chapter3/section4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_PjgXHFMjaH"
      },
      "source": [
        "✏️ **Try it out!** Modify the previous training loop to fine-tune your model on the SST-2 dataset.\n",
        "\n",
        "(This exercise is from https://huggingface.co/learn/nlp-course/chapter3/4?fw=pt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ffYICrZ8UvF",
        "outputId": "a8ea8000-1742-43ad-ad69-ec7c06701ccc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jJMh8TN8dwx",
        "outputId": "a8244f0b-fbb5-441a-9236-78762e9d2dcb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.4/196.4 kB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m257.7/257.7 kB\u001b[0m \u001b[31m34.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q wandb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mi0bNz0FmOwW"
      },
      "source": [
        "If running this notebook in Colab, please ensure that your Hugging Face `HF_TOKEN` and your Weights & Biases `WANDB_API_KEY` are added to your Colab secrets.\n",
        "\n",
        "Alternatively, please login to Hugging Face and Weights & Biases by running the following two cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OA4Pl_ytmSNw"
      },
      "outputs": [],
      "source": [
        "# !huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2GKFeJmBmVPg"
      },
      "outputs": [],
      "source": [
        "# !wandb login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z55VPmq78qPI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "def seed_everything(seed):\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "seed_everything(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dq7k8qs0L1W1"
      },
      "outputs": [],
      "source": [
        "os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZTLmLGTqxtz"
      },
      "source": [
        "From the <a href=\"https://huggingface.co/datasets/sst2\" target=\"_blank\">dataset page</a>:\n",
        "\n",
        "> The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.\n",
        "\n",
        "> Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary."
      ]
    },
    {
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        },
        "id": "s7nw2Yzn9CU9",
        "outputId": "f212eb3b-cb5e-4c9d-9ef8-f7ee4e26d398"
      },
      "outputs": [
        {
          "data": {
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              "version_major": 2,
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.27k [00:00<?, ?B/s]"
            ]
          },
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          "output_type": "display_data"
        },
        {
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            "text/plain": [
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          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "Downloading data:   0%|          | 0.00/72.8k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "Downloading data:   0%|          | 0.00/148k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "afdef0a983fe465e981ee3afd264a787",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/67349 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "042f053f32074ff782648f7c6f38768c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split:   0%|          | 0/872 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e50cb0b6f03e4a288999d01569cd22fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split:   0%|          | 0/1821 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['idx', 'sentence', 'label'],\n",
              "        num_rows: 67349\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['idx', 'sentence', 'label'],\n",
              "        num_rows: 872\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['idx', 'sentence', 'label'],\n",
              "        num_rows: 1821\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"sst2\")\n",
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iDQTR0h9N3p",
        "outputId": "48fdf3c7-8ce1-4395-a495-dc260293d7ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'idx': Value(dtype='int32', id=None),\n",
              " 'sentence': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['negative', 'positive'], id=None)}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features = raw_datasets['train'].features\n",
        "features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uxBVK8G9U3j",
        "outputId": "f2ef893a-09e6-4c8c-e719-bca6dd19cd1e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{0: 'negative', 1: 'positive'}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "id2label = {id: label for id, label in enumerate(features['label'].names)}\n",
        "id2label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJweku_RtrHJ"
      },
      "source": [
        "Let's check the class distribution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eiqvOR1g9dKl",
        "outputId": "bfd8814f-d284-4c4f-a13a-18aee06830e1"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-4acb8726-812b-4c7e-8459-a31d60df711c\" class=\"colab-df-container\">\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idx</th>\n",
