03_BERT_HuggingFace.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "id": "a1597b20",
   "metadata": {},
   "source": [
    "Resource :\n",
    "- https://www.youtube.com/watch?v=DkzbCJtFvqM\n",
    "- https://www.youtube.com/watch?v=GSt00_-0ncQ\n",
    "- https://huggingface.co/transformers/v3.2.0/custom_datasets.html\n",
    "\n",
    "# I. Introduction\n",
    "\n",
    "Hugging face là một starup tập trung vào NLP, cung cấp số lượng lớn opensource liên quan tới NLP. Các opensource này được cung cấp thông qua lib tên *Transformer*\n",
    "\n",
    "*Transformer* cung cấp API để dễ sử dụng một số transformer architecture nổi tiếng như BERT, RoBERTa, GPT2 và DistilBERT.\n",
    "\n",
    "**Install**\n",
    "> pip install transformers==4.20.0 fugashi ipadic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9806bac5",
   "metadata": {},
   "source": [
    "## 1. Pipeline\n",
    " \n",
    "**Pipeline**\n",
    "Là class đơn giản nhất trong transformer, giúp dễ dàng download pretrain model để thực hiện predict. \n",
    "\n",
    "*Pretrained model được chia thành nhiều task khác nhau, cụ thể như sau :*\n",
    "- Sentiment analysis : Một đoạn text có nội dung positive or negative\n",
    "- Text generatation (English)\n",
    "- Name entity recognition : input 1 đoạn text, label từng từ vựng trong đó là person, place hay .v..v\n",
    "- Question answering : Input 1 đoạn câu hỏi, model trả về câu trả lời\n",
    "- Filling masked text : model điền vào đoạn text có chứa [mask]\n",
    "- Translation\n",
    "- Feature extraction : Return tensor thể hiện đoạn text input\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "43a715c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e483b5a4",
   "metadata": {},
   "source": [
    "Pretrained Model Sentiment analysis sẽ được donwload từ Huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8952595c",
   "metadata": {},
   "source": [
    "### 1.1. Task\n",
    "\n",
    "Pipeline cung cấp rất nhiều model thực hiện các công việc khác nhau như đã liệt kê bên trên.\n",
    "\n",
    "Để pipeline có thể load chính xác model cần thiết, ta define task name, ví dụ như bên dưới, task name = sentiment-analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f6e4f7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
     ]
    }
   ],
   "source": [
    "sentiment_model = pipeline(task=\"sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "785eeb41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9998606443405151}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_model(\"The pizza is quite good\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "044cdb1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'NEGATIVE', 'score': 0.9977707862854004}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_model(\"The pizza is really good but it's too expensive\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e3a286",
   "metadata": {},
   "source": [
    "**NOTE** : Sentiment-analysis task được cung cấp bởi nhiều model khác nhau. Bằng cách setting `model=<Tên model>`, ta có thể load chính xác model cần dùng trên Hugging Face.\n",
    "\n",
    "Nếu model = None, model có tên `distilbert-base-uncased-finetuned-sst-2-english` sẽ được tự động download về. Có thể tham khảo thông tin về model tại https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5a9d97",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.2. Model name\n",
    "\n",
    "*Ứng với từng task như `entiment-analysis` sẽ có rất nhiều pre-trained model với tính năng, tập dữ liệu, ngôn ngữ support khác nhau.\n",
    "Bằng cách chỉ định model name, pipeline sẽ download chính xác model cần sử dụng*\n",
    "\n",
    "**Note:**\n",
    "Với `model=None`, model default tương ứng với task được sử dụng.\n",
    "\n",
    "\n",
    "Ví dụ sau sử dụng model có tên `daigo/bert-base-japanese-sentiment`. Có thể tìm tên model phù hợp trên web của hugging face.\n",
    "https://huggingface.co/models?language=ja&sort=downloads&search=bert\n",
    "\n",
    "Lib này yêu cầu phải cài đặt `pip install ipadic`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "443f1fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_model_jp = pipeline(\"sentiment-analysis\", model=\"daigo/bert-base-japanese-sentiment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4497f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_model_jp(\"この店の雰囲気がかなり良い\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5fbd6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_model_jp(\"店員の態度が想像出来ない\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f47197c",
   "metadata": {},
   "source": [
    "## 2. Model and Tokenizer\n",
    "\n",
    "Pipeline sẽ tự động download 2 package gồm tokenizer và model, tuy nhiên để hiểu rõ hơn model hoạt động ra sao, ta có thể tách pipeline thành 2 phần :\n",
    "- `Tokenizer` : Tranform text input qua vector\n",
    "- `Model` : pre-trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "727bd6fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3a6d6fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name = \"daigo/bert-base-japanese-sentiment\")\n",
    "\n",
    "# Load Model\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name = \"daigo/bert-base-japanese-sentiment\")\n",
    "\n",
    "# Create pipeline \n",
    "sentiment_model_jp_custom = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a2faeb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_model_jp_custom(\"この店の雰囲気がかなり良い\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b4f62b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_model_jp_custom(\"この店の店員の態度が悪い\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2059f24d",
   "metadata": {},
   "source": [
    "### 2.1 Tokenizer\n",
    "\n",
    "Giúp convert raw text thành `sequences of tokens`, cụ thể như gồm các task sau\n",
    "- `tokenize` : Tách đoạn văn thành các tokens, ví dụ như words hoặc subwords\n",
    "- `Encode` : Convert từ tokens qua ID.\n",
    "\n",
    "Ví dụ\n",
    "> text =\"私が目指すのは、新しい資本主義の実現です。\"\n",
    "\n",
    "Tokenize\n",
    "> ['私','が','目指す','の','は','、', '新しい','資本','主義','の','実現','です',']\n",
    "\n",
    "Encode\n",
    "> [2,1325,14,5059,5,9,6,1842,4330,1008,5,2519,2992,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c20dff56",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer_output = tokenizer(\"この店の雰囲気がかなり良い\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78db95f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer_output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ef1f166",
   "metadata": {},
   "source": [
    "Tockenizer thực hiện các 2 **Steps** sau."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdf57ad",
   "metadata": {},
   "source": [
    "**Tokenizer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5022c7fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = tokenizer.tokenize(\"この店の雰囲気がかなり良い\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07d9a5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00e7ee0",
   "metadata": {},
   "source": [
    "**Encode**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ef13b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_ids = tokenizer.encode(\"この店の雰囲気がかなり良い\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ea74869",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens_ids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099dfb9e",
   "metadata": {},
   "source": [
    "**Note** : \n",
    "- Input text sẽ được add thêm 2 kí tự [cls] và [SEP] vào vị trí cuối. Ví dụ ['[CLS]','私','が','目指す','の','は','、','新しい','資本','主義','の','実現','です','。', [SEP]]\n",
    "\n",
    "- 2 kí từ [CLS] và [SEP] có id tương ứng với 2 và 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6369d4b3",
   "metadata": {},
   "source": [
    "- Sử dụng `padding, truncatetion set = true` để toàn bộ các đoạn văn đều có độ dài như nhau.\n",
    "- Return tensor, model chỉ có thể nhận input là tensor, vì vậy cần return tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "777ff3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer([\"この店の雰囲気がかなり良い\", \"この店の店員の態度が悪い\"], padding=True, truncation=True, max_length=512, return_tensors='pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c8795e",
   "metadata": {},
   "source": [
    "### 2.2 Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84d2c19",
   "metadata": {},
   "source": [
    "**Tìm hiểu cách thức hoạt động của model**\n",
    "\n",
    "Như giới thiệu bên trên, huggingface cung cấp model được build bởi cả 2 thư viện Pytorch và Tensorflow.\n",
    "Model ta sử dụng lần này là AutoModel, với task SequenceClassification. \n",
    "- Với model build bởi pytorch, `import AutoModelForSequenceClassification`\n",
