fine_tuning_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/loganathanspr/nlp_course/blob/main/fine_tuning_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNDrR2P1_HuB"
      },
      "source": [
        "# Fine-tuning a model with the Trainer API or Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhDjBAB8_HuC"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8OPFNiIy_HuD",
        "outputId": "f1540f75-e250-45a0-d44c-2f0eedbd8f2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n",
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            "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.17.2 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.2 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[torch]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kxk51L67BtMC",
        "outputId": "d2f7f903-72a7-4ed3-c1c2-50a485dc7d09"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.23.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uDpCFMfCvUP",
        "outputId": "7cd85aa6-3a13-4227-bee5-3313a3a1355b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2197663ffaf8415989dfde78362a6683",
            "9d6d125181014d8fac5d8a60eeb00d58",
            "ade9905f1d094ae785a1416b4f2f0a26",
            "043e94bdb8b143f29c0464c8d5f495b7",
            "5389cb97a7c842b1b2ad9ab20d5eff99",
            "c1e19e0e018441989bc4295a7d6004ef",
            "29b6715b66794ea2a286283a79271478",
            "d9552df7bc494cfebb179db167a2f1c5",
            "636ad9cd70304b48af16094008e4f1ae",
            "b05cedc4c6b54177b7051bd27807b0e2",
            "91d15347f48c421d89858f65e7cc5525"
          ]
        },
        "id": "SdqarpnH_HuD",
        "outputId": "74399bda-e4e0-4552-b23e-305877641af8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2197663ffaf8415989dfde78362a6683"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(example):\n",
        "  return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eDA2suQSCB92",
        "outputId": "8316c534-4311-446f-bf37-4abd8a323250"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0_BtgEUi_HuD"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "training_args = TrainingArguments(\"test-trainer\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "4de75bb4e3bb4cdda2dffdf96d57674f",
            "82bdefb7828346cb99db04057be06d7e",
            "1c6a1f4f8a9a410889f38226ef534e1c",
            "8913ba59ff7648498e18f105e0a69340",
            "1acb684b51374091ba9830e619b6266f",
            "4df6af7e8f334aeabf43176df289dc5e",
            "1a8a39f5bb2b4007a29d04688e7c2c54",
            "aa6dfafd48294f2a950b3760c63e4ec6",
            "c6c058eaa2ca4a68abd94e8dce35cb49",
            "8cb98748d5d047bebdcfa65ff97d2989",
            "472d042042f346399962261bda6a4567"
          ]
        },
        "id": "BzHwAz31_HuE",
        "outputId": "7d78ca29-08e0-45e9-8831-94d5a5e59fa2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4de75bb4e3bb4cdda2dffdf96d57674f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-7WljNl4_HuE"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "7ZBB5i-F_HuE",
        "outputId": "a5a7b0a4-8571-4d30-af68-17b1a6a34cdd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1377/1377 03:26, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.501000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.265100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1377, training_loss=0.3107989552926567, metrics={'train_runtime': 209.8905, 'train_samples_per_second': 52.427, 'train_steps_per_second': 6.561, 'total_flos': 405324636337200.0, 'train_loss': 0.3107989552926567, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "MEiFTKOM_HuE",
        "outputId": "b077299d-2abf-4c9b-abce-10066005c12b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(408, 2) (408,)\n"
          ]
        }
      ],
      "source": [
        "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
        "print(predictions.predictions.shape, predictions.label_ids.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24bYhWmgLmQK",
        "outputId": "007983b8-2b8f-486d-9945-6817823370cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_loss': 0.6561302542686462,\n",
              " 'test_runtime': 2.4182,\n",
              " 'test_samples_per_second': 168.723,\n",
              " 'test_steps_per_second': 21.09}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xt_q3rT_HuF",
        "outputId": "313dc790-c4c9-4583-e4fb-63c3bbad1d8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0,\n",
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              "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
              "       1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
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              "       1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "import numpy as np\n",
        "preds = np.argmax(predictions.predictions, axis=-1)\n",
