Masked Language Model/US Economic News Articles/US_Economic_News_Articles_MLM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HB6ucLYhPmbz"
      },
      "source": [
        "## 2,000 Cleaned Medical Articles: Masked Language Modeling\n",
        "\n",
        "\n",
        "Dataset Source: https://www.kaggle.com/datasets/trikialaaa/2k-clean-medical-articles-medicalnewstoday"
      ]
    },
    {
      "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": "370d6f33-12b9-470e-d654-36e0ae87157c"
      },
      "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-4.27.1 xxhash-3.2.0 yarl-1.8.2\n"
          ]
        }
      ],
      "source": [
        "%pip install transformers datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jkf9PyX-Pmb0"
      },
      "source": [
        "#### Import Necessary & Missing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMNOIhymPmb0",
        "outputId": "7b01e2bd-295e-42a6-a46e-d0ffad4dbf4e"
      },
      "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, AutoModelForMaskedLM, DataCollatorForLanguageModeling\n",
        "from transformers import TrainingArguments, Trainer, set_seed\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TallIFwUPmb1"
      },
      "source": [
        "#### Display Library Versions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "vtOYQ9R_Pmb1",
        "outputId": "081bb91d-ae49-4fd2-ddac-97b2445023d5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Python: 3.9.16\n",
            "        Pandas: 1.4.4\n",
            "      Datasets: 2.10.1\n",
            "  Transformers: 4.27.1\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": "MJ0Z721Pmywm"
      },
      "source": [
        "#### Login For Access to HuggingFace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0vDqT1nmymR",
        "outputId": "93a758e3-c197-4071-88c6-9d29630cb857"
      },
      "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"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "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": "2b12d287-03e4-440c-9077-efa220a8634a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozt2NTNIPmb1"
      },
      "source": [
        "#### Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "TXYdhDN2Pmb1",
        "outputId": "1b2dfe1d-7f50-4ad3-b5aa-d3246039a7d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<p>8000 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-667e38c3-44b6-470a-8069-0d5636122a65')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-667e38c3-44b6-470a-8069-0d5636122a65 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-667e38c3-44b6-470a-8069-0d5636122a65');\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": 6
        }
      ],
      "source": [
        "parent_dir = \"/content/drive/MyDrive/Colab Notebooks/US Econ Articles\"\n",
        "\n",
        "data_file = os.path.join(parent_dir, \"US-Economic-News.csv\")\n",
        "\n",
        "data = pd.read_csv(data_file, encoding_errors=\"ignore\", usecols=[\"text\"])\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ML19xt7hPmb1"
      },
      "source": [
        "#### Visual: Histogram of Input Lengths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "YJp5msvtPmb1",
        "outputId": "f5f2fd42-3ba4-4b24-d783-32bb7d9db5cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The maximum length of this dataset is approximately 863 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": [
        "data['input_len'] = data['text'].str.split().str.len()\n",
        "\n",
        "max_len = max(data['input_len'])\n",
        "\n",
        "print(f\"The maximum length of this dataset is approximately {max_len} words.\")\n",
        "\n",
        "n = max_len\n",
        "data.hist(\"input_len\", 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": "CTXwatYYPmb1"
      },
      "source": [
        "#### Remove Unnecessary Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "msYLXr_iPmb1",
        "outputId": "1cee7b02-aa7f-4064-b796-059a8996c316",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text\n",
              "0     NEW YORK -- Yields on most certificates of dep...\n",
              "1     The Wall Street Journal Online</br></br>The Mo...\n",
              "2     WASHINGTON -- In an effort to achieve banking ...\n",
              "3     The statistics on the enormous costs of employ...\n",
              "4     NEW YORK -- Indecision marked the dollar's ton...\n",
              "...                                                 ...\n",
              "7995  Secretary of Commerce Charles W. Sawyer said y...\n",
              "7996  U.S. stocks inched up last week, overcoming co...\n",
              "7997  Ben S. Bernanke cleared a key hurdle Thursday ...\n",
              "7998  The White House's push to contract out many fe...\n",
              "7999  NEW YORK. April 17-Automobile stocks put on th...\n",
              "\n",
              "[8000 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4796d4e5-da92-4c24-a54b-3930328d916c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NEW YORK -- Yields on most certificates of dep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Wall Street Journal Online&lt;/br&gt;&lt;/br&gt;The Mo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>WASHINGTON -- In an effort to achieve banking ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The statistics on the enormous costs of employ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NEW YORK -- Indecision marked the dollar's ton...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7995</th>\n",
              "      <td>Secretary of Commerce Charles W. Sawyer said y...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7996</th>\n",
              "      <td>U.S. stocks inched up last week, overcoming co...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7997</th>\n",