    "- Với model build bởi tensor, `import TFAutoModelForSequenceClassification`\n",
    "\n",
    "Ví dụ này, ta sử dụng model được build bới Pytorch, vì vậy sử dụng `AutoModelForSequenceClassification`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbc96d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52efd93",
   "metadata": {},
   "source": [
    "**Step 1** : Tokenized input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ac460aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = [\n",
    "    \"この店の雰囲気がかなり良い\",\n",
    "    \"この店の店員の態度が悪い\"\n",
    "]\n",
    "\n",
    "batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors='pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4feef0",
   "metadata": {},
   "source": [
    "Tình huống không return_tensors=pt, ta có thể tự convert batch qua tensor bằng cách\n",
    "```\n",
    "batch = tokenizer(X_train, padding=True, truncation=True, max_length=512)\n",
    "batch = torch.tensor(batch[\"input_ids\"])\n",
    "\n",
    "...\n",
    "\n",
    "model(batch)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e97a99",
   "metadata": {},
   "source": [
    "**Step 2** : Input tokenized data to model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d35d493",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    outputs = model(**batch)\n",
    "    print(outputs)\n",
    "    \n",
    "    predictions = F.softmax(outputs.logits, dim=1)\n",
    "    print(predictions)\n",
    "    \n",
    "    labels = torch.argmax(predictions, dim=1)\n",
    "    print(labels)\n",
    "    \n",
    "    labels_name = [model.config.id2label.get(l_key) for l_key in labels.tolist()]\n",
    "    print(labels_name)"
   ]
  },
  {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "4c623643",
   "metadata": {},
   "source": [
    "Flows Xử lý model như sau \n",
    "![image.png](attachment:58e3646d-146d-4f42-9ee2-386312a66d25.png)\n",
    "\n",
    "Note : bản thân model lưu trữ các config như target encoder. Có thể get thông tin config của model bằng cách \n",
    "```\n",
    "model.config\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a0d19f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.config.id2label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ad8ab4",
   "metadata": {},
   "source": [
    "## 3. Save and load model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db843ad9",
   "metadata": {},
   "source": [
    "### 3.1 Save pretrain model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "763ccd13",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_pretrain_model_directory = \"pretrain-model\"\n",
    "\n",
    "model.save_pretrained(save_pretrain_model_directory)\n",
    "tokenizer.save_pretrained(save_pretrain_model_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558ba66b",
   "metadata": {},
   "source": [
    "### 3.2 Load pretrain model\n",
    "\n",
    "Thay vì download từ huggingface, có thể sử dụng model đã được lưu tại directory bên trên"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "645494cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(save_pretrain_model_directory)\n",
    "tokenizer = AutoTokenizer.from_pretrained(save_pretrain_model_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c10fcb-5f26-4f47-8685-5df8cc88a2f0",
   "metadata": {},
   "source": [
    "___________________________________________________"
   ]
  },
  {
   "attachments": {
    "830d0232-5e5b-452c-8963-036debd5205d.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d3524041-bb0d-41f9-9b28-ee86e2a76850",
   "metadata": {},
   "source": [
    "# II. Dataset of HuggingFace\n",
    "\n",
    "https://huggingface.co/docs/datasets/index\n",
    "\n",
    "Huggingface cung cấp class dataset giúp :\n",
    "- Dễ dàng download và sharing dataset gồm audio, text và images\n",
    "- Các method evaluation\n",
    "- Xử lý data thực hiện quá trình fine-tuning\n",
    "\n",
    "> `pip install datasets`\n",
    "\n",
    "Để sử dụng hết tính năng của dataset, cần tìm hiểu :\n",
    "\n",
    "![image.png](attachment:830d0232-5e5b-452c-8963-036debd5205d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c83ab766-6d70-44d9-9605-9047bd361115",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Onsiter\\.conda\\envs\\nlp\\lib\\site-packages\\tqdm\\auto.py:22: 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 datasets import Dataset\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import pandas as pd\n",
    "from transformers import AutoTokenizer\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cde779b-1f73-40a0-a8bf-5cecabbe2721",
   "metadata": {},
   "source": [
    "## 1. Dataset of Huggingface\n",
    "\n",
    "Huggingface cung cấp class Dataset giúp đơn giản quá trình chuẩn bị data.\n",
    "\n",
    "4 Steps sau hướng dẫn cách sử dụng class Dataset của Huggingface, tạo ra dataset để có thể thực hiện fine-tuning model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3d10d7-f1cf-4f9d-aede-7985a2e427d2",
   "metadata": {},
   "source": [
    "**Step 1. Load Csv File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0cbddd4c-108a-48d2-bd5d-866af7b47927",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv(\"data/spam.csv\", encoding='cp1252')[[\"v1\", \"v2\"]].rename(columns={\"v1\": 'label', 'v2': 'text'})\n",
    "df_raw = df_raw.sample(100).reset_index(drop=True)\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "df_raw['label'] = encoder.fit_transform(df_raw.label.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1195eec1-e1f6-49df-9ef7-ebd9b68151ce",
   "metadata": {},
   "source": [
    "**Step 2. Use Loaddata**\n",
    "\n",
    "Cho phép load data từ nhiều nguồn, trong đó đơn giản nhất load từ pandas\n",
    "```\n",
    "https://huggingface.co/docs/datasets/loading\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "19d7ef84-535a-4e38-b016-344611088ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['label', 'text'],\n",
       "    num_rows: 100\n",
       "})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_raw = Dataset.from_pandas(df_raw)\n",
    "dataset_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0a22c963-ba58-462c-814a-ff5c8ffa2eac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'label': 0, 'text': 'Haha just kidding, papa needs drugs'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_raw[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c024b7ca-fe55-4c6e-8d8a-4854f520e2f2",
   "metadata": {},
   "source": [
    "**Step 3. Tokenizer dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a8ce4f92-fce7-462e-b9e0-be9b9b40e8b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'distilbert-base-uncased'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "\n",
    "def tokenization(sample):\n",
    "    return tokenizer(sample['text'], return_tensors='tf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c1e8c465-c78c-4377-8986-bf5b5278e461",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 2919.69ex/s]\n"
     ]
    }
   ],
   "source": [
    "dataset_tokenized_raw = dataset_raw.map(tokenization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "39be6990-a814-40f0-9e8f-c69c4869a433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['label', 'text', 'input_ids', 'attention_mask'],\n",
       "    num_rows: 100\n",
       "})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_tokenized_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e5456ce6-8dce-4132-8515-bbfc174813ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['label', 'text', 'input_ids', 'attention_mask'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_tokenized_raw[0].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d73f05f9-c712-437a-9d15-16262d6dd8df",
   "metadata": {},
   "source": [
    "**Step 4. Convert to pytorch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f2762f2d-a13c-4959-bfe9-72f11b85b56b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_tokenized_torch_raw = dataset_tokenized_raw\n",
    "dataset_tokenized_torch_raw.set_format(\n",
    "    type=\"torch\", \n",
    "    columns=[\"input_ids\", \"attention_mask\", \"label\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "21597def-eaad-4cc6-b5a7-9ad5cee45533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['label', 'text', 'input_ids', 'attention_mask'],\n",
       "    num_rows: 100\n",
       "})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_tokenized_torch_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7eeb80c4-161b-4fe2-96df-da13d316b932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'label': tensor(0),\n",
       " 'input_ids': [tensor([  101,  5292,  3270,  2074, 12489,  1010, 13008,  3791,  5850,   102])],\n",
       " 'attention_mask': [tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])]}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_tokenized_torch_raw[0]"
