[0;36m_process_class\u001b[0;34m(cls, init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots)\u001b[0m\n\u001b[1;32m   1050\u001b[0m     self_tuple \u001b[38;5;241m=\u001b[39m _tuple_str(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m'\u001b[39m, flds)\n\u001b[1;32m   1051\u001b[0m     other_tuple \u001b[38;5;241m=\u001b[39m _tuple_str(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mother\u001b[39m\u001b[38;5;124m'\u001b[39m, flds)\n\u001b[1;32m   1052\u001b[0m     _set_new_attribute(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__eq__\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m-> 1053\u001b[0m                        \u001b[43m_cmp_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m__eq__\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m==\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1054\u001b[0m \u001b[43m                               \u001b[49m\u001b[43mself_tuple\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother_tuple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1055\u001b[0m \u001b[43m                               \u001b[49m\u001b[38;5;28;43mglobals\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mglobals\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m order:\n\u001b[1;32m   1058\u001b[0m     \u001b[38;5;66;03m# Create and set the ordering methods.\u001b[39;00m\n\u001b[1;32m   1059\u001b[0m     flds \u001b[38;5;241m=\u001b[39m [f \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m field_list \u001b[38;5;28;01mif\u001b[39;00m f\u001b[38;5;241m.\u001b[39mcompare]\n",
+            "File \u001b[0;32m/usr/local/lib/python3.10/dataclasses.py:629\u001b[0m, in \u001b[0;36m_cmp_fn\u001b[0;34m(name, op, self_tuple, other_tuple, globals)\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_cmp_fn\u001b[39m(name, op, self_tuple, other_tuple, \u001b[38;5;28mglobals\u001b[39m):\n\u001b[1;32m    624\u001b[0m     \u001b[38;5;66;03m# Create a comparison function.  If the fields in the object are\u001b[39;00m\n\u001b[1;32m    625\u001b[0m     \u001b[38;5;66;03m# named 'x' and 'y', then self_tuple is the string\u001b[39;00m\n\u001b[1;32m    626\u001b[0m     \u001b[38;5;66;03m# '(self.x,self.y)' and other_tuple is the string\u001b[39;00m\n\u001b[1;32m    627\u001b[0m     \u001b[38;5;66;03m# '(other.x,other.y)'.\u001b[39;00m\n\u001b[0;32m--> 629\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_create_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    630\u001b[0m \u001b[43m                      \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mself\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mother\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    631\u001b[0m \u001b[43m                      \u001b[49m\u001b[43m[\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mif other.__class__ is self.__class__:\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[43m                       \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m return \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mself_tuple\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mop\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mother_tuple\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    633\u001b[0m \u001b[43m                        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreturn NotImplemented\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    634\u001b[0m \u001b[43m                      \u001b[49m\u001b[38;5;28;43mglobals\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mglobals\u001b[39;49m\u001b[43m)\u001b[49m\n",
+            "File \u001b[0;32m/usr/local/lib/python3.10/dataclasses.py:432\u001b[0m, in \u001b[0;36m_create_fn\u001b[0;34m(name, args, body, globals, locals, return_type)\u001b[0m\n\u001b[1;32m    430\u001b[0m txt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdef __create_fn__(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlocal_vars\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtxt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m return \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    431\u001b[0m ns \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 432\u001b[0m \u001b[43mexec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtxt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mglobals\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ns[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__create_fn__\u001b[39m\u001b[38;5;124m'\u001b[39m](\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mlocals\u001b[39m)\n",
+            "File \u001b[0;32m<string>:1\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {
diff --git a/course/en/chapter3/trainer.py b/course/en/chapter3/trainer.py
new file mode 100644
index 00000000..206445e7
--- /dev/null
+++ b/course/en/chapter3/trainer.py
@@ -0,0 +1,41 @@
+import numpy as np
+import evaluate
+from datasets import load_dataset
+from transformers import AutoTokenizer, DataCollatorWithPadding, TrainingArguments, Trainer, AutoModelForSequenceClassification
+
+### dataset, checkpoint  
+raw_datasets = load_dataset("glue", "sst2")
+checkpoint = "bert-base-uncased"
+
+### tokenize
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+
+def tokenize_function(example):
+    return tokenizer(example["sentence"], truncation=True)
+
+tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
+data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+### metrics
+def compute_metrics(eval_preds):  # takes on EvalPrediction object
+    metric = evaluate.load("glue", "sst2")
+    logits, labels = eval_preds
+    predictions = np.argmax(logits, axis=-1)
+    return metric.compute(predictions=predictions, references=labels)
+
+### model
+training_args = TrainingArguments("test-trainer", evaluation_strategy="epoch", push_to_hub=False)
+model = AutoModelForSequenceClassification.from_pretrained(
+    checkpoint, num_labels=2)
+
+trainer = Trainer(
+    model,
+    training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+    data_collator=data_collator,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,  # <-- we need this to evaluate!!!
+)
+
+trainer.train()

From fabd85528da8ee0869743015220c9f11e80bacd3 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Wed, 18 Sep 2024 14:30:01 +0200
Subject: [PATCH 18/20] Now use in Colab...

