Causal Language Modeling/AG News/GPT2 Version/GPT2 - AG_News_CLM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5DH1SaThv58"
      },
      "source": [
        "## AG News: Causal Language Modeling\n",
        "\n",
        "Dataset Source: https://www.kaggle.com/datasets/thedevastator/new-dataset-for-text-classification-ag-news"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Install Necessary & Missing Libraries"
      ],
      "metadata": {
        "id": "zNgg53XDiqzU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install transformers datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PCmRdgrRiqqv",
        "outputId": "a2b73d86-0e76-41ac-a060-cf728a5f6389"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: tokenizers, xxhash, multidict, frozenlist, dill, charset-normalizer, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, transformers, aiohttp, datasets\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 charset-normalizer-3.1.0 datasets-2.10.1 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.13.2 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.26.1 xxhash-3.2.0 yarl-1.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Import Necessary Libraries"
      ],
      "metadata": {
        "id": "b3Njpq-Nh0hW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTBtZSGDhv5_",
        "outputId": "4453d77f-b7c7-4acc-ad10-cf2d77646ccf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: Failed to call git rev-parse --git-dir: exit status 128 \n",
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys, random, math\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "\n",
        "import datasets\n",
        "from datasets import ClassLabel, Dataset, DatasetDict\n",
        "\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForLanguageModeling\n",
        "from transformers import TrainingArguments, Trainer, set_seed\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Login For Access to HuggingFace"
      ],
      "metadata": {
        "id": "MJ0Z721Pmywm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "z0vDqT1nmymR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9008fc88-97f3-489b-f036-fe2ac5f0b1ab"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid.\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0DH480nhv6B"
      },
      "source": [
        "#### Display Library Versions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Stbgxd8hv6C",
        "outputId": "b5b9e112-f32f-4771-be2b-433fd84869bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Python: 3.9.16\n",
            "        Pandas: 1.4.4\n",
            "      Datasets: 2.10.1\n",
            "  Transformers: 4.26.1\n"
          ]
        }
      ],
      "source": [
        "print(\"Python:\".rjust(15), sys.version[0:6])\n",
        "print(\"Pandas:\".rjust(15), pd.__version__)\n",
        "print(\"Datasets:\".rjust(15), datasets.__version__)\n",
        "print(\"Transformers:\".rjust(15), transformers.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Mount Google Drive to Notebook"
      ],
      "metadata": {
        "id": "wr2mnxgDEPrn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYJX7qhoEK9Y",
        "outputId": "5a0122f3-ad5b-4e49-d399-2abeab505f6a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6141v14hv6C"
      },
      "source": [
        "#### Load Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ekygkxU0hv6C",
        "outputId": "91be08e9-7eac-4cc1-a9c5-2bcbafd5035e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "</table>\n",
              "<p>120000 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11b7f745-7974-41b8-95e2-ad5f6b732a95')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-11b7f745-7974-41b8-95e2-ad5f6b732a95 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-11b7f745-7974-41b8-95e2-ad5f6b732a95');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "parent_dir = \"/content/drive/MyDrive/Colab Notebooks/AG News\"\n",
        "\n",
        "train_data_file = os.path.join(parent_dir, \"train.csv\")\n",
        "\n",
        "train_data = pd.read_csv(train_data_file, usecols=[\"text\"])\n",
        "\n",
        "train_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mghdE-8hv6D"
      },
      "source": [
        "#### Load Evaluation Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "xSz7H3hZhv6D",
        "outputId": "1e0179cd-c499-4b81-d5a4-29457d8619fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text\n",
              "0     Fears for T N pension after talks Unions repre...\n",
              "1     The Race is On: Second Private Team Sets Launc...\n",
              "2     Ky. Company Wins Grant to Study Peptides (AP) ...\n",
              "3     Prediction Unit Helps Forecast Wildfires (AP) ...\n",
              "4     Calif. Aims to Limit Farm-Related Smog (AP) AP...\n",
              "...                                                 ...\n",
              "7595  Around the world Ukrainian presidential candid...\n",
              "7596  Void is filled with Clement With the supply of...\n",
              "7597  Martinez leaves bitter Like Roger Clemens did ...\n",
              "7598  5 of arthritis patients in Singapore take Bext...\n",
              "7599  EBay gets into rentals EBay plans to buy the a...\n",
              "\n",
              "[7600 rows x 1 columns]"
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            "text/html": [
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              "<style scoped>\n",