   ]
  },
  {
   "attachments": {
    "fe7ad76c-efae-4bb8-bbcc-ce7251cd74f3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "e8e4d977",
   "metadata": {},
   "source": [
    "# III. Fine-tuning\n",
    "\n",
    "***Tuning Flows :***\n",
    "1. Prepare dataset.\n",
    "2. Load pretrained Tokenizer\n",
    "3. Build Pytorch Dataset\n",
    "4. Load pretrained model\n",
    "5. Tuning *Có 2 phương pháp tuning khác nhau*\n",
    "    \n",
    "    5.1 Load Trainer và train (Phương pháp đơn giản hóa mà hugging face cung cấp)\n",
    "    \n",
    "    5.2 Sử dụng Pytorch hoặc Tensorflow để thực hiện train (Phương pháp phức tạp hơn nhưng customize dễ dàng)\n",
    "    \n",
    "    \n",
    "Tuning Doc:\n",
    "- https://huggingface.co/docs/transformers/training\n",
    "- https://huggingface.co/transformers/v3.3.1/training.html **Best explain about tunning**\n",
    "\n",
    "![image.png](attachment:fe7ad76c-efae-4bb8-bbcc-ce7251cd74f3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb54044",
   "metadata": {},
   "source": [
    "## 1. Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dad33cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "695453be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    \"\"\"\n",
    "    Load spam, split random data to Train, Test\n",
    "    \"\"\"\n",
    "    df_raw = pd.read_csv(\"data/spam.csv\", encoding='cp1252')[[\"v1\", \"v2\"]]\n",
    "    df_raw.rename(columns={\"v1\": \"label\", \"v2\": \"sentences\"}, inplace=True)\n",
    "\n",
    "    # Encode label\n",
    "    df_raw['label_encoded'] = df_raw.label.apply(lambda x: int(x=='spam'))\n",
    "\n",
    "    # Split train and test\n",
    "    X_train, X_test, y_train, y_test = train_test_split(df_raw[\"sentences\"], df_raw[\"label_encoded\"], test_size=0.2) \n",
    "    \n",
    "    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1)\n",
    "    \n",
    "    return df_raw, X_train.tolist(), X_val.tolist(), X_test.tolist(), y_train.tolist(), y_val.tolist(), y_test.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0c1f599",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw, X_train, X_val, X_test, y_train, y_val, y_test = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "64a1fe25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ham     4825\n",
       "spam     747\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41dca901-d56d-4803-abf9-0853929e0f72",
   "metadata": {},
   "source": [
    "## 2. Fine-tune\n",
    "\n",
    "https://huggingface.co/docs/transformers/training\n",
    "\n",
    "**Có 3 phương pháp có thể fine-tune model.**\n",
    "- Trainer. Class do transformer cung cấp.\n",
    "- Pytorch. Sử dụng pytorch thực hiện tuning.\n",
    "- Tensorflow. Sử dụng tensor thực hiện tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb7a8a9-82e1-4342-8541-df307e1cc29d",
   "metadata": {},
   "source": [
    "### 2.1 Trainer \n",
    "\n",
    "*Hugging face cung cấp function `Trainer`, là cách đơn giản nhất để thực hiện Fine-tune model*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84b91025-427d-4323-b726-dd599c2d4ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a30b0414-235c-4a03-bfbf-0cdaf6a27a43",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_projector.bias']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.bias', 'pre_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": [
    "model_name = 'distilbert-base-uncased'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a4423a-e008-4a8d-a9cf-25c8bd0b07a8",
   "metadata": {},
   "source": [
    "#### 2.1.1 Processing Data"
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "66888bd7-02ec-42be-afdc-c9b8ce7580d1",
   "metadata": {},
   "source": [
    "![image.png](attachment:376363cc-0573-4e3c-943d-581d165a1c61.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "409a95cf-bff4-438b-999d-f4cb3daf9ddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TorchDataSet(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "        \n",
    "        self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]).to(self.device) for key, val in self.encodings.items()}\n",
    "        item['labels'] = torch.tensor(self.labels[idx]).to(self.device)\n",
    "        return item\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "113e624f-c696-44bc-ba00-d7d82c754fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenize_train = tokenizer(X_train, truncation=True, padding=True)\n",
    "tokenize_val = tokenizer(X_val, truncation=True, padding=True)\n",
    "tokenize_test = tokenizer(X_test, truncation=True, padding=True)\n",
    "\n",
    "torch_train_set = TorchDataSet(tokenize_train, y_train)\n",
    "torch_val_set = TorchDataSet(tokenize_val, y_val)\n",
    "torch_test_set = TorchDataSet(tokenize_test, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9a0c1f-88a1-4e59-a297-adadf2b71a56",
   "metadata": {},
   "source": [
    "#### 2.1.2 Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c4e353-06f0-4300-b5dc-d600531e689d",
   "metadata": {},
   "source": [
    "Importance Training Arguments\n",
    "- auto_find_batch_size : Tự động tìm kiếm batch size phù hợp với environment. Required `pip install accelerate`\n",
    "- seed, data_seed : G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0d690a5-cb3f-4e31-a0bc-4ccdf68a3803",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"results\", \n",
    "    evaluation_strategy=\"epoch\",\n",
    "    per_device_train_batch_size=8,  # batch size per device during training\n",
    "    per_device_eval_batch_size=8,   # batch size for evaluation\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "942dacbf-6e85-4120-b5f2-d150e05a16a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=torch_train_set,\n",
    "    eval_dataset=torch_val_set\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6df1baaf-38f9-4d19-9841-159b7ec89c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Onsiter\\.conda\\envs\\nlp\\lib\\site-packages\\transformers\\optimization.py:309: 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",
      "  FutureWarning,\n",
      "***** Running training *****\n",
      "  Num examples = 4011\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1506\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1506' max='1506' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1506/1506 02:21, 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.073800</td>\n",
       "      <td>0.083816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.027100</td>\n",
       "      <td>0.040672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.062362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to results\\checkpoint-500\n",
      "Configuration saved in results\\checkpoint-500\\config.json\n",
      "Model weights saved in results\\checkpoint-500\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to results\\checkpoint-1000\n",
      "Configuration saved in results\\checkpoint-1000\\config.json\n",
      "Model weights saved in results\\checkpoint-1000\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to results\\checkpoint-1500\n",
      "Configuration saved in results\\checkpoint-1500\\config.json\n",
      "Model weights saved in results\\checkpoint-1500\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1506, training_loss=0.036255549197456244, metrics={'train_runtime': 143.7033, 'train_samples_per_second': 83.735, 'train_steps_per_second': 10.48, 'total_flos': 688026613230684.0, 'train_loss': 0.036255549197456244, 'epoch': 3.0})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db955c0-4e73-49d1-9890-6371435cc6c1",
   "metadata": {},
   "source": [
    "#### 2.1.3 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c269da81-bc6f-4b41-b3aa-d1c720b7b694",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='168' max='56' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [56/56 00:09]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 2.294200897216797,\n",
       " 'eval_runtime': 1.1598,\n",
       " 'eval_samples_per_second': 384.562,\n",
       " 'eval_steps_per_second': 48.286,\n",
       " 'epoch': 3.0}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate(torch_test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fe98c19e-c61e-4796-8322-c8f87715d103",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Prediction *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[320,  56],\n",
       "       [ 64,   6]], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_values = trainer.predict(torch_test_set)[0]\n",
    "\n",
    "y_predict = np.argmax(predict_values, axis=-1)\n",
    "\n",
    "confusion_matrix(torch_test_set.labels, y_predict)"
   ]
  },
  {
   "attachments": {
    "30eb0c89-7e9a-4bd9-9fd8-760569f18739.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "210fce3f-bab7-41e2-bbbc-648060ebf4a4",
