Causal Language Modeling/AG News/DistilGPT2 Version/DistilGPT2 - AG_News_CLM.ipynb

{
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      "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",
      "metadata": {
        "id": "zNgg53XDiqzU"
      },
      "source": [
        "#### Install Necessary & Missing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PCmRdgrRiqqv",
        "outputId": "c94abeaa-6421-465c-a0e1-d58afc75bbc0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\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"
          ]
        }
      ],
      "source": [
        "%pip install transformers datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3Njpq-Nh0hW"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTBtZSGDhv5_",
        "outputId": "dbeb8df8-a73c-43c6-b405-8788d8b05b93"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
      "metadata": {
        "id": "MJ0Z721Pmywm"
      },
      "source": [
        "#### Login For Access to HuggingFace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0vDqT1nmymR",
        "outputId": "9c2ff444-4fb9-401c-e2d4-3e2cc22e98f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "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": "26d202c8-a5c4-4b4b-9c1d-df70ab601eeb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
      "metadata": {
        "id": "wr2mnxgDEPrn"
      },
      "source": [
        "#### Mount Google Drive to Notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYJX7qhoEK9Y",
        "outputId": "cd7b61bc-a6fa-4b5f-cd92-1b40360e1a7c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "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": "c2fb3b99-f94e-4830-9000-a45c5d038473"
      },
      "outputs": [
        {
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              "\n",
              "    .colab-df-convert {\n",
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              "\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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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3180d7d9-c0f0-4707-a202-ad3cbff44545');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                     text\n",
              "0       Wall St. Bears Claw Back Into the Black (Reute...\n",
              "1       Carlyle Looks Toward Commercial Aerospace (Reu...\n",
              "2       Oil and Economy Cloud Stocks' Outlook (Reuters...\n",
              "3       Iraq Halts Oil Exports from Main Southern Pipe...\n",
              "4       Oil prices soar to all-time record, posing new...\n",
              "...                                                   ...\n",
              "119995  Pakistan's Musharraf Says Won't Quit as Army C...\n",
              "119996  Renteria signing a top-shelf deal Red Sox gene...\n",
              "119997  Saban not going to Dolphins yet The Miami Dolp...\n",
              "119998  Today's NFL games PITTSBURGH at NY GIANTS Time...\n",
              "119999  Nets get Carter from Raptors INDIANAPOLIS -- A...\n",
              "\n",
              "[120000 rows x 1 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": "cece6f7b-9ebd-4fa6-af4d-281ba2753a35"
      },
      "outputs": [
        {
          "data": {
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              "      <td>The Race is On: Second Private Team Sets Launc...</td>\n",
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              "    <tr>\n",
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              "      <td>Ky. Company Wins Grant to Study Peptides (AP) ...</td>\n",
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              "      <td>Prediction Unit Helps Forecast Wildfires (AP) ...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Calif. Aims to Limit Farm-Related Smog (AP) AP...</td>\n",
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              "      <th>...</th>\n",
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              "      <td>Void is filled with Clement With the supply of...</td>\n",
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              "      <td>Martinez leaves bitter Like Roger Clemens did ...</td>\n",
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              "      <td>EBay gets into rentals EBay plans to buy the a...</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-b808c093-7721-4dc8-91d3-f6bfddedd23b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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            "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]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": 515
        },
        "id": "FwJWS2zzhv6E",
        "outputId": "8c49c916-77cd-462d-aeee-6e6f27d0dc07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The maximum length of this dataset is approximately 177 words.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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": "7d56e730-aa16-47a5-b7fd-558050792b92"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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": "5bfef06d-1da6-4a93-db52-1c70e1ccc3f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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 = 64\n",
        "MODEL_CKPT = \"distilgpt2\"\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": [
            "f36e2704ba81465e90f1ea4d59ebc736",
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            "137a228cd94a4b67af651d4a944293af",
            "b6886618f59b42f18e4ac4371e7da0ca",
            "1446780e751a45da8ff2a9bea897f619",
            "ff93dd39cf4e43649fa96ae8b1be4e6d",
            "0cf848952cac48d6899feb21f7f80b7c",
            "e41834b56ea64aa4a8274992ec5cd743",
            "556c4b8f254e4ae5a50989a96238ee26",
            "1bd740c2206c45e4b3df775c7cc84cf8",
            "216128f7982d4c22809338d6c367c5b1",
            "d979c79c11ea4af4af8b4160b59561d8",
            "7e1b0cbc545644249f8627be20aa3ceb",
            "8e51a12d314c4768b3e2d4f2ac74570c",
            "923796fbaf6a4ab6a13559e35f4b8518",
            "07ecd73d28524bf1b89abd4dad572813",
            "0153875516834d089c946ee1a76a6a67",
            "5b01a0746b604f3783c9e0348a3e6ed4",