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              "      <td>Fears for T N pension after talks Unions repre...</td>\n",
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              "      <td>The Race is On: Second Private Team Sets Launc...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ky. Company Wins Grant to Study Peptides (AP) ...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Prediction Unit Helps Forecast Wildfires (AP) ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Calif. Aims to Limit Farm-Related Smog (AP) AP...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>7595</th>\n",
              "      <td>Around the world Ukrainian presidential candid...</td>\n",
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              "    <tr>\n",
              "      <th>7596</th>\n",
              "      <td>Void is filled with Clement With the supply of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7597</th>\n",
              "      <td>Martinez leaves bitter Like Roger Clemens did ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7598</th>\n",
              "      <td>5 of arthritis patients in Singapore take Bext...</td>\n",
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              "    <tr>\n",
              "      <th>7599</th>\n",
              "      <td>EBay gets into rentals EBay plans to buy the a...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>7600 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f34921c-5fb1-4b67-9ded-f510d8840cd1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\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",
              "    [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",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-0f34921c-5fb1-4b67-9ded-f510d8840cd1 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-0f34921c-5fb1-4b67-9ded-f510d8840cd1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "eval_data_file = os.path.join(parent_dir, \"test.csv\")\n",
        "\n",
        "eval_data = pd.read_csv(eval_data_file, usecols=[\"text\"])\n",
        "\n",
        "eval_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OH_klDSYhv6D"
      },
      "source": [
        "#### Visual: Histogram of Input Lengths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "FwJWS2zzhv6E",
        "outputId": "76288c77-93f3-4394-e374-7b3075eb19f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The maximum length of this dataset is approximately 177 words.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "train_data['input_len'] = train_data['text'].str.split().str.len()\n",
        "eval_data['input_len'] = eval_data['text'].str.split().str.len()\n",
        "\n",
        "max_len = max(max(train_data['input_len']), max(eval_data['input_len']))\n",
        "print(f\"The maximum length of this dataset is approximately {max_len} words.\")\n",
        "\n",
        "input_lengths = pd.DataFrame()\n",
        "input_lengths = pd.concat([train_data['input_len'], eval_data['input_len']])\n",
        "\n",
        "n = max_len\n",
        "input_lengths.hist(grid=False, figsize=(18,8), range=(0, n), bins=n)\n",
        "plt.suptitle('')\n",
        "plt.title('Histogram of Text Length')\n",
        "plt.ylabel('')\n",
        "plt.xlabel('Words per Sample')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYSG1x1Zhv6E"
      },
      "source": [
        "#### Remove Unnecessary Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTFHHFy5hv6F",
        "outputId": "4edfb868-e5d2-49a2-8fad-f4ec51954c04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120000 entries, 0 to 119999\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count   Dtype \n",
            "---  ------  --------------   ----- \n",
            " 0   text    120000 non-null  object\n",
            "dtypes: object(1)\n",
            "memory usage: 937.6+ KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7600 entries, 0 to 7599\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   text    7600 non-null   object\n",
            "dtypes: object(1)\n",
            "memory usage: 59.5+ KB\n"
          ]
        }
      ],
      "source": [
        "train_data = train_data.drop(columns=[\"input_len\"])\n",
        "eval_data = eval_data.drop(columns=[\"input_len\"])\n",
        "\n",
        "train_data.info()\n",
        "eval_data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Am3Y0-fEhv6F"
      },
      "source": [
        "#### Convert DataFrame to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "iAakjOyohv6F"
      },
      "outputs": [],
      "source": [
        "training_dataset = Dataset.from_pandas(train_data)\n",
        "evaluation_dataset = Dataset.from_pandas(eval_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWfNFmUshv6F"
      },
      "source": [
        "#### Split Dataset into Training & Evaluation Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPbeYe0Vhv6F",
        "outputId": "dbb4541e-316e-41ae-ca16-78d338cb17a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (120000, 1)\n",
            "Eval data shape: (7600, 1)\n"
          ]
        }
      ],
      "source": [
        "ds = DatasetDict({\n",
        "    'train' : training_dataset,\n",
        "    'eval' : evaluation_dataset\n",
        "})\n",
        "\n",
        "print('Training data shape:', ds['train'].shape)\n",