   "metadata": {},
   "source": [
    "#### 2.1.4 Advance\n",
    "\n",
    "Trainer gồm nhiều function như bên dưới, dựa vào các tình huống cụ thể, có thể overwrite function của trainer.\n",
    "\n",
    "![image.png](attachment:30eb0c89-7e9a-4bd9-9fd8-760569f18739.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc7bd87-3c7e-4876-89ee-015a661b3e85",
   "metadata": {},
   "source": [
    "##### 2.1.4.1 Customize compute loss\n",
    "*Do data bị unbalance, vì vậy tỉ lệ đoán đúng ở TP cao, nhưng FP rất thấp, vì vậy cần customize loss function bằng cách đưa tăng weight cho class FP*\n",
    "\n",
    "\n",
    "**NOTE**\n",
    "- Make sure model and any variable is set to run by GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e08d536-b0f0-4ac2-8da2-0329482a5c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158aa52a-718a-4e07-a99a-3467453ee200",
   "metadata": {},
   "source": [
    "**Step 1. Customize Trainer Class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "96b76ebb-9048-484a-a51c-98c172c52c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomTrainer(Trainer):\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        # Fit input to model and get logist\n",
    "        output = model(**inputs)\n",
    "        logits = output.get('logits')\n",
    "        \n",
    "        # Extract label from input\n",
    "        labels = inputs.get(\"labels\")\n",
    "        \n",
    "        # Lost func calculation\n",
    "        weight = torch.from_numpy(np.array([0.8, 0.2])).float().cuda()\n",
    "        loss_func = nn.CrossEntropyLoss(weight=weight)\n",
    "        \n",
    "        loss = loss_func(logits, labels)\n",
    "        \n",
    "        return (loss, output) if  return_outputs else loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504e2095-4901-4061-a8cc-0e15cd7ab005",
   "metadata": {},
   "source": [
    "**Step 2. Load Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8b15bf0-6230-4555-9ccf-6f9d594456e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'distilbert-base-uncased'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
    "\n",
    "device = torch.device(str(\"cuda:0\") if torch.cuda.is_available() else \"cpu\") \n",
    "model = model.to(device) # the network should be on GPU, next(model.parameters()).is_cuda==False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b19e81-de2b-4972-b9bd-e8bd4a7af765",
   "metadata": {},
   "source": [
    "**Step 3. Tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50179cbc-4334-4191-bbac-0845c18fb866",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"results1\", \n",
    "    evaluation_strategy=\"epoch\",\n",
    "    per_device_train_batch_size=8,  # batch size per device during training\n",
    "    per_device_eval_batch_size=8,   # batch size for evaluation\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d5b7e96b-9f76-48f7-9c28-155f6744f61b",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = CustomTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=torch_train_set,\n",
    "    eval_dataset=torch_val_set\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d139ca65-81b4-4457-8be4-ceae1bbae3e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running training *****\n",
      "  Num examples = 4011\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1506\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1506' max='1506' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1506/1506 02:28, 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.049100</td>\n",
       "      <td>0.009871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.014404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.002200</td>\n",
       "      <td>0.015966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to results1\\checkpoint-500\n",
      "Configuration saved in results1\\checkpoint-500\\config.json\n",
      "Model weights saved in results1\\checkpoint-500\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to results1\\checkpoint-1000\n",
      "Configuration saved in results1\\checkpoint-1000\\config.json\n",
      "Model weights saved in results1\\checkpoint-1000\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to results1\\checkpoint-1500\n",
      "Configuration saved in results1\\checkpoint-1500\\config.json\n",
      "Model weights saved in results1\\checkpoint-1500\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1506, training_loss=0.01970740364478582, metrics={'train_runtime': 148.5832, 'train_samples_per_second': 80.985, 'train_steps_per_second': 10.136, 'total_flos': 740951737325352.0, 'train_loss': 0.01970740364478582, 'epoch': 3.0})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337aba32-0346-4554-8261-dc997fe29cb9",
   "metadata": {},
   "source": [
    "**Step 4. Evaluation**\n",
    "\n",
    "Ta thấy f1 score đã có kết quả tốt hơn trước."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0047ebdc-7453-401a-ac70-1991cff9b71f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Prediction *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[346,  47],\n",
       "       [ 39,  14]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_values = trainer.predict(torch_test_set)[0]\n",
    "\n",
    "y_predict = np.argmax(predict_values, axis=-1)\n",
    "\n",
    "confusion_matrix(torch_test_set.labels, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30fe5b7d-7fd1-4210-afbd-2447f35ae138",
   "metadata": {},
   "source": [
    "##### 2.1.4.2 Customize compute metric\n",
    "\n",
    "*Customize metric return tại từng Epoch*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f44d17-8461-4d74-b0d5-f0165f0e5b7a",
   "metadata": {},
   "source": [
    "**Step 1. Customize metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0a4a9077-a47a-47d8-964c-43e338951278",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
    "\n",
    "def compute_metrics(p):\n",
    "    pred, labels = p\n",
    "    pred = np.argmax(pred, axis=1)\n",
    "\n",
    "    accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
    "    recall = recall_score(y_true=labels, y_pred=pred)\n",
    "    precision = precision_score(y_true=labels, y_pred=pred)\n",
    "    f1 = f1_score(y_true=labels, y_pred=pred)\n",
    "\n",
    "    return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2e0d1d-2ccf-4ae8-9297-4a13d9276f5f",
   "metadata": {},
   "source": [
    "**Step 2. Run Finetune**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7be4a3a-6ca7-4d7b-82f9-b17b26e84176",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
      "***** Running training *****\n",
      "  Num examples = 4011\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1506\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1506' max='1506' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1506/1506 02:30, 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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.023704</td>\n",
       "      <td>0.991031</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.961538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.006500</td>\n",
       "      <td>0.035410</td>\n",
       "      <td>0.991031</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>0.960784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.004500</td>\n",
       "      <td>0.032776</td>\n",
       "      <td>0.991031</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924528</td>\n",
       "      <td>0.960784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to results\\checkpoint-500\n",
      "Configuration saved in results\\checkpoint-500\\config.json\n",
      "Model weights saved in results\\checkpoint-500\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to results\\checkpoint-1000\n",
      "Configuration saved in results\\checkpoint-1000\\config.json\n",
      "Model weights saved in results\\checkpoint-1000\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to results\\checkpoint-1500\n",
      "Configuration saved in results\\checkpoint-1500\\config.json\n",
      "Model weights saved in results\\checkpoint-1500\\pytorch_model.bin\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 446\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1506, training_loss=0.006296726676924521, metrics={'train_runtime': 150.5634, 'train_samples_per_second': 79.92, 'train_steps_per_second': 10.002, 'total_flos': 740951737325352.0, 'train_loss': 0.006296726676924521, 'epoch': 3.0})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    del trainer\n",
    "    \n",
    "    import mlflow\n",
    "    mlflow.end_run()\n",
    "except:\n",
    "    pass\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"results\", \n",
    "    evaluation_strategy=\"epoch\",\n",