              "      <th>sentence</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>hide new secretions from the parental units</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>contains no wit , only labored gags</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>that loves its characters and communicates som...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>remains utterly satisfied to remain the same t...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>on the worst revenge-of-the-nerds clichés the ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67344</th>\n",
              "      <td>67344</td>\n",
              "      <td>a delightful comedy</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67345</th>\n",
              "      <td>67345</td>\n",
              "      <td>anguish , anger and frustration</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67346</th>\n",
              "      <td>67346</td>\n",
              "      <td>at achieving the modest , crowd-pleasing goals...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67347</th>\n",
              "      <td>67347</td>\n",
              "      <td>a patient viewer</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67348</th>\n",
              "      <td>67348</td>\n",
              "      <td>this new jangle of noise , mayhem and stupidit...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67349 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4acb8726-812b-4c7e-8459-a31d60df711c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4acb8726-812b-4c7e-8459-a31d60df711c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4acb8726-812b-4c7e-8459-a31d60df711c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c995406f-27aa-4a9d-86d7-fd5c6da6b73f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c995406f-27aa-4a9d-86d7-fd5c6da6b73f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c995406f-27aa-4a9d-86d7-fd5c6da6b73f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_12f51464-3e9b-4957-b7fc-f63bd7a52d97\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_12f51464-3e9b-4957-b7fc-f63bd7a52d97 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         idx                                           sentence  label\n",
              "0          0       hide new secretions from the parental units       0\n",
              "1          1               contains no wit , only labored gags       0\n",
              "2          2  that loves its characters and communicates som...      1\n",
              "3          3  remains utterly satisfied to remain the same t...      0\n",
              "4          4  on the worst revenge-of-the-nerds clichés the ...      0\n",
              "...      ...                                                ...    ...\n",
              "67344  67344                               a delightful comedy       1\n",
              "67345  67345                   anguish , anger and frustration       0\n",
              "67346  67346  at achieving the modest , crowd-pleasing goals...      1\n",
              "67347  67347                                  a patient viewer       1\n",
              "67348  67348  this new jangle of noise , mayhem and stupidit...      0\n",
              "\n",
              "[67349 rows x 3 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "train_df = raw_datasets['train'].with_format(\"pandas\")[:]\n",
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2DDG2zJ9mHE",
        "outputId": "7d42a9f6-38a5-4d63-87f6-995aa8c63e7e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    37569\n",
              "0    29780\n",
              "Name: label, dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHgTujBH9qAx",
        "outputId": "0c370fdc-7367-4885-933d-1edd72473af0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    55.782565\n",
              "0    44.217435\n",
              "Name: label, dtype: float64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(train_df['label'].value_counts() / len(train_df)) * 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjpTPufbtw6v"
      },
      "source": [
        "The dataset is well-balanced."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
            "ff2ec3b37d1b426aafcbdeecc4a4d814",
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            "4ee58cdb0c5140d5a0088f732d19b87f",
            "9b6d2ac8622c40699541d1fec538b93c",
            "760e3946c7184c879b250f0caecd88f3",
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        },
        "id": "PCJOCfpC9zja",
        "outputId": "57d85e23-fce3-42a1-f25e-e871edcd2c20"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ff2ec3b37d1b426aafcbdeecc4a4d814",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "ab9ec037c7fd4d8697a885f43a38300f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "527cc5dbfaae4a648c888f580ea78aec",
              "version_major": 2,
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            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "70f32d7fc49f47c2be2957dba9946457",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "tokenizer.model_max_length"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3Y9YFuS-A2p",
        "outputId": "ba3028a5-08dc-4747-dd17-e6cd053d413d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'idx': [0, 1, 2, 3],\n",
              " '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",