        "print('Eval data shape:', ds['eval'].shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tt2n312ohv6G"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "sKPeH_eChv6G"
      },
      "outputs": [],
      "source": [
        "BLOCK_SIZE = 128\n",
        "MODEL_CKPT = \"gpt2\"\n",
        "\n",
        "MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-Causal_Language_Model-AG_News\"\n",
        "set_seed = 42\n",
        "\n",
        "BATCH_SIZE = 1000 \n",
        "NUM_OF_EPOCHS = 3\n",
        "\n",
        "WEIGHT_DECAY = 0.01\n",
        "STRATEGY = \"epoch\"\n",
        "\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "LEARNING_RATE = 2e-5\n",
        "\n",
        "DEVICE = torch.device(\"cuda\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgqausbIhv6G"
      },
      "source": [
        "#### Tokenize Entire Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "78923dff81da41b281e1eed08eb60c03",
            "7decb3c948e44574a926610f84a0ffb1",
            "8b1867e8b484434bb0f52bf4eaacded4",
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            "694f01cd466a44a3a57e4eaa1052cfde",
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            "60d5c826308a44119eff7f62e9ac1d38",
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            "e9fe45996a424e77b442c8c0121d46dc",
            "cad74b995540487583499fa2fe34e083",
            "31d7e1df26444fc498d60c0d7078c695",
            "9600fd17ecd146f6aee55b5aa16d4447",
            "14fce922fe4141edac324b367b305ed8",
            "91aeef98021a474fbce1b4f1326d10ef",
            "4928fc8e100c47b1ab2783ade1ff4481",
            "c47af48935c9491a907241a813445671",
            "99b7d8365f2b475c8892b9074d3de164",
            "4c26908385e64ee4b7508dcd0bcbed94",
            "bc84d90fb8e6456499a6b5937b24a8b6",
            "baa524963dfa436c98d22bd6cafaa873",
            "42c62efa4fbd447db70ec14bc30621e4",
            "6e35326adcb246219f2c1587998360ea",
            "7874384053b049df96eeda6670c13bb6",
            "dcd36aff05a942c6bcbdb78005dac66a",
            "44e3c65ee20c46e1b095fc3416510050",
            "2fd9c254504748cc8ca3b697f14645d8",
            "280e164125f24f04b6304c9338942086",
            "6bcb32c5f3224b2e8eff51e1e4e74f55",
            "173e2c6518ad48c58e7d036adb59792a",
            "f21c977de5084bfe8ff0effadb89365a",
            "2fde08a6124046abb3d8d58cca231bfc"
          ]
        },
        "id": "6dHzppm_hv6G",
        "outputId": "aeec6455-740d-43e9-a6a4-5f49ac3eac77"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78923dff81da41b281e1eed08eb60c03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53141ef3337c4420b8092036b815f1b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cad74b995540487583499fa2fe34e083"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42c62efa4fbd447db70ec14bc30621e4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT, use_fast=True)\n",
        "\n",
        "def tokenizer_function(samples):\n",
        "    return tokenizer(samples[\"text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DEoTvnghv6G"
      },
      "source": [
        "#### Apply Tokenizer Function to Entire Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "ace35e4b6fe1413b91208bebd44a55ff",
            "467d204019df4c908c6cdcce659cc578",
            "70b40ea4a8604464b25f8eb7ba637fab",
            "4e4373ac20f543b7a57be7750eaa2985",
            "d287cebafaab4f05958fb762fba96355",
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            "4489141bf66f4bcfb2a73499971adacb",
            "fade81b7ffc7497db597e9f427c84238",
            "4d43d3d221954e0c87b9eb3a17ca29c8",
            "8d417d9bdeeb481891fcb0341ad51549",
            "3e59663028294c6190e102067b667b58",
            "c23bed6b72d44b6eb562c074be5e46ab",
            "bcb81faa968448d1b249878eca72f5e3",
            "f826a60146394ebd8eb269459aca865a",
            "3988e0e471ec48f2853264e7dc821d8c",
            "44b3edf21c1e437b978743cc9c88450d",
            "fa03782895d74f5289bafcad92f2e6e9",
            "cf56a33b831145538bad649f286f26d5",
            "3ce371d4ae3d46dd899be1a81a7a47b0",
            "9d20a496413246e594fa99f218a1da57"
          ]
        },
        "id": "2iKvu1odhv6G",
        "outputId": "e8b412ac-85de-43c9-c952-56207b848e1c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ace35e4b6fe1413b91208bebd44a55ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d417d9bdeeb481891fcb0341ad51549"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['input_ids', 'attention_mask'],\n",
            "    num_rows: 120000\n",
            "})\n",
            "Dataset({\n",
            "    features: ['input_ids', 'attention_mask'],\n",
            "    num_rows: 7600\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "tokenized_ds = ds.map(tokenizer_function, \n",
        "                      batched=True,\n",
        "                      remove_columns=ds[\"train\"].column_names,)\n",
        "\n",
        "print(tokenized_ds[\"train\"])\n",
        "print(tokenized_ds[\"eval\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQcEjZ4hhv6H"
      },
      "source": [
        "#### Preprocess Tokenized Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "5tA7L3i1hv6H"
      },
      "outputs": [],
      "source": [
        "def group_texts(samples):\n",
        "    concatenated_examples = {k: sum(samples[k], []) for k in samples.keys()}\n",
        "    total_length = len(concatenated_examples[list(samples.keys())[0]])\n",