        "preds"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "metric = evaluate.load(\"glue\", \"mrpc\")\n",
        "metric.compute(predictions=preds, references=predictions.label_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "445e29b715aa4290be597ed5dbfac4a4",
            "a0c5970f837a4105bf8c83d82c391365",
            "63b3d2fb001b4adda5e1423ef5830a61",
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            "d781ff4e5d8d46e29eb8e40e1faaa2da",
            "0db92304679a47849e69e033bbe477e8",
            "16d8f6bafd5b4c92a6bc87f83c506ea0",
            "a8865368db6346b48ec68da58cc0edef",
            "8ed6c63bd4494b57a8412fcb9ee3b0df",
            "1850ab18a3bb4608999465bc250b90bd",
            "172d8c1e2d9e4494b7df14eec63729c4"
          ]
        },
        "id": "Wpe9aJouMm7k",
        "outputId": "80baaac9-883b-4f2c-fa75-00285b2d69ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "445e29b715aa4290be597ed5dbfac4a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.8627450980392157, 'f1': 0.903114186851211}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v_iM1nkx_HuF"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_preds):\n",
        "  metric = evaluate.load(\"glue\", \"mrpc\")\n",
        "  logits, labels = eval_preds\n",
        "  predictions = np.argmax(logits, axis=-1)\n",
        "  return metric.compute(predictions=predictions, references=labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3hWbHTL_HuF",
        "outputId": "5a57481c-ebca-470a-95fe-a75abbc4ebed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "-JPwhG6q_HuF",
        "outputId": "09bcf012-5044-44fd-f8fe-1586cdf194f9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1377/1377 03:41, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.446707</td>\n",
              "      <td>0.796569</td>\n",
              "      <td>0.844860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.557100</td>\n",
              "      <td>0.537062</td>\n",
              "      <td>0.808824</td>\n",
              "      <td>0.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.363300</td>\n",
              "      <td>0.564370</td>\n",
              "      <td>0.850490</td>\n",
              "      <td>0.893170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1377, training_loss=0.4059399762011648, metrics={'train_runtime': 222.1279, 'train_samples_per_second': 49.539, 'train_steps_per_second': 6.199, 'total_flos': 405540469624800.0, 'train_loss': 0.4059399762011648, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Finetuning SST2"
      ],
      "metadata": {
        "id": "3HkoivcVHPzC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "raw_datasets_train = load_dataset(\"glue\", \"sst2\", split=\"train[:100]\")\n",
        "raw_datasets_validation = load_dataset(\"glue\", \"sst2\", split=\"validation\")\n",
        "raw_datasets_test = load_dataset(\"glue\", \"sst2\", split=\"test\")"
      ],
      "metadata": {
        "id": "h4EWv2c0N5EY"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets_train, raw_datasets_validation, raw_datasets_test"
      ],
      "metadata": {
        "id": "fQlbKNu-JrsB",
        "outputId": "1ebad039-799f-490e-c806-40872dd37685",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset({\n",
              "     features: ['sentence', 'label', 'idx'],\n",
              "     num_rows: 100\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['sentence', 'label', 'idx'],\n",
              "     num_rows: 872\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['sentence', 'label', 'idx'],\n",
              "     num_rows: 1821\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets_train.features"
      ],
      "metadata": {
        "id": "2H7isdUPJ04w",
        "outputId": "0fe962e4-ce9f-4cd0-fc3d-7d2002082f47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['negative', 'positive'], id=None),\n",
              " 'idx': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets_train[:5]"
      ],
      "metadata": {
        "id": "xDdDR_jcJ6EL",
        "outputId": "a6c51ba7-6f0e-49a2-f09e-599bac61aa49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence': ['hide new secretions from the parental units ',\n",
              "  'contains no wit , only labored gags ',\n",
              "  'that loves its characters and communicates something rather beautiful about human nature ',\n",
              "  'remains utterly satisfied to remain the same throughout ',\n",
              "  'on the worst revenge-of-the-nerds clichés the filmmakers could dredge up '],\n",
              " 'label': [0, 0, 1, 0, 0],\n",
              " 'idx': [0, 1, 2, 3, 4]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "def tokenize_function(example):\n",
        "  return tokenizer(example[\"sentence\"], truncation=True)"
      ],
      "metadata": {
        "id": "cXqCyGIRKGr5",