    "    per_device_train_batch_size=8,  # batch size per device during training\n",
    "    per_device_eval_batch_size=8,   # batch size for evaluation\n",
    ")\n",
    "\n",
    "trainer = CustomTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=torch_train_set,\n",
    "    eval_dataset=torch_val_set,\n",
    "    compute_metrics=compute_metrics\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aafbfc77-b9b0-45c4-8980-9996e1d790d2",
   "metadata": {},
   "source": [
    "##### 2.1.4. Eary Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "340844b7-4633-4b16-ac65-7f551ac11ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import EarlyStoppingCallback\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='./results3',          # output directory\n",
    "    num_train_epochs=3,              # total number of training epochs\n",
    "    per_device_train_batch_size=16,  # batch size per device during training\n",
    "    per_device_eval_batch_size=64,   # batch size for evaluation\n",
    "    warmup_steps=500,                # number of warmup steps for learning rate scheduler\n",
    "    weight_decay=0.01,               # strength of weight decay\n",
    "    logging_dir='./logs',            # directory for storing logs\n",
    "    logging_steps=10,\n",
    "    load_best_model_at_end=True,     # Early stopping yêu cầu phải được set = True.\n",
    "    evaluation_strategy = 'steps',   # Sử dụng evaluation step thay vì `epoch`\n",
    "    \n",
    ")\n",
    "\n",
    "trainer = WeightedLossTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=torch_train_set,\n",
    "    eval_dataset=torch_val_set,\n",
    "    compute_metrics=compute_metrics,\n",
    "    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0425ccc9",
   "metadata": {},
   "source": [
    "### 2.2 PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da181359-9bb1-438e-a673-a172a29d96c8",
   "metadata": {},
   "source": [
    "#### 2.2.1 Processing Data"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "id": "a26c62c4",
   "metadata": {},
   "source": [
    "- `DataLoader` giúp split Tourch Data Set thành nhiều batch size khác nhau. Ví dụ bên dưới, ta set `batch_size = 8`. Mỗi batch sẽ chứa 8 samples.\n",
    "\n",
    "```\n",
    "for train_set in dataloader_train_set:\n",
    "    print(train_set.get('input_ids').shape)\n",
    "    \n",
    ">> torch.Size([8, 238])\n",
    "```\n",
    "\n",
    "![image.png](attachment:88e1ce99-318d-4a77-bf40-92ee4906ef5f.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8d85f50-6187-45f6-b95e-d38cd0a69096",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa2f4799-176d-4b1d-be39-a06d06742e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenize_train = tokenizer(X_train, truncation=True, padding=True)\n",
    "tokenize_val = tokenizer(X_val, truncation=True, padding=True)\n",
    "tokenize_test = tokenizer(X_test, truncation=True, padding=True)\n",
    "\n",
    "torch_train_set = TorchDataSet(tokenize_train, y_train)\n",
    "torch_val_set = TorchDataSet(tokenize_val, y_val)\n",
    "torch_test_set = TorchDataSet(tokenize_test, y_val)\n",
    "\n",
    "dataloader_train = DataLoader(torch_train_set, batch_size=8, shuffle=True)\n",
    "dataloader_val = DataLoader(torch_val_set, batch_size=8, shuffle=True)\n",
    "dataloader_test = DataLoader(torch_test_set, batch_size=8, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac2ca8b-7e29-4977-a4cf-cd720c4d8b34",
   "metadata": {},
   "source": [
    "#### 2.2.2 Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "008fab27-9925-49f0-86f1-4e234a0e9eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting training\n",
    "epochs_num = 3\n",
    "learning_rate = 5e-5\n",
    "\n",
    "# Load Model\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "model.to(device)\n",
    "\n",
    "# Optimizer\n",
    "optim = torch.optim.AdamW(model.parameters(), lr=5e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f8fd96b-ebb9-42df-85da-2a155be78298",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.train()\n",
    "\n",
    "for epoch in range(epochs_num):\n",
    "    for batch in dataloader_train:\n",
    "        optim.zero_grad()\n",
    "\n",
    "        outputs = model(**batch)\n",
    "        \n",
    "        loss = outputs[0]\n",
    "        loss.backward()\n",
    "        optim.step()\n",
    "\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b71b93-6ed5-4a4d-bffa-7cc2e4691030",
   "metadata": {},
   "source": [
    "#### 2.2.3 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6c0395c6-a16b-420a-81bb-4c80781f8bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "592f0592-aac6-4610-8190-fb7d49f72a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = []\n",
    "\n",
    "for data in DataLoader(torch_test_set, batch_size=1):\n",
    "    output = model(**data)\n",
    "    y_pred = torch.argmax(output.logits)\n",
    "    \n",
    "    y_predict.append(y_pred.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3ff64af6-9e07-4ccf-9d6d-f06d7b8a116a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7488789237668162"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(torch_test_set.labels, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a5089e38-6175-4759-8d3a-089bfcc90171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[326,  49],\n",
       "       [ 63,   8]], dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(torch_test_set.labels, y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2113bd4b-909f-4313-84f3-fecf38293eb5",
   "metadata": {},
   "source": [
    "### 2.3 Tensorflow"
   ]
  },
  {
   "attachments": {
    "c66cb252-961e-47d1-85d2-9384b75e0667.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "dfee7319-b2c2-4f58-8eb7-5aed7d56647d",
   "metadata": {},
   "source": [
    "#### 2.3.1 Processing Data\n",
    "\n",
    "Có 2 phương pháp Processing data.\n",
    "- Sử dụng function Dataset do tensorflow cung cấp.  Hướng dẫn https://huggingface.co/transformers/v3.2.0/custom_datasets.html\n",
    "- Sử dụng function dataset do HunggingFace cung cấp. Hướng dẫn https://huggingface.co/docs/transformers/training\n",
    "![image.png](attachment:c66cb252-961e-47d1-85d2-9384b75e0667.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "be98cb80-db5b-4ced-b36d-71aa98c649dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_transform', 'vocab_layer_norm', 'vocab_projector', 'activation_13']\n",
      "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier', 'dropout_173', 'classifier']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from transformers import AutoTokenizer,TFAutoModelForSequenceClassification, Trainer, TrainingArguments\n",
    "\n",
    "model_name = 'distilbert-base-uncased'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5b3a5cad-784d-43b5-a008-a0590b3e036d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenize_train = tokenizer(X_train, truncation=True, padding=True)\n",
    "tokenize_val = tokenizer(X_val, truncation=True, padding=True)\n",
    "tokenize_test = tokenizer(X_test, truncation=True, padding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "5d22d95b-bba9-4da1-87e7-daf197a32417",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor_train = tf.data.Dataset.from_tensor_slices((\n",
    "    dict(tokenize_train),\n",
    "    y_train\n",
    "))\n",
    "\n",
    "tensor_val = tf.data.Dataset.from_tensor_slices((\n",
    "    dict(tokenize_val),\n",
    "    y_val\n",
    "))\n",
    "\n",
    "tensor_test = tf.data.Dataset.from_tensor_slices((\n",
    "    dict(tokenize_test),\n",
    "    y_test\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b41e70-52cf-4e0c-9b88-e26ead4ab5d9",
   "metadata": {},
   "source": [
    "#### 2.3.2 Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "85894565-ebd0-4e99-bc3f-e43cd0aec9f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=tf.metrics.SparseCategoricalAccuracy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8229b314-7c21-4a9d-ad3f-7b41bf9a491f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "251/251 [==============================] - 54s 215ms/step - loss: 0.0093 - sparse_categorical_accuracy: 0.9978 - val_loss: 0.0697 - val_sparse_categorical_accuracy: 0.9843\n",
      "Epoch 2/2\n",
      "251/251 [==============================] - 58s 232ms/step - loss: 0.0020 - sparse_categorical_accuracy: 0.9995 - val_loss: 0.0684 - val_sparse_categorical_accuracy: 0.9865\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x169982cd148>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    tensor_train.shuffle(1000).batch(16),\n",
    "    validation_data=tensor_val.shuffle(1000).batch(16),\n",
    "    epochs=2, \n",
    "    batch_size=16\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74241598-a896-4d9f-bb01-2744ca25d6f1",