            "5047e656dbe84e79a1df700606ec15ea",
            "019359523eaf4eeeab0a03f5505075d8",
            "f2a616d3c4014398a68af305f92d6a1f",
            "6786023203244963bdc435ee2fb11a93",
            "9b7d430e71b74f6abbfc2c6269e8410b",
            "a3988d171f5548e899f64c53c1a2dcce",
            "f44a1a31f9dd4924aa8f1ba8d2b03251",
            "d5e19a789584481eb354f1c947caf84e",
            "26d735ea143e4a14b3e6aec14b69358f",
            "53f656633cfc49f191bf6927542c675d",
            "f2ac0496c1994898954465b5ad498a89",
            "a28c89e1aa614d76a2de5734bfce242e",
            "2be2fa0020ba411284472dad4fb931cf",
            "3d88ba2f3c104655a238f0136ac1bc65",
            "f94ffdf3e7f247c78f3eb40ed9052419",
            "8defc05e29874d418f056b6f07ac8ce1"
          ]
        },
        "id": "6dHzppm_hv6G",
        "outputId": "4c4ae45b-dc96-4b1c-ee6d-9641ee86c011"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f36e2704ba81465e90f1ea4d59ebc736",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "637efaf1a01e47b3852fcb8f24d34a59",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e1b0cbc545644249f8627be20aa3ceb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3988d171f5548e899f64c53c1a2dcce",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": 86,
          "referenced_widgets": [
            "b1f04bdc994f4cb2a7d720614a564b5e",
            "fff5fd30729647e3a91918a0294669a3",
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            "71d19c96a61140b59635c4861832a70c",
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            "c890765245534aa58744ea2fc251eb0a",
            "a16f1613ac7845d8a27d001e76e9729f",
            "fa11bfc346a24062ae67b5c1ed85bd07",
            "657c0914adbd4056a844ade8ef8f4eaf",
            "54e63f87bc82454f87658ef13907eaa3",
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            "fa1eb8f171c84cc6b5722a3c97d12588",
            "3301db3c874d481ead1de58897ba935a",
            "4956cd0878414c48b6833626482839e7",
            "27427a64cbac481489acde38660fb5bf",
            "de6bf5d686fd4e53b22958377273bc07",
            "cd31b1d4ab70477fa9c68ba4d71e227d",
            "34388c3581324f03a89e432dfd8d58f4"
          ]
        },
        "id": "2iKvu1odhv6G",
        "outputId": "4742ee3e-94fd-472c-9db8-17cb6b33625a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b1f04bdc994f4cb2a7d720614a564b5e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "94738e6f960d45c2a2acd2fe2677b22c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'attention_mask'],\n",
              "    num_rows: 120000\n",
              "})"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_ds = ds.map(tokenizer_function, \n",
        "                      batched=True,\n",
        "                      remove_columns=ds[\"train\"].column_names,)\n",
        "\n",
        "tokenized_ds[\"train\"]"
      ]
    },
    {
      "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": [
            "c5adb5d7d87243d58552ed0ce4ba7731",
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            "11fc53e053ce4cb49578c49ccebffb54",
            "317e8c48ab83403989b6725637fb2598",
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            "46ebf3e9fa394e7db72b14030e671cbe",
            "b0f3340831de4b4584090fcab2ae0e11",
            "fc5d3778e1ec4ffc83f802d3c1dbe6da",
            "cdb90d697c42433f80dcdd7efa7f532d",
            "942c405dcfa3467c96415f2d8cad01c1",
            "bd9f1c7e54cd4628be24adfa0cffe1a1",
            "c8ce03b3bfcb45bfaad100b8792b0d5c",
            "10efbbfe8d9f4c7083ea17fc82a30b9d",
            "3fb7a358fe4d48dfaaa2d082e261ebbd",
            "fa932878fc2e42679921ef9512567f1e",
            "e0acd91520264d189707218d0d94934a",
            "860130efdeae47cd99f31c2607355a65",
            "47cf63551f4b4ceba238313ad2dce507",
            "4c23839477df4aff8e31e2ebe2c250cd",
            "d96373f19498467f97ef3f673a568af2",
            "5ca87a5105f04d7dafe0ac747384b741"
          ]
        },
        "id": "trQJcO9Bhv6H",
        "outputId": "14c027cd-ad85-4110-d48a-6c9b72f19cde"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5adb5d7d87243d58552ed0ce4ba7731",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd9f1c7e54cd4628be24adfa0cffe1a1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": [
            "586bec7103c540bfb89e7cfbb18c5515",
            "3ebd46b5ebdd43168202d0cb770c76b1",
            "fcbf319c53684970852962e75b318702",
            "8d9ecf8484ad47f3ae48356610e6c4ad",
            "796675ef903e4ecb9aa652d5d98e40b9",
            "d0913e6c61f24bdba6d8bdb28789ab53",
            "32dca2e315204b9ab583b890704b9242",
            "1a6f9666265342a1b97f1c5b33467f52",
            "43b2fab8888342e79c8cfae79bd603f3",
            "6790835d072d4139baba87646f981988",
            "b8d3c281274449918699673f72f63aed",
            "bdd57e770b9a4d178d476f04b93914d8",
            "1a49d3525c2843819ec6e1a2e3b9c87b",
            "e0eed53548124f3e87640e96282748a8",
            "e1367b99f0b242e3a615a0541c2f050e",
            "faf487c5e4c84360bb1ae23eb8e5cfa8",
            "355dc2b66cbc4e65968b0f6cd5cfa20d",
            "ae5484e5e88d40b68b4ec397ea585158",
            "9dd6b1c2cf6946c18130fd9d923e95c1",
            "280199ab56a747fbb8907df18110462c",
            "0a06906e024b48aabfae3dce230cfd87",
            "004fdccf01484da0a73ba6d9c2f16576"
          ]
        },
        "id": "QsCBa-c4hv6H",
        "outputId": "0408faa5-26b5-4029-816f-2e4b35637c95"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "586bec7103c540bfb89e7cfbb18c5515",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/353M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdd57e770b9a4d178d476f04b93914d8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": 19,
      "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": 20,
      "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": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPGY499qhv6I",
        "outputId": "dbd99be7-12de-428c-b628-8c90bb34e0bd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/distilgpt2-Causal_Language_Model-AG_News into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/distilgpt2-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": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "7qD1Wmuwhv6I",