        "    total_length = (total_length // BLOCK_SIZE) * BLOCK_SIZE\n",
        "    result = {\n",
        "        k: [t[i : i + BLOCK_SIZE] for i in range(0, total_length, BLOCK_SIZE)]\n",
        "        for k, t in concatenated_examples.items()\n",
        "    }\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pL6tOgX0hv6H"
      },
      "source": [
        "#### Apply Preprocessing Function to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "15c1cd36a00543069da9758308f0fd09",
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            "a48b67ae52fc4b4a8ded6750ba2ec4c8",
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            "a8a19a1514a446378eaa40cd4769b900",
            "4c805ecb33c846eda33f3388c856bfec",
            "67c0b7116f284673a88934db7328317f",
            "1b2f29564d1b4a7eb75dde213e158c87",
            "0acc8c916ea34db59ffa0b6c9ae3b83e",
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            "3ceef89f013548a395e7d5ba74da4f64",
            "9becca90563e4c76b6fae86967f9470a",
            "8edd6b1a736d4aca99aa254cb0ed02d9",
            "54044526900540dfba293f1c85404806",
            "b00cdc8d32dc4d0ba5f91e9a0764625c",
            "27fcfb8adb80401b999df5efed774ac8",
            "29b27d63138447f784eefa7844b765f4"
          ]
        },
        "id": "trQJcO9Bhv6H",
        "outputId": "e9c66d6c-6f65-49d1-af30-fce20dc92de6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15c1cd36a00543069da9758308f0fd09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "67c0b7116f284673a88934db7328317f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "clm_ds = tokenized_ds.map(\n",
        "    group_texts,\n",
        "    batched=True,\n",
        "    num_proc=4,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0Ne39r-hv6H"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "51b27e83e5bd474fbe435ad305226355",
            "5c7d88266019420dba56b9d1b48304a0",
            "6079d431d580409998934df07eabffde",
            "f5d487815a7a405da97daa04c63c0440",
            "44e030d5c1324441bfcd201e5c5d1a66",
            "00a2f506d0cf41d2a65a86ebbb36b5c8",
            "20da9d195128485ebc96510e23533014",
            "e7f50e3424ec48c299face4e1d8a1643",
            "ca99b089697443128b608a4c010187b0",
            "a134cda40c374bad905f50c7d7b77298",
            "6f204f5761a84e67ba0dadad1f7b191c",
            "684f3b7aacf84614924e45d9d8226349",
            "be89bf8c715d4047b7293831f9a69c44",
            "3a17f659d7824d1e9d5fe406e9bd1d75",
            "7f86c0430d01484f93ab81e6be60e7f0",
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            "650cc64445c841d19b88c93e3eea6e73",
            "aed17054ab3e4570b35f60cde8bea256",
            "20a7e46f41f144bba31d89736990a214",
            "a2cdee48d1554c2db45344c074dbe0c7",
            "44d767471fe24e6b96591df35ccf22a0",
            "a971c4c00dea42b4a426c0c41ed80b3a"
          ]
        },
        "id": "QsCBa-c4hv6H",
        "outputId": "a087c4a3-4cb0-4721-9ce5-3ec55b96b6c2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51b27e83e5bd474fbe435ad305226355"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "684f3b7aacf84614924e45d9d8226349"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = (\n",
        "    AutoModelForCausalLM.from_pretrained(MODEL_CKPT)\n",
        "    ).to(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L5JjDmg4hv6H"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "vytmc_s1hv6H"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    output_dir=MODEL_NAME,\n",
        "    evaluation_strategy=STRATEGY,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    logging_strategy=STRATEGY,\n",
        "    save_strategy=STRATEGY,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    report_to=REPORTS_TO,\n",
        "    hub_private_repo=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-R5wGLUhv6H"
      },
      "source": [
        "#### Define Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "jLyu7HfIhv6I"
      },
      "outputs": [],
      "source": [
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFK5HAbPhv6I"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "OPGY499qhv6I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa46c2ef-3cf5-4d63-9ac5-fb3e3f690c75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/gpt2-Causal_Language_Model-AG_News into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/gpt2-Causal_Language_Model-AG_News into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=clm_ds[\"train\"],\n",
        "    eval_dataset=clm_ds[\"eval\"],\n",
        "    data_collator=data_collator\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNOM5Dfghv6I"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "7qD1Wmuwhv6I",
        "outputId": "23aa3f8d-bc44-4f4b-9c8f-8ce485c884ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 48791\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 18297\n",
            "  Number of trainable parameters = 124439808\n",
            "You're using a GPT2TokenizerFast 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='18297' max='18297' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [18297/18297 1:38:26, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.486500</td>\n",
              "      <td>3.218378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.238800</td>\n",
              "      <td>3.150211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.161000</td>\n",