   "metadata": {},
   "source": [
    "#### 2.3.3 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "00d79880-ff77-4baf-a581-a096aec25178",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1115/1115 [==============================] - 10s 9ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[973,   6],\n",
       "       [  5, 131]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_values = model.predict(tensor_test)\n",
    "\n",
    "predict_values = predict_values.logits\n",
    "\n",
    "y_predict_class = tf.argmax(predict_values, axis=1)\n",
    "\n",
    "confusion_matrix(y_test, y_predict_class)"
   ]
  },
  {
   "attachments": {
    "432e3c25-09e0-40d9-84ef-0fb226c16e94.png": {
     "image/png": 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"
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"
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "4d43bfda-bd36-4b85-ab24-0c57371d6bb6",
   "metadata": {},
   "source": [
    "# IV. Feature Base\n",
    "\n",
    "Doc: http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/\n",
    "\n",
    "*Là phương pháp sử dụng pre-trained model như một bước tiền xử lý dữ liệu, output vector sẽ được dùng để train model.*\n",
    "\n",
    "![image.png](attachment:432e3c25-09e0-40d9-84ef-0fb226c16e94.png)\n",
    "\n",
    "Features base đơn giản được chia thành 3 step như sau : \n",
    "\n",
    "\n",
    "**STEP1** Sentence Embedding\n",
    "\n",
    "![image.png](attachment:dd59b875-d253-4725-8a87-61c883e2b41f.png)\n",
    "\n",
    "\n",
    "**STEP 2** Sentence Embedding được chia thành train test\n",
    "\n",
    "![image.png](attachment:46773e65-dc56-4544-b732-518743166b9b.png)\n",
    "\n",
    "\n",
    "**STEP 3** TRAINING\n",
    "\n",
    "![image.png](attachment:ff462b98-fc0a-4bcf-9bb8-d055fc4e37ed.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9f5e79-a8c8-416f-b253-74c142eba72b",
   "metadata": {},
   "source": [
    "## 1. Use Pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58b95381-19d1-4803-ade5-6c7836d99420",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Onsiter\\.conda\\envs\\nlp\\lib\\site-packages\\tqdm\\auto.py:22: 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 AutoTokenizer, AutoModelForSequenceClassification, BertConfig\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb0ba89d-5e4c-4539-8fea-f929cbe3a5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'distilbert-base-uncased'\n",
    "         \n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "\n",
    "config = BertConfig.from_pretrained(model_name, output_hidden_states=True, return_dict=True)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name, config=config)\n",
    "\n",
    "# device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "device = torch.device('cpu')\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609cdb31-582b-4314-baeb-26cc79e84c79",
   "metadata": {},
   "source": [
    "### 1.1 Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e08274a3-0698-4845-8644-59c8205e8e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TorchDataSet(torch.utils.data.Dataset):\n",
    "    def __init__(self, input_ids):\n",
    "        self.input_ids = input_ids\n",
    "        # self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "        self.device = torch.device('cpu')\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        item = {'input_ids': torch.tensor(self.input_ids[idx]).to(self.device) }\n",
    "        return item\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.input_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bbbe488f-878a-4a33-b019-cee30b1ded5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_X_train = tokenizer(X_train, padding=True, truncation=True)\n",
    "tokenized_X_test =  tokenizer(X_test, padding=True, truncation=True)\n",
    "\n",
    "torch_set_train = TorchDataSet(tokenized_X_train.get('input_ids'))\n",
    "torch_set_test = TorchDataSet(tokenized_X_test.get('input_ids'))\n",
    "\n",
    "dataloader_train = DataLoader(torch_set_train, batch_size=128, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "117d4aba-69a6-405f-b0a3-75608f2cf344",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = []\n",
    "for i, bath in enumerate(dataloader_train):\n",
    "    \n",
    "    _out = model(**bath)\n",
    "    _last_layer = _out.hidden_states[-1]\n",
    "    _cls_vector = _last_layer[:, 0, :]\n",
    "    \n",
    "    outputs.append(_cls_vector)\n",
    "    \n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650062ea-bbb4-4b27-9906-c5e6b2f030cb",
   "metadata": {},
   "source": [
    "### 1.2 Build Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d007976e-5c2a-408a-849b-0b0931d64e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "94730e5a-5d81-4cc1-8c6d-59d479fe8305",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = last_layer_cls_emb.cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f9aed3cf-c4b6-41e0-a47f-8fd406d3db76",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y_train[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fcf8aadc-37e9-448e-b9ac-43145bd4b41a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Onsiter\\.conda\\envs\\nlp\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=0).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5542a195-e315-4ab4-b196-1ed6a14634f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6a9ef0-3519-48c4-907b-a4da671d455f",
   "metadata": {},
   "source": [
    "## 2. Use Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a5adbb7-25a4-4678-a4f9-a5db0ea3602f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Onsiter\\.conda\\envs\\nlp\\lib\\site-packages\\tqdm\\auto.py:22: 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",
      "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_projector', 'activation_13', 'vocab_transform', 'vocab_layer_norm']\n",
      "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier', 'pre_classifier', 'dropout_19']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from transformers import AutoTokenizer,TFAutoModelForSequenceClassification, Trainer, TrainingArguments\n",
    "\n",
    "model_name = 'distilbert-base-uncased'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4b2f8fb-46c0-47e0-af7d-ea42607ea157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"tf_distil_bert_for_sequence_classification\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " distilbert (TFDistilBertMai  multiple                 66362880  \n",
      " nLayer)                                                         \n",
      "                                                                 \n",
      " pre_classifier (Dense)      multiple                  590592    \n",
      "                                                                 \n",
      " classifier (Dense)          multiple                  1538      \n",
      "                                                                 \n",
      " dropout_19 (Dropout)        multiple                  0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 66,955,010\n",
      "Trainable params: 66,955,010\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c9d55a8-6602-40e5-9ece-1a3eb9def212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input layers\n",
    "input_ids = tf.keras.layers.Input(shape=(512,), name=\"input_ids\", dtype=\"int32\")\n",
    "attention_mask = tf.keras.layers.Input(shape=(512, ), name=\"attention_mask\", dtype=\"int32\")\n",
    "\n",
    "# Transform Layer\n",
    "embeddings = model.distilbert(input_ids, attention_mask=attention_mask)[1]\n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "92f61b8c-5f32-4d1c-a4c8-92850aea9cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertMainLayer at 0x1abe0b5dc48>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.distilbert"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea0c669-ad39-4ef5-8208-72a0eecf3496",
   "metadata": {},
   "source": [
    "__________________________\n",
    "\n",
    "# V. Deep dive models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e6ce14c-f819-46ec-9cac-b9995e0a8ac7",
   "metadata": {},
   "source": [
    "## 1. Model\n",
    "Ứng với mỗi models Input và Ouput có thể sẽ khác nhau, tuy nhiên cũng sẽ có điểm chung. \n",
    "\n",
    "Ta sẽ tìm hiểu Input và Output chi tiết tại chương này. \n",
    "\n",
    "Tham khảo inputs variable tại url : https://huggingface.co/docs/transformers/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5ba37916-d709-478b-a50b-485fde029297",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_projector', 'activation_13', 'vocab_transform', 'vocab_layer_norm']\n",
      "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['dropout_39', 'classifier', 'pre_classifier']\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 TFAutoModelForSequenceClassification\n",