---
 course/en/chapter3/section4.ipynb | 80 ++++++++++++++++++++++++-------
 1 file changed, 62 insertions(+), 18 deletions(-)

diff --git a/course/en/chapter3/section4.ipynb b/course/en/chapter3/section4.ipynb
index 397b34d1..d54aa5ef 100644
--- a/course/en/chapter3/section4.ipynb
+++ b/course/en/chapter3/section4.ipynb
@@ -16,21 +16,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!pip install accelerate\n",
+    "!pip install datasets evaluate transformers[sentencepiece] -q\n",
+    "!pip install accelerate -q\n",
     "# To run the training on TPU, you will need to uncomment the following line:\n",
     "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/vscode/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from datasets import load_dataset\n",
     "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
@@ -50,11 +59,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['labels', 'input_ids', 'token_type_ids', 'attention_mask']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
+    "tokenized_datasets = tokenized_datasets.remove_columns(\n",
+    "    [\"sentence1\", \"sentence2\", \"idx\"])\n",
     "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
     "tokenized_datasets.set_format(\"torch\")\n",
     "tokenized_datasets[\"train\"].column_names"
@@ -62,20 +83,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "[\"attention_mask\", \"input_ids\", \"labels\", \"token_type_ids\"]"
+    "from torch.utils.data import DataLoader\n",
+    "# help(DataLoader)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from torch.utils.data import DataLoader\n",
+    "\n",
     "\n",
     "train_dataloader = DataLoader(\n",
     "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
@@ -118,7 +140,8 @@
    "source": [
     "from transformers import AutoModelForSequenceClassification\n",
     "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    checkpoint, num_labels=2)"
    ]
   },
   {
@@ -202,7 +225,8 @@
    "source": [
     "import torch\n",
     "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "device = torch.device(\n",
+    "    \"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
     "model.to(device)\n",
     "device"
    ]
@@ -272,10 +296,12 @@
    "source": [
     "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
     "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    checkpoint, num_labels=2)\n",
     "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
     "\n",
-    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+    "device = torch.device(\n",
+    "    \"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
     "model.to(device)\n",
     "\n",
     "num_epochs = 3\n",
@@ -311,10 +337,11 @@
    "source": [
     "from accelerate import Accelerator\n",
     "from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n",
-    "\n",
+    "from tqdm import tqdm\n",
     "accelerator = Accelerator()\n",
     "\n",
-    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    checkpoint, num_labels=2)\n",
     "optimizer = AdamW(model.parameters(), lr=3e-5)\n",
     "\n",
     "train_dl, eval_dl, model, optimizer = accelerator.prepare(\n",
@@ -361,6 +388,23 @@
   "colab": {
    "name": "A full training",
    "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

From a1d39b92e56dae48258c16437a33fb26090c2a00 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Wed, 18 Sep 2024 15:27:18 +0200
Subject: [PATCH 19/20] ...

---
 course/en/chapter3/section4.ipynb | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/course/en/chapter3/section4.ipynb b/course/en/chapter3/section4.ipynb
index d54aa5ef..d3ffca7f 100644
--- a/course/en/chapter3/section4.ipynb
+++ b/course/en/chapter3/section4.ipynb
@@ -288,6 +288,13 @@
     "metric.compute()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Supercharge your training loop with 🤗 Accelerate "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From c7b6a1e6e3d9ac70b83a9a94f771da84f5d463e9 Mon Sep 17 00:00:00 2001
From: Neelan Pather <97616162+neelan-elucidate@users.noreply.github.com>
Date: Mon, 23 Sep 2024 10:03:38 +0000
Subject: [PATCH 20/20] setting up container correctly from `git`

---
 course/en/chapter4/section2_tf.ipynb |  88 ---------
 course/en/chapter4/section3_pt.ipynb |  83 ++++++--
 course/en/chapter4/section3_tf.ipynb | 282 ---------------------------
 3 files changed, 70 insertions(+), 383 deletions(-)
 delete mode 100644 course/en/chapter4/section2_tf.ipynb
 delete mode 100644 course/en/chapter4/section3_tf.ipynb