              " 'label': [0, 0, 1, 0]}"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "examples = raw_datasets['train'][:4]\n",
        "examples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSEMCQic-IRN",
        "outputId": "d919d844-db50-4b7a-dc96-4a42c463e07c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'input_ids': [[101, 5342, 2047, 3595, 8496, 2013, 1996, 18643, 3197, 102], [101, 3397, 2053, 15966, 1010, 2069, 4450, 2098, 18201, 2015, 102], [101, 2008, 7459, 2049, 3494, 1998, 10639, 2015, 2242, 2738, 3376, 2055, 2529, 3267, 102], [101, 3464, 12580, 8510, 2000, 3961, 1996, 2168, 2802, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer(examples['sentence'], truncation=True, max_length=512)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eZvlqgZG-TRP"
      },
      "outputs": [],
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples['sentence'], truncation=True, max_length=512)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
            "75d130e3d05d4c82a516990493e8b58a",
            "b124eb7f1015437582e4131d946cd858",
            "6c6f1dfa39594cb5a83b537690d0882f",
            "f7edad7a55154dd29fe80c8e45ea9cae",
            "cdd665159daf494caf10176421918b36",
            "197c7a19fc744155b3e117a2b23d9cb1",
            "c78cc06a9b07449394e1a492d2a1bf71",
            "fc67678aa0004df6bf3309d3628a4123",
            "d5dc718732a94130ad816c4d6001f35d",
            "21c8504eed224c9a8c6175424d7823ec",
            "ee7d48b93368405ca64a295f45b1b059",
            "806792035d68458aa3b54b9ed503f0d8",
            "560a664b85cb487cb5b0d14746fa3e3f",
            "c2c5be5db019426c9c26c8e7764ed561",
            "ea2b17412ba348aab0d0f3bc91f9a529",
            "d7ba7338139a46818b90e6b23962662b",
            "3a6332849e4247d195eda383375fd353",
            "d7b8c500c5704dd786614341d9923af1",
            "2b6b70568d864ad28ecf0b6c593d4318",
            "bba2008b5a4d44e8967f534b43a4c402",
            "e5b548d728634c7b864e51e97998535f",
            "dc5215b95f34408c899b5748fd47b9ea",
            "9e8ee55d705c46cba46921b2cf5f3f45",
            "6b6d524fc3bc470880d295190e3db12d",
            "9e5dda3e49244054b49108b901a28c45",
            "1053b4fc9251433ab1f3522ec8834b65",
            "6ebc610a96114f6baa62f5d022f94997",
            "40d9158187d248469d0b7297466fe5be",
            "f1d56d17d7a0427699909e2ea002aaa5",
            "efca45f472524aaa92c4449906173044",
            "f03aa502b3ab4292aab62e0516ce123f",
            "3e4d3b5aaf4f46db983cf178ed03ea0b",
            "aedc04b501ac4a11a3f14402f74d6afe"
          ]
        },
        "id": "tZIdc5wP-eNq",
        "outputId": "329be04b-7989-4807-9f3f-2d1a89808621"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75d130e3d05d4c82a516990493e8b58a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/67349 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "806792035d68458aa3b54b9ed503f0d8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/872 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e8ee55d705c46cba46921b2cf5f3f45",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1821 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['idx', 'sentence', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 67349\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['idx', 'sentence', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 872\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['idx', 'sentence', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1821\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dobt-Jpt-pbp",
        "outputId": "070ed225-4545-4be3-8744-e03f673d52de"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 67349\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 872\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1821\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_datasets = tokenized_datasets.remove_columns(['idx', 'sentence'])\n",
        "tokenized_datasets = tokenized_datasets.rename_column('label', 'labels')\n",
        "tokenized_datasets.set_format(\"torch\")\n",
        "tokenized_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Edd2EqH-6Kg",
        "outputId": "67532223-8527-4993-b2d9-313f14cacda5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "labels: 0\n",
            "input_ids: tensor([  101,  5342,  2047,  3595,  8496,  2013,  1996, 18643,  3197,   102])\n",
            "token_type_ids: tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
            "attention_mask: tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n"
          ]
        }
      ],
      "source": [
        "example = tokenized_datasets['train'][0]\n",
        "for k, v in example.items():\n",
        "    print(f\"{k}: {v}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUASrih3_CPt",
        "outputId": "69ea7473-342c-43cf-bc81-791f31d5d6fa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(0)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example['labels']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Mn207Oir_EsY",
        "outputId": "8ed230d2-7c73-4c05-bf90-50f961337c11"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'negative'"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "id2label[example['labels'].item()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G7NqQaDl_IFx"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOexU_Si_VM6",