    "\n",
    "model_name = 'distilbert-base-uncased'\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "382c2922-2d9c-4c97-b78d-7d80678bb1ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.summary()"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "c5d74a25-8b6c-4104-b108-e6fbe88fc467",
   "metadata": {},
   "source": [
    "![image.png](attachment:17a4b7b0-ccd5-470f-8a53-33206daadf4b.png)\n",
    "\n",
    "- Model gồm 4 layers, trong đó `BERT` layer nằm ở layer đầu tiên\n",
    "- Access tới layer thông qua `model.layers` hoặc `model.<layer_name>` ví dụ `model.distilbert`\n",
    "- Weight layer `model.layers[0].weights`\n",
    "- Khi thực hiện tuning, để tránh update weight, có thể thực hiện `model.layers[0].trainable = False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f047c011-86cd-4cd7-9a8b-3654d1ac8389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30522"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2b622513-01e0-4538-b28a-628c5c30988a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertMainLayer at 0x1ad8ee54448>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ecbaa234-a27e-4630-b9f9-b5cb9c2facf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertMainLayer at 0x1ad8ee54448>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.distilbert"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e07d2a1-cd41-4f52-a70f-8a4bf8f9a400",
   "metadata": {},
   "source": [
    "Model nhận 3 params quan trọng:\n",
    "- input_ids : input id được tokenized\n",
    "- attention_mask : nhận được từ tokenized\n",
    "- labels : Not required. Nếu None, loss của model sẽ None. Nếu được truyền vào, loss model sẽ được tính toán, từ giá trị loss nhận được, có thể update weight cho model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db169254-bdf6-4d74-b9f7-41d78a8a0510",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_batch = [\"I love Pixar.\", \"I don't care for Pixar.\"]\n",
    "label = [0, 1]\n",
    "\n",
    "tf_label = tf.cast(label, tf.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1fcd65-17fe-42be-8025-af3668107548",
   "metadata": {},
   "source": [
    "**Raw Data**\n",
    "- Cho 2 câu [\"I love Pixar.\", \"I don't care for Pixar.\"], label [0,1] tương ứng positive và negative\n",
    "\n",
    "**Transform**\n",
    "*Do model là dạng Tensorflow, vì vậy cần return về dạng tensor, có 2 cách thực hiện*:\n",
    "- Cách 1 : tokenizer sử dụng `return_tensors='tf'`\n",
    "- Cách 2 : Sử dụng `tf.cast()` để convert về data type như mong muốn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "893b7c39-119a-4b39-b8ca-20ab7eb5ff06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cách 1.\n",
    "inputs1 = tokenizer(text_batch, return_tensors='tf', padding=True, truncation=True, )\n",
    "outputs1 = model(**inputs1, labels=tf_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c16357a0-34fa-4994-a54c-c3224e0b632f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cách 2. \n",
    "\n",
    "encoding = tokenizer(text_batch, padding=True, truncation=True, )\n",
    "\n",
    "inputs2 = {\n",
    "    'input_ids': tf.cast(encoding.get(\"input_ids\"), tf.int32),\n",
    "    'attention_mask': tf.cast(encoding.get(\"attention_mask\"), tf.int32),\n",
    "    'labels': tf.cast(label, tf.int32)\n",
    "\n",
    "}\n",
    "\n",
    "outputs2 = model(**inputs2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8401eda2-39d8-420b-bc2d-7c569b22df95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.6960371 , 0.68417794], dtype=float32)>, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
       "array([[0.04468593, 0.05045751],\n",
       "       [0.03155107, 0.04957069]], dtype=float32)>, hidden_states=None, attentions=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6f8cf0a1-5218-457c-990f-75a01ad9f16f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.6960371 , 0.68417794], dtype=float32)>, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
       "array([[0.04468593, 0.05045751],\n",
       "       [0.03155107, 0.04957069]], dtype=float32)>, hidden_states=None, attentions=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a39ff70c-2f7b-4421-a9a9-0ebef77fcbef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TFBaseModelOutput(last_hidden_state=<tf.Tensor: shape=(2, 12, 768), dtype=float32, numpy=\n",
       "array([[[ 3.4019709e-02, -6.6927578e-03, -1.4624323e-01, ...,\n",
       "         -1.0526595e-01,  4.8749053e-01,  2.8431541e-01],\n",
       "        [ 5.2832651e-01,  2.6760536e-01, -1.8289202e-01, ...,\n",
       "         -8.8303629e-04,  7.4926734e-01,  3.6566862e-01],\n",
       "        [ 1.0713220e+00,  6.4043432e-01,  5.7274568e-01, ...,\n",
       "         -2.3331147e-01,  5.8694959e-01,  1.5197034e-01],\n",
       "        ...,\n",
       "        [ 8.2670435e-02,  5.2127380e-02, -9.8093830e-02, ...,\n",
       "          1.7210686e-01,  2.4491163e-01,  1.0410643e-01],\n",
       "        [ 2.4761711e-01,  6.2976196e-02,  1.9212425e-02, ...,\n",
       "          7.6376408e-02,  2.5096276e-01, -2.0748209e-02],\n",
       "        [ 9.3785554e-02,  7.0093192e-02, -9.7792983e-02, ...,\n",
       "          1.8248136e-01,  2.1425895e-01,  1.2580812e-01]],\n",
       "\n",
       "       [[ 1.7257513e-01,  1.2390751e-01, -8.4526688e-02, ...,\n",
       "          1.3114606e-02,  5.5684346e-01,  3.3941603e-01],\n",
       "        [ 4.7622269e-01,  2.9056025e-01, -3.1635219e-01, ...,\n",
       "          1.6961768e-01,  5.8902955e-01,  3.9600712e-01],\n",
       "        [ 2.2462727e-01,  3.7315616e-01,  1.3315143e-01, ...,\n",
       "          2.3506325e-02,  2.9870480e-01,  7.1196519e-03],\n",
       "        ...,\n",
       "        [ 1.4967691e-02, -7.6013729e-02, -2.5683388e-01, ...,\n",
       "          3.9468843e-02,  7.8828484e-01, -2.0935594e-01],\n",
       "        [ 5.7030684e-01, -7.4458942e-03, -5.9440964e-01, ...,\n",
       "          3.2803109e-01, -1.3513890e-01, -4.0103450e-01],\n",
       "        [ 4.0126097e-01,  1.8461984e-01,  1.0285180e-01, ...,\n",
       "          7.3854461e-02,  5.2884912e-01,  2.3018671e-03]]], dtype=float32)>, hidden_states=None, attentions=None)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.distilbert(**inputs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3b01570a-7ab4-46e0-83fc-bb66fcab39f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TFBaseModelOutput(last_hidden_state=<tf.Tensor: shape=(2, 12, 768), dtype=float32, numpy=\n",
       "array([[[ 3.4019709e-02, -6.6927578e-03, -1.4624323e-01, ...,\n",
       "         -1.0526595e-01,  4.8749053e-01,  2.8431541e-01],\n",
       "        [ 5.2832651e-01,  2.6760536e-01, -1.8289202e-01, ...,\n",
       "         -8.8303629e-04,  7.4926734e-01,  3.6566862e-01],\n",
       "        [ 1.0713220e+00,  6.4043432e-01,  5.7274568e-01, ...,\n",
       "         -2.3331147e-01,  5.8694959e-01,  1.5197034e-01],\n",
       "        ...,\n",
       "        [ 8.2670435e-02,  5.2127380e-02, -9.8093830e-02, ...,\n",
       "          1.7210686e-01,  2.4491163e-01,  1.0410643e-01],\n",
       "        [ 2.4761711e-01,  6.2976196e-02,  1.9212425e-02, ...,\n",
       "          7.6376408e-02,  2.5096276e-01, -2.0748209e-02],\n",
       "        [ 9.3785554e-02,  7.0093192e-02, -9.7792983e-02, ...,\n",
       "          1.8248136e-01,  2.1425895e-01,  1.2580812e-01]],\n",
       "\n",
       "       [[ 1.7257513e-01,  1.2390751e-01, -8.4526688e-02, ...,\n",
       "          1.3114606e-02,  5.5684346e-01,  3.3941603e-01],\n",
       "        [ 4.7622269e-01,  2.9056025e-01, -3.1635219e-01, ...,\n",
       "          1.6961768e-01,  5.8902955e-01,  3.9600712e-01],\n",
       "        [ 2.2462727e-01,  3.7315616e-01,  1.3315143e-01, ...,\n",
       "          2.3506325e-02,  2.9870480e-01,  7.1196519e-03],\n",
       "        ...,\n",
       "        [ 1.4967691e-02, -7.6013729e-02, -2.5683388e-01, ...,\n",
       "          3.9468843e-02,  7.8828484e-01, -2.0935594e-01],\n",
       "        [ 5.7030684e-01, -7.4458942e-03, -5.9440964e-01, ...,\n",
       "          3.2803109e-01, -1.3513890e-01, -4.0103450e-01],\n",
       "        [ 4.0126097e-01,  1.8461984e-01,  1.0285180e-01, ...,\n",
       "          7.3854461e-02,  5.2884912e-01,  2.3018671e-03]]], dtype=float32)>, hidden_states=None, attentions=None)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.distilbert(**inputs1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f02b6d-e7d0-4fc6-a918-13f84db81184",
   "metadata": {},
   "source": [
    "**OUTPUT**\n",
    "- `logits` : Giá trị dự đoán ứng với từng samples.\n",
    "- `loss` : Nếu nhiều hơn 1 class, cross-entropy sẽ được sử dụng để tính loss.\n",