        "outputId": "0675e1c0-d05d-4143-c581-b547f50932ff"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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 = 97646\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 = 36618\n",
            "  Number of trainable parameters = 81912576\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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='36618' max='36618' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [36618/36618 1:16:55, 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>3.834300</td>\n",
              "      <td>3.556036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>3.574500</td>\n",
              "      <td>3.474413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>3.492200</td>\n",
              "      <td>3.450913</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 6129\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to distilgpt2-Causal_Language_Model-AG_News/checkpoint-12206\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-12206/config.json\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-12206/generation_config.json\n",
            "Model weights saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-12206/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 6129\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to distilgpt2-Causal_Language_Model-AG_News/checkpoint-24412\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-24412/config.json\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-24412/generation_config.json\n",
            "Model weights saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-24412/pytorch_model.bin\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 6129\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to distilgpt2-Causal_Language_Model-AG_News/checkpoint-36618\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-36618/config.json\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-36618/generation_config.json\n",
            "Model weights saved in distilgpt2-Causal_Language_Model-AG_News/checkpoint-36618/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()"
      ]
    },
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        "id": "60expOoLhv6I"
      },
      "source": [
        "#### Evaluate Model"
      ]
    },
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      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "UZfPJ0cfhv6I",
        "outputId": "cd0fcfea-1724-4823-f3a9-84cfcd4493bc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 6129\n",
            "  Batch size = 8\n"
          ]
        },
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              "      [767/767 00:23]\n",
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              "    "
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          "metadata": {},
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          "text": [
            "Perplexity: 31.53\n"
          ]
        }
      ],
      "source": [
        "evaluation_results = trainer.evaluate()\n",
        "print(f\"Perplexity: {math.exp(evaluation_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9S3q9gNhv6I"
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      "source": [
        "#### Push Model to HuggingFace Hub"
      ]
    },
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          "name": "stderr",
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          "text": [
            "Saving model checkpoint to distilgpt2-Causal_Language_Model-AG_News\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/config.json\n",
            "Configuration saved in distilgpt2-Causal_Language_Model-AG_News/generation_config.json\n",
            "Model weights saved in distilgpt2-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"
          ]
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            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 32.0k/318M [00:00<?, ?B/s]"
            ]
          },
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              "Upload file runs/Mar14_00-36-14_c0eac7fc11b5/events.out.tfevents.1678754188.c0eac7fc11b5.3483.0: 100%|########…"
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          },
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              "Upload file runs/Mar14_00-36-14_c0eac7fc11b5/events.out.tfevents.1678758831.c0eac7fc11b5.3483.2: 100%|########…"
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          "text": [
            "remote: Scanning LFS files of refs/heads/main for validity...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/DunnBC22/distilgpt2-Causal_Language_Model-AG_News\n",
            "   44822d1..35f8abf  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/distilgpt2-Causal_Language_Model-AG_News\n",
            "   44822d1..35f8abf  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/distilgpt2-Causal_Language_Model-AG_News\n",
            "   35f8abf..745c855  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/distilgpt2-Causal_Language_Model-AG_News\n",
            "   35f8abf..745c855  main -> main\n",
            "\n"
          ]
        },
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              "'https://huggingface.co/DunnBC22/distilgpt2-Causal_Language_Model-AG_News/commit/35f8abfa329919899e04a7831c2eb2f6b292b314'"
            ]
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          "execution_count": 24,
          "metadata": {},
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        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "attachments": {},
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      "metadata": {},
      "source": [
        "### Citations\n",
        "\n",
        "- Model Checkpoint\n",
        "    - @inproceedings{sanh2019distilbert,\n",
        "  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},\n",
        "  author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},\n",
        "  booktitle={NeurIPS EMC^2 Workshop},\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",
        "}"
      ]
    }
  ],
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