              "      <td>3.131831</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3063\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to gpt2-Causal_Language_Model-AG_News/checkpoint-6099\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/checkpoint-6099/config.json\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/checkpoint-6099/generation_config.json\n",
            "Model weights saved in gpt2-Causal_Language_Model-AG_News/checkpoint-6099/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3063\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to gpt2-Causal_Language_Model-AG_News/checkpoint-12198\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/checkpoint-12198/config.json\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/checkpoint-12198/generation_config.json\n",
            "Model weights saved in gpt2-Causal_Language_Model-AG_News/checkpoint-12198/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3063\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to gpt2-Causal_Language_Model-AG_News/checkpoint-18297\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/checkpoint-18297/config.json\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/checkpoint-18297/generation_config.json\n",
            "Model weights saved in gpt2-Causal_Language_Model-AG_News/checkpoint-18297/pytorch_model.bin\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60expOoLhv6I"
      },
      "source": [
        "#### Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "UZfPJ0cfhv6I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "8ceec89d-d62f-4e5f-85ef-d794e6e38cf6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 3063\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='383' max='383' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [383/383 00:33]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perplexity: 22.92\n"
          ]
        }
      ],
      "source": [
        "evaluation_results = trainer.evaluate()\n",
        "print(f\"Perplexity: {math.exp(evaluation_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9S3q9gNhv6I"
      },
      "source": [
        "#### Push Model to HuggingFace Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to gpt2-Causal_Language_Model-AG_News\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/config.json\n",
            "Configuration saved in gpt2-Causal_Language_Model-AG_News/generation_config.json\n",
            "Model weights saved in gpt2-Causal_Language_Model-AG_News/pytorch_model.bin\n",
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 32.0k/487M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "Upload file runs/Mar15_01-26-17_b684efef14b7/events.out.tfevents.1678843583.b684efef14b7.155.0: 100%|#########…"
            ],
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            "text/plain": [
              "Upload file runs/Mar15_01-26-17_b684efef14b7/events.out.tfevents.1678849526.b684efef14b7.155.2: 100%|#########…"
            ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "remote: Scanning LFS files of refs/heads/main for validity...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/DunnBC22/gpt2-Causal_Language_Model-AG_News\n",
            "   88e8740..b54ed8f  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:remote: Scanning LFS files of refs/heads/main for validity...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/DunnBC22/gpt2-Causal_Language_Model-AG_News\n",
            "   88e8740..b54ed8f  main -> main\n",
            "\n",
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}\n",
            "To https://huggingface.co/DunnBC22/gpt2-Causal_Language_Model-AG_News\n",
            "   b54ed8f..6170af9  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/gpt2-Causal_Language_Model-AG_News\n",
            "   b54ed8f..6170af9  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/DunnBC22/gpt2-Causal_Language_Model-AG_News/commit/b54ed8fb0222352a1104688218de91f8fa5b7b9c'"
            ],
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          "metadata": {},
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        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmKKcyoBhv6I"
      },
      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "- This is definitely an improvement over the distilGPT2 version of this model, but I would still prefer better results.\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Citations\n",
        "- Model Checkpoint\n",
        "  - @article{radford2019language,\n",
        "  title={Language Models are Unsupervised Multitask Learners},\n",
        "  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},\n",
        "  year={2019}\n",
        "}\n",
        "\n",
        "- Metric (Perplexity)\n",
        "  - @article{jelinek1977perplexity,\n",
        "title={Perplexity—a measure of the difficulty of speech recognition tasks},\n",
        "author={Jelinek, Fred and Mercer, Robert L and Bahl, Lalit R and Baker, James K},\n",
        "journal={The Journal of the Acoustical Society of America},\n",
        "volume={62},\n",
        "number={S1},\n",
        "pages={S63--S63},\n",
        "year={1977},\n",
        "publisher={Acoustical Society of America}\n",
        "}"
      ],
      "metadata": {
        "id": "3pbVQwMqA4wW"
      }
    }
  ],
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      "version": "3.9.12"
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    "orig_nbformat": 4,
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