        "outputId": "8a6882d9-3553-4e67-9c0c-7f6233dc8b53"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2105, 28)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "batch_size = 32\n",
        "train_dl = DataLoader(tokenized_datasets['train'], batch_size=batch_size, shuffle=True, collate_fn=data_collator, num_workers=2)\n",
        "val_dl = DataLoader(tokenized_datasets['validation'], batch_size=batch_size, shuffle=False, collate_fn=data_collator, num_workers=2)\n",
        "len(train_dl), len(val_dl)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFfdu-5F_146",
        "outputId": "1cf3179e-a2a3-4adf-a715-a218e5049b71"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'labels': torch.Size([32]),\n",
              " 'input_ids': torch.Size([32, 33]),\n",
              " 'token_type_ids': torch.Size([32, 33]),\n",
              " 'attention_mask': torch.Size([32, 33])}"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Sanity check:\n",
        "for batch in train_dl:\n",
        "    break\n",
        "{k: v.shape for k, v in batch.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865,
          "referenced_widgets": [
            "ae5ee2eb37664ba4b09eb4f41be529bf",
            "00a8d67d02304c7b9494fdd93003a71c",
            "e2de9243937f46d2ac5c3fccf58c38fa",
            "39bdb776b06b4377883dc8d97f8acf63",
            "74805c7bdb7348d8b25e85faa0729ac4",
            "205ba4e7add84dbf90a5d62a0dc3f731",
            "a7b646eb4c574ff28c480b4c44fce3bf",
            "58ebf9ff92ac4274a92c4f307f66e167",
            "9072d0f26e3f4b8e926ce30a20c5ac45",
            "eab36351145e464dbcee20b52bfdd097",
            "8b70b908eb8b44718c6f70cd12c948d0"
          ]
        },
        "id": "IAy0rwSf_-9f",
        "outputId": "4f16b704-85aa-48f1-9dfa-9f059e161002"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae5ee2eb37664ba4b09eb4f41be529bf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySmtlBtiAOtF",
        "outputId": "15f3c057-54c9-4df8-c07e-2464e3a484e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qiNxiNyrAhDq"
      },
      "outputs": [],
      "source": [
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ds36qwa4Ajcv",
        "outputId": "5f24036f-2321-4d11-e951-afdbbb64e2ef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SequenceClassifierOutput(loss=tensor(0.6751, device='cuda:0', grad_fn=<NllLossBackward0>), logits=tensor([[ 0.2692,  0.0768],\n",
              "        [ 0.2673, -0.0022],\n",
              "        [ 0.2352, -0.1076],\n",
              "        [ 0.2086, -0.3152],\n",
              "        [ 0.2320, -0.0012],\n",
              "        [ 0.2137,  0.1305],\n",
              "        [ 0.3201, -0.0364],\n",
              "        [ 0.2502, -0.1508],\n",
              "        [ 0.2656, -0.0365],\n",
              "        [ 0.2057, -0.0881],\n",
              "        [ 0.1889,  0.2329],\n",
              "        [ 0.2673,  0.1059],\n",
              "        [ 0.2805,  0.0771],\n",
              "        [ 0.2659,  0.2067],\n",
              "        [ 0.3059, -0.0498],\n",
              "        [ 0.3487, -0.0887],\n",
              "        [ 0.2121, -0.0198],\n",
              "        [ 0.2409,  0.0659],\n",
              "        [ 0.1603,  0.1637],\n",
              "        [ 0.1797, -0.0110],\n",
              "        [ 0.2672,  0.1040],\n",
              "        [ 0.2379,  0.1663],\n",
              "        [ 0.2489, -0.2742],\n",
              "        [ 0.3049, -0.0914],\n",
              "        [ 0.2763, -0.0045],\n",
              "        [ 0.2838,  0.1267],\n",
              "        [ 0.2690, -0.0442],\n",
              "        [ 0.2672, -0.2779],\n",
              "        [ 0.2967, -0.0637],\n",
              "        [ 0.2731, -0.1183],\n",
              "        [ 0.2111, -0.2203],\n",
              "        [ 0.2280, -0.0512]], device='cuda:0', grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Sanity check:\n",
        "batch = {k: v.to(device) for k, v in batch.items()}\n",
        "output = model(**batch)\n",
        "output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-et-sIcVRxIh"
      },
      "source": [
        "**Note:** If we get the same output each time we run the notebook (from top to the above cell), then the random seed has been set correctly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l84cDh3EA274"
      },
      "outputs": [],
      "source": [
        "from torch.optim import AdamW\n",
        "\n",
        "learning_rate = 5e-5\n",
        "optimizer = AdamW(model.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "deNH7mcGBAc-",
        "outputId": "0835c096-8022-4ecd-f25d-cc0bc38b5f31"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(6315, 0)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr_scheduler_type = \"linear\"\n",
        "num_epochs = 3\n",
        "num_training_steps = num_epochs * len(train_dl)\n",
        "num_warmup_steps = 0\n",
        "num_training_steps, num_warmup_steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BeuQC62mBRvG"
      },
      "outputs": [],
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "lr_scheduler = get_scheduler(\n",
        "    lr_scheduler_type,\n",
        "    optimizer=optimizer,\n",