    "- `hidden_state`, `attentions` = None. Hidden state và attentions không được return. Sẽ tìm hiểu trong phần 2 về cách extract hidden state cũng như attentions layer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e205c7a4-ac72-484a-9204-901dcfe996a1",
   "metadata": {},
   "source": [
    "## 2. Extract hidden state of model\n",
    "\n",
    "Có thể lấy Hidden State của model bằng 2 cách :\n",
    "- Sử dụng `BertConfig`.\n",
    "- Thay vì sử dụng toàn model architecture của Bert model, sử dụng duy nhất mình `BERT Layer`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b41b4c1-256e-4809-9129-64d9bb2b1903",
   "metadata": {},
   "source": [
    "**Cách 1** Sử dụng BERT Config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e19a23-163b-4d1f-9417-4544ec87fd89",
   "metadata": {},
   "source": [
    "**Step1:** Load model with config `output_hiddent_states = True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd1bd43d-8153-4d76-89b0-26aa5574be7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertConfig,TFAutoModelForSequenceClassification\n",
    "\n",
    "model_name = 'distilbert-base-uncased'\n",
    "\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "\n",
    "config = BertConfig.from_pretrained(model_name, output_hidden_states=True, return_dict=True)\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(model_name, config=config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab0e8d8-b10d-41b7-9e2a-d08de24a0679",
   "metadata": {},
   "source": [
    "**Step 2** Prepare sample data, input into models\n",
    "\n",
    "⚠　**Note :** Model Tensorflow yêu cầu `intput_ids` và `attention_mask` phải được convert qua tensor type, sử dụng `tf.cast(<input>, <data type>)` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ba0eee1a-4144-40c6-9c18-751977efcf5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_batch = [\"I love Pixar.\", \"I don't care for Pixar.\"]\n",
    "encoding = tokenizer(text_batch, return_tensors='pt', padding=True, truncation=True, )\n",
    "\n",
    "input_ids = tf.cast(encoding.get('input_ids'), tf.int32)\n",
    "attention_mask = tf.cast(encoding.get(\"attention_mask\"), tf.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a73b849e-49bd-44da-a4a6-e3004e743091",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = model(input_ids=input_ids, attention_mask=attention_mask)"
   ]
  },
  {
   "attachments": {
    "c1f72194-7aa9-4163-850b-b9df92c6f231.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d46ac9f0-9a6b-4f11-a93e-a341c35a721f",
   "metadata": {},
   "source": [
    "**Step 3** Extract hidden state.\n",
    "\n",
    "- Hidden state được lưu tại output[1].\n",
    "- Length hidden state = `13`. Trong đó layer đầu tiên là `embedding vector`, 12 layer còn lại là `attention hidden states`\n",
    "\n",
    "![image.png](attachment:c1f72194-7aa9-4163-850b-b9df92c6f231.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "89a36869-096d-4076-a86d-d4294a0d3e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of layer  13\n",
      "Embedding output length : 1. Shape is  (2, 12, 768)\n",
      "Hiddent state length :  12  Shape of each layer is :  (2, 12, 768)\n"
     ]
    }
   ],
   "source": [
    "hidden_states = output[1]\n",
    "embedding_output = hidden_states[0]\n",
    "attention_hidden_states = hidden_states[1:]\n",
    "\n",
    "print(\"Total number of layer \", len(hidden_states)) \n",
    "\n",
    "print(\"Embedding output length : 1. Shape is \", embedding_output.shape)\n",
    "\n",
    "print(\"Hiddent state length : \", len(attention_hidden_states), \" Shape of each layer is : \", attention_hidden_states[0].shape)"
   ]
  },
  {
   "attachments": {
    "8e0d8c31-592d-452d-9ee7-a811e769a5a1.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8f8e0e14-cb5c-43e1-b70f-370062f92cbb",
   "metadata": {},
   "source": [
    "**Chi tiết dimention tại hiddent layer**\n",
    "\n",
    "- Dimension max length of word tùy thuộc vào độ dài của tập Train mà sẽ được quyết định. Ví dụ trong các sample, có sample độ dài 238, khi đó 238 sẽ đưuọc xem là độ dài tối đa.\n",
    "\n",
    "![image.png](attachment:8e0d8c31-592d-452d-9ee7-a811e769a5a1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3f3c362-f981-47b5-b6b9-acbf9da24cef",
   "metadata": {},
   "source": [
    "**Cách 2**: Sử dụng BERT layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "804a38ad-eb96-4e97-b822-5eb1d430fba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_only_bert_layer = model.distilbert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "bb9cf169-cbee-40b7-b044-fb423e95ed63",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = model_only_bert_layer(input_ids=input_ids, attention_mask=attention_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "5b6baa3d-96e3-4883-9b2b-1e671cc2ead8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_only_bert_layer.num_hidden_layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "48420026-97bb-4173-99be-a160ee481b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<transformers.models.distilbert.modeling_tf_distilbert.TFEmbeddings at 0x1ad8eec1708>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_only_bert_layer.embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21e8f34a-abde-4976-8f58-83d298be0cea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8bf453b6-055a-4e11-85ee-81d8e0735a59",
   "metadata": {},
   "source": [
    "## QUESTION LIST : \n",
    "- Q1. Diffirent between BERT Based model with other model like DistilBERT Model, RobertA\n",
    "- Q2. How Huggingface model was builded ? \n",
    "\n",
    "- Q3. Is Weight of model will be changed after run tuning model ? \n",
    "- Q4. How to add dense layer into last layer\n",
    "- Q5. How optimizer model works ? \n",
    "- Q6. Is imposible to customize loss function and otpimizer function also ? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82e343e-26c4-4b04-bfb0-ce837c417172",
   "metadata": {},
   "source": [
    "_____________\n",
    "> Q3. Is Weight of model will be changed after run tuning model ?\n",
    "\n",
    "**Ans**: Có 2 phương pháp chính để sử dụng pre-trained model\n",
    "- feature-based : Sử dụng model như công cụ extract features\n",
    "- fine-tuning : Sử dụng lượng sample data nhỏ nhằm thay đổi weight của model.\n",
    "\n",
    "Đối với phương án fine-tuning, chắc chắn weight của model sẽ thay đổi. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "aec6305c-9799-4529-b088-75dd5c517c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = model.weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "9d288e40-32b1-4d57-b77f-2fcc5b4dfd98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "c4047ad5-7164-470c-a553-f9f65cbc7c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([30522, 768])"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "0603002f-d9bb-4596-8f5a-801b8cfc0f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([512, 768])"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "c7dd2e95-badb-46c8-845f-dfcaa256b8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([768])"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "f3be0377-6ac5-4ce1-bc64-354856456aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([768, 768])"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights[10].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "64da1ad1-6f5c-47e6-b4ca-83cd05467a56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"tf_distil_bert_for_sequence_classification_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " distilbert (TFDistilBertMai  multiple                 66362880  \n",
      " nLayer)                                                         \n",
      "                                                                 \n",
      " pre_classifier (Dense)      multiple                  590592    \n",
      "                                                                 \n",
      " classifier (Dense)          multiple                  1538      \n",
      "                                                                 \n",
      " dropout_233 (Dropout)       multiple                  0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 66,955,010\n",
      "Trainable params: 66,955,010\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c5e34d-2405-45a0-847b-27162f4d8743",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f950c043-baf7-4001-98e8-9208563208af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c099e0f6-1e76-409e-9912-77cfa1381a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e99aa0d4-3033-4e41-a590-9b024aade57f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp.append(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50ad8ebb-d7af-41dc-adfa-cc1b9941b632",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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