        "outputId": "565824eb-7756-427b-81a1-ea17e5efd399",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "f8531ca31a20451395e06892e8ea1128",
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            "d984dbefd0664299a6974e7d38ffdc08",
            "3a86d5733a5e44268c7e3aa5ba932075",
            "425bd4431bc944f385430b4600fc8564",
            "7605234c546747998c09811c67eb0cd3",
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            "3d6d141146734b4bbdf8193bba2b8bf5",
            "5b15584a6fd54f78a340850274b16b71",
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            "100f2daaa09147bb9045eb55545670da",
            "15dd2a887d2e48c0b808349cbf40f141",
            "4ff7d10c05014415a228e730ef8c156d",
            "b8f7e204e59947f684469375082d7528",
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            "29c3af08927840ce851cb62ce1f1ef97",
            "f7c313c694b54db19703341f6b25f658",
            "0ab787e3eceb42b59670bf747df0a08f",
            "d2014dd6b06f4d61bc6791e52caf16c3",
            "107cac36dffc4056ae5f87f1af808605",
            "2c411902f59644c5acfcb8d92f0cd702",
            "30cc6e5566434585a53e547dbc5216ad",
            "ed2069008fa34b6e82378e2908e9e67d",
            "cf2a96de4a5c4c72b26ef2e24ce343e6",
            "dafd4a4352574e129939bd8a4cee2f1f",
            "40d6bd6969c442ef80adff3a015fc8d0",
            "f5571d9e32c1454c93ba6e3c95e8a946",
            "e0902307af3943fd8962a80c45348b31",
            "27598962d99747d3827b1cf4ed9d3059",
            "d15fc33035df4ce4a5174e8dbf6ae98a",
            "8241239ab6af47ca9e2cfc6a61ce55c7",
            "0c4705443a5644e98eb21b462a8c118d",
            "597762000a5742158e333028742f5a4d",
            "d319e743cbb74a7499b77080665ae544",
            "be0116e2db9540b2a746e12357004d32",
            "61dc57128a464998a81cdfc6952c9b2f",
            "cd953031582d41a4827d5cdffd61db02",
            "8cc6e4ed000f45f88097875aa3f78a8b",
            "fc1859172a754526b299bab033e95712",
            "b5b373b6de08494eb4a6c080d3d32dfc",
            "7a4808a05d45407fbe9084f293529b90"
          ]
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8531ca31a20451395e06892e8ea1128"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b15584a6fd54f78a340850274b16b71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "107cac36dffc4056ae5f87f1af808605"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8241239ab6af47ca9e2cfc6a61ce55c7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets_train = raw_datasets_train.map(tokenize_function, batched=True)\n",
        "tokenized_datasets_val = raw_datasets_validation.map(tokenize_function, batched=True)\n",
        "tokenized_datasets_test = raw_datasets_test.map(tokenize_function, batched=True)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "FbJ0-3P7KzRT",
        "outputId": "8de631dd-2afa-486e-eb68-dad37b56e0b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "4ad841e80177465fac8f9674ee06a67a",
            "49d4f1376ef54fc7b86c68ee401880e7",
            "96b1e4ddd2864572a258cf571d7a3b1e",
            "c8e8ae0c9f234b3e90f46f86dd2b2acc",
            "4b76a0a2d3ef4a26b82b251dc7c4fa97",
            "cae429bb809e443d95afac8427d36ab6",
            "657db037278f4a2387d79f010813a65f",
            "a8396da33daa40e4a987c6fa6b25a1a0",
            "25f008736cff4c50beabb895bbdd93d4",
            "5da4685499de4b9f9ee8f9d1821510fe",
            "20bdcb18b67a4d6eba9b6812e0cdd949",
            "1d36bff847c24a3a877de13ebd779e79",
            "34078f3036d8455cb676ce2be9d61e60",
            "6955dc9e0b9046709a3f796fbe5dbf7d",
            "746662364dbf4effbaafc950f6fb2c6c",
            "e3bb69cf18884ea293d0333c0bb8b58b",
            "7e3eb49567be40eda22a6a6b53f9817c",
            "4c1d9d75b0bf44468b93ac6911025999",
            "17d1b7574ca24f239b5f693c87ea6624",
            "0c56bb1c3ae24c61a492647721726453",
            "7fbaa77501a0479b966f4b707b492985",
            "c1e78bbc892d492293d2b56109f11f7d",
            "1338613f019e4abc8ec957ea0f9665f0",
            "f19e5fd915d84f889d51330a131e5cd7",
            "7398147203464f93a05fc4c779bb4c03",
            "c755c47123934b4bb55f75f1f4f49832",
            "3edddc579c88479da5cb91e9835d6400",
            "5df5274a6b364f6bbc544654ce676721",
            "b7e89cf000f6426a9514868ac93e5305",
            "bcf4ae04ae1549c49c9f66bc4aaa2bc9",
            "1f5f2cf895a04fe7bff9f63ba032dfc0",
            "5ff3ea630c9b413fa481f35b2f9c3a56",
            "215b1cf5a78945fcab3aedaaf728e95e"
          ]
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ad841e80177465fac8f9674ee06a67a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/872 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d36bff847c24a3a877de13ebd779e79"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1821 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1338613f019e4abc8ec957ea0f9665f0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "G1tryO0cLaiu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "training_args = TrainingArguments(\"test-trainer-1\")"