        "    num_training_steps=num_training_steps,\n",
        "    num_warmup_steps=num_warmup_steps\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dAFfxKKWBxEZ"
      },
      "outputs": [],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "def train_epoch():\n",
        "    print(\"Training...\")\n",
        "    model.train()\n",
        "    train_loss = 0\n",
        "    progress_bar = tqdm(range(len(train_dl)))\n",
        "    for batch in train_dl:\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        output = model(**batch)\n",
        "        loss = output.loss\n",
        "        train_loss += loss.item()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)\n",
        "    train_loss /= len(tokenized_datasets['train'])\n",
        "    train_loss = round(train_loss, 4)\n",
        "    return train_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JQVhYmucCrlL"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "\n",
        "def validate_epoch():\n",
        "    print(\"Validating...\")\n",
        "    model.eval()\n",
        "    val_loss = 0\n",
        "    metrics = evaluate.load(\"glue\", \"sst2\")\n",
        "    progress_bar = tqdm(range(len(val_dl)))\n",
        "    for batch in val_dl:\n",
        "        batch = {k: v.to(device) for k, v in batch.items()}\n",
        "        with torch.no_grad():\n",
        "            output = model(**batch)\n",
        "        loss = output.loss\n",
        "        val_loss += loss.item()\n",
        "        logits = output.logits\n",
        "        preds = torch.argmax(logits, dim=-1)\n",
        "        metrics.add_batch(predictions=preds, references=batch['labels'])\n",
        "        progress_bar.update(1)\n",
        "    val_loss /= len(tokenized_datasets['validation'])\n",
        "    val_loss = round(val_loss, 4)\n",
        "    computed_metrics = metrics.compute()\n",
        "    acc = round(computed_metrics['accuracy'], 4)\n",
        "    return val_loss, acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "z8YYYSe0EYwW",
        "outputId": "6987c4a7-f5f3-40e0-9197-8fbde6d637ef"
      },
      "outputs": [
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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            "Epoch: 0\n",
            "Training...\n"
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            "Accuracy: 0.9243\n",
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          "text": [
            "---\n",
            "Epoch: 1\n",
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            "Training loss: 0.003\n",
            "Validating...\n"
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            "Validation loss: 0.0064\n",
            "Accuracy: 0.9278\n",
            "Pushing model...\n"
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          "text": [
            "---\n",
            "Epoch: 2\n",
            "Training...\n"
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            "Validation loss: 0.0077\n",
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            "Pushing model...\n"
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              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▃█▁</td></tr><tr><td>train_loss</td><td>█▃▁</td></tr><tr><td>val_loss</td><td>▁▂█</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.9232</td></tr><tr><td>train_loss</td><td>0.0015</td></tr><tr><td>val_loss</td><td>0.0077</td></tr></table><br/></div></div>"
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              " View run <strong style=\"color:#cdcd00\">confused-totem-1</strong> at: <a href='https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-sst2-v2/runs/ep1lkevp' target=\"_blank\">https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-sst2-v2/runs/ep1lkevp</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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              "Find logs at: <code>./wandb/run-20240207_105914-ep1lkevp/logs</code>"
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          "text": [
            "---\n",
            "Pushing tokenizer...\n",
            "Done!\n"
          ]
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        "for epoch in range(num_epochs):\n",
        "    print(f\"Epoch: {epoch}\")\n",
        "\n",
        "    train_loss = train_epoch()\n",
        "    print(f\"Training loss: {train_loss}\")\n",
        "\n",
        "    val_loss, acc = validate_epoch()\n",
        "    print(f\"Validation loss: {val_loss}\\nAccuracy: {acc}\")\n",
        "\n",
        "    wandb.log({\n",
        "        'train_loss': train_loss,\n",
        "        'val_loss': val_loss,\n",
        "        'accuracy': acc\n",
        "    })\n",
        "\n",
        "    print(\"Pushing model...\")\n",
        "    model.push_to_hub(\"bert-base-uncased-finetuned-sst2-v2\", commit_message=f\"epoch: {epoch}, accuracy: {acc}\")\n",
        "    print(\"---\")\n",
        "wandb.finish()\n",
        "print(\"---\")\n",
        "print(\"Pushing tokenizer...\")\n",
        "tokenizer.push_to_hub(\"bert-base-uncased-finetuned-sst2-v2\", commit_message=\"pushing tokenizer\")\n",
        "print(\"Done!\")"
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