diff --git a/course/en/chapter4/section2_tf.ipynb b/course/en/chapter4/section2_tf.ipynb
deleted file mode 100644
index 0125cf84..00000000
--- a/course/en/chapter4/section2_tf.ipynb
+++ /dev/null
@@ -1,88 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using pretrained models (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[\n",
-       "  {'sequence': 'Le camembert est délicieux :)', 'score': 0.49091005325317383, 'token': 7200, 'token_str': 'délicieux'}, \n",
-       "  {'sequence': 'Le camembert est excellent :)', 'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent'}, \n",
-       "  {'sequence': 'Le camembert est succulent :)', 'score': 0.03453313186764717, 'token': 26202, 'token_str': 'succulent'}, \n",
-       "  {'sequence': 'Le camembert est meilleur :)', 'score': 0.0330314114689827, 'token': 528, 'token_str': 'meilleur'}, \n",
-       "  {'sequence': 'Le camembert est parfait :)', 'score': 0.03007650189101696, 'token': 1654, 'token_str': 'parfait'}\n",
-       "]"
-      ]
-     },
-     "execution_count": null,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "\n",
-    "camembert_fill_mask = pipeline(\"fill-mask\", model=\"camembert-base\")\n",
-    "results = camembert_fill_mask(\"Le camembert est <mask> :)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import CamembertTokenizer, TFCamembertForMaskedLM\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFCamembertForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import AutoTokenizer, TFAutoModelForMaskedLM\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\"camembert-base\")\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(\"camembert-base\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Using pretrained models (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/course/en/chapter4/section3_pt.ipynb b/course/en/chapter4/section3_pt.ipynb
index e1c8276e..4ad7a5d6 100644
--- a/course/en/chapter4/section3_pt.ipynb
+++ b/course/en/chapter4/section3_pt.ipynb
@@ -16,12 +16,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Reading package lists... Done\n",
+      "Building dependency tree... Done\n",
+      "Reading state information... Done\n",
+      "\u001b[1;31mE: \u001b[0mUnable to locate package git-lfs\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
+    "!pip install datasets evaluate transformers[sentencepiece] -q\n",
+    "!sudo apt install git-lfs"
    ]
   },
   {
@@ -33,12 +47,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "error: could not write config file /home/vscode/.gitconfig: Device or resource busy\n",
+      "error: could not write config file /home/vscode/.gitconfig: Device or resource busy\n"
+     ]
+    }
+   ],
    "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
+    "!git config --global user.email \"neelan.pather@gmail.com\"\n",
+    "!git config --global user.name \"Neelan Pather\""
    ]
   },
   {
@@ -50,9 +73,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b22f4dce24a14380b829e9a9450581dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from huggingface_hub import notebook_login\n",
     "\n",
@@ -72,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +168,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
+    "tokenizer.push_to_hub(\n",
+    "    \"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
    ]
   },
   {
@@ -205,7 +244,8 @@
    "source": [
     "from huggingface_hub import Repository\n",
     "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
+    "repo = Repository(\"<path_to_dummy_folder>\",\n",
+    "                  clone_from=\"<namespace>/dummy-model\")"
    ]
   },
   {
@@ -275,6 +315,23 @@
   "colab": {
    "name": "Sharing pretrained models (PyTorch)",
    "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/course/en/chapter4/section3_tf.ipynb b/course/en/chapter4/section3_tf.ipynb
deleted file mode 100644
index 8a9501b7..00000000
--- a/course/en/chapter4/section3_tf.ipynb
+++ /dev/null
@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sharing pretrained models (TensorFlow)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install datasets evaluate transformers[sentencepiece]\n",
-    "!apt install git-lfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need to setup git, adapt your email and name in the following cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!git config --global user.email \"you@example.com\"\n",
-    "!git config --global user.name \"Your Name\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "\n",
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import PushToHubCallback\n",
-    "\n",
-    "callback = PushToHubCallback(\n",
-    "    \"bert-finetuned-mrpc\", save_strategy=\"epoch\", tokenizer=tokenizer\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokenizer.push_to_hub(\"dummy-model\", organization=\"huggingface\", use_auth_token=\"<TOKEN>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import (\n",
-    "    # User management\n",
-    "    login,\n",
-    "    logout,\n",
-    "    whoami,\n",
-    "\n",
-    "    # Repository creation and management\n",
-    "    create_repo,\n",
-    "    delete_repo,\n",
-    "    update_repo_visibility,\n",
-    "\n",
-    "    # And some methods to retrieve/change information about the content\n",
-    "    list_models,\n",
-    "    list_datasets,\n",
-    "    list_metrics,\n",
-    "    list_repo_files,\n",
-    "    upload_file,\n",
-    "    delete_file,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import create_repo\n",
-    "\n",
-    "create_repo(\"dummy-model\", organization=\"huggingface\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import upload_file\n",
-    "\n",
-    "upload_file(\n",
-    "    \"<path_to_file>/config.json\",\n",
-    "    path_in_repo=\"config.json\",\n",
-    "    repo_id=\"<namespace>/dummy-model\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import Repository\n",
-    "\n",
-    "repo = Repository(\"<path_to_dummy_folder>\", clone_from=\"<namespace>/dummy-model\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()\n",
-    "repo.git_add()\n",
-    "repo.git_commit()\n",
-    "repo.git_push()\n",
-    "repo.git_tag()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_pull()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo.git_add()\n",
-    "repo.git_commit(\"Add model and tokenizer files\")\n",
-    "repo.git_push()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from transformers import TFAutoModelForMaskedLM, AutoTokenizer\n",
-    "\n",
-    "checkpoint = \"camembert-base\"\n",
-    "\n",
-    "model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)\n",
-    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
-    "\n",
-    "# Do whatever with the model, train it, fine-tune it...\n",
-    "\n",
-    "model.save_pretrained(\"<path_to_dummy_folder>\")\n",
-    "tokenizer.save_pretrained(\"<path_to_dummy_folder>\")"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "Sharing pretrained models (TensorFlow)",
-   "provenance": []
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}