      ],
      "metadata": {
        "id": "aEOQfzBlLT-b"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
      ],
      "metadata": {
        "id": "RKNrlC63Ln6D",
        "outputId": "3f8a38e2-286b-4381-b24e-d938f1f654c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "ba3151c0575a46338f5367bea8208394",
            "378eff925aee4bf5a37751ba4e0d19f3",
            "dfed86f8d0264b219446b239948fab13",
            "7d9d0e2aab34450590e2b64441041912",
            "46cd1dee54d54dcfa5cc2e6eb68f90b6",
            "788d18599c314166a02896e11d387434",
            "1ee05944e20a4b2db3371b586a198f85",
            "87778c9e76bc4f51a02b4cd411abd5ff",
            "8aea7149c4284403b0ef41f2be430008",
            "a1107692d5344d8f9cac71171ed8af23",
            "e17077c2e315476b98f7994f57747bd2"
          ]
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba3151c0575a46338f5367bea8208394"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset = tokenized_datasets_train,\n",
        "    eval_dataset = tokenized_datasets_val,\n",
        "    data_collator = data_collator,\n",
        "    tokenizer = tokenizer\n",
        ")"
      ],
      "metadata": {
        "id": "l_oC12dxMxX-"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "exVKPd7CODa1",
        "outputId": "7d459044-5088-4566-fad3-e3594923933f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='39' max='39' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [39/39 03:07, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=39, training_loss=0.645692874223758, metrics={'train_runtime': 194.5493, 'train_samples_per_second': 1.542, 'train_steps_per_second': 0.2, 'total_flos': 4715443445280.0, 'train_loss': 0.645692874223758, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ],
      "metadata": {
        "id": "CADrprMMBaQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = trainer.predict(tokenized_datasets_val)\n",
        "print(predictions.predictions.shape, predictions.label_ids.shape)"
      ],
      "metadata": {
        "id": "PUcNqZqCBZGT",
        "outputId": "ef2a44bc-413b-4d50-cda0-7331ddadc190",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(872, 2) (872,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "preds = np.argmax(predictions.predictions, axis=-1)"
      ],
      "metadata": {
        "id": "WMeZW000B74b"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "metric = evaluate.load(\"glue\", \"sst2\")\n",
        "metric.compute(predictions=preds, references=predictions.label_ids)"
      ],
      "metadata": {
        "id": "nbqBSRWpEhRB",
        "outputId": "24946acd-e366-402b-8eb9-90d583499415",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "acd71fd2d0074133bca09fea1c93175b",
            "bcac5280c27d42808ce5a2bcd8797964",
            "15ed174194c344018845045ffa09fbc6",
            "ce1d95335b864425af3c45ce7402139d",
            "385c2ba828cf4f96afa6ed3796a9350c",
            "c48688104aad4df7b574150886e3b8fd",
            "ed8445cccb8546bfa33b7bffea74f64b",
            "07a80dc480cd41039161058c0559394a",
            "12f3f5750c494a64a06a86aa4165bd52",
            "564a41b8a2f84802bf3af7c5c0d5198e",
            "3d39405a536649bb969d137cbe86b9c5"
          ]
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.75k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acd71fd2d0074133bca09fea1c93175b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.6823394495412844}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_preds):\n",
        "  metric = evaluate.load(\"glue\", \"sst2\")\n",
        "  logits, labels = eval_preds\n",
        "  predictions = np.argmax(logits, axis=-1)\n",
        "  return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "Uac9eC60FKon"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\"test-trainer-1\", evaluation_strategy=\"epoch\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset = tokenized_datasets_train,\n",
        "    eval_dataset = tokenized_datasets_val,\n",
        "    data_collator=data_collator,\n",
        "    tokenizer = tokenizer,\n",
        "    compute_metrics = compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "QNyjKuslFnV_",
        "outputId": "15dcd402-e366-408b-b1d5-68a12aa3a7cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "JU84bgqlGUMt",
        "outputId": "94729cf3-9332-4c53-ab56-e70bdce23a32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='39' max='39' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [39/39 10:36, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.631986</td>\n",
              "      <td>0.678899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.540902</td>\n",
              "      <td>0.809633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.488527</td>\n",
              "      <td>0.823394</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
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