              "      <td>Ben S. Bernanke cleared a key hurdle Thursday ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7998</th>\n",
              "      <td>The White House's push to contract out many fe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7999</th>\n",
              "      <td>NEW YORK. April 17-Automobile stocks put on th...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8000 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4796d4e5-da92-4c24-a54b-3930328d916c')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4796d4e5-da92-4c24-a54b-3930328d916c 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-4796d4e5-da92-4c24-a54b-3930328d916c');\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": 8
        }
      ],
      "source": [
        "data = data.drop(columns=[\"input_len\"])\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lj1Hy366Pmb2"
      },
      "source": [
        "#### Convert DataFrame to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "vjoz14C7Pmb2",
        "outputId": "feae1fd0-f82d-4b7f-93e7-6ecb31983faf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 8000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dataset = Dataset.from_pandas(data)\n",
        "\n",
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DH6Fh97KPmb2"
      },
      "source": [
        "#### Split Dataset into Training & Evaluation Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "eTq6cgq9Pmb2",
        "outputId": "dae160a1-9d03-421c-8a0e-b97c18b4f5be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (6400, 1)\n",
            "Evaluation data shape: (1600, 1)\n",
            "{'text': Value(dtype='string', id=None)}\n"
          ]
        }
      ],
      "source": [
        "train_testvalid = dataset.train_test_split(test_size=0.20)\n",
        "\n",
        "ds = DatasetDict({\n",
        "    'train' : train_testvalid['train'],\n",
        "    'eval' : train_testvalid['test']\n",
        "})\n",
        "\n",
        "print('Training data shape:', ds['train'].shape)\n",
        "print('Evaluation data shape:', ds['eval'].shape)\n",
        "print(ds['train'].features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1-VrAVymPmb2"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "PzPXMnQqPmb2"
      },
      "outputs": [],
      "source": [
        "BLOCK_SIZE = 128\n",
        "MODEL_CKPT = \"bert-base-uncased\"\n",
        "\n",
        "MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-Masked_Language_Model-US_Economic_News_Articles\"\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\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCHkZhn4Pmb2"
      },
      "source": [
        "#### Tokenize Entire Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "WyHjGSn6Pmb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257,
          "referenced_widgets": [
            "607b621698db46eba0a83dadef6d02ec",
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            "be36cbffdcaa48aaaa92c210c46f143e",
            "d89049806b3e44f78c00ff45c7d9d15b",
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            "c276ca29b08d4fec9462ac473d203d6e",
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            "06757f7288cc46dfb09e34ae33b84b16",
            "8ebe7b9b55b64039bd96f6770cc9d58f"
          ]
        },
        "outputId": "f242f880-f255-4fc0-e272-e4ef1ca48939"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "607b621698db46eba0a83dadef6d02ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "417756021c914a1fad6f9b585089d841"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2071f7c55860461ba9eef6cb287cbe76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d89049806b3e44f78c00ff45c7d9d15b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT, \n",
        "                                          use_fast=True)\n",
        "\n",
        "def tokenizer_function(samples):\n",
        "    return tokenizer(samples[\"text\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzoz_3OhPmb2"
      },
      "source": [
        "#### Apply Tokenizer Function to Entire Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315,
          "referenced_widgets": [
            "0964f1c1da044e45b89b18979c5c3033",
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            "75bf1c03d1154767aaf71048d6709dec",
            "3a860ac321474193a0377040e48cc890",
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          ]
        },
        "id": "yNRhD3GNPmb2",
        "outputId": "57c3ea0e-5a98-4af7-cc14-fce6f7bfda23"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/6400 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0964f1c1da044e45b89b18979c5c3033"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (637 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (550 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (528 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (644 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/1600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cec76ce70eaf4d54aab4a1020bf19996"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (591 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (525 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (518 > 512). Running this sequence through the model will result in indexing errors\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (561 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 6400\n",
            "})\n",
            "Dataset({\n",
            "    features: ['input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 1600\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "tokenized_ds = ds.map(tokenizer_function, \n",
        "                      batched=True,\n",
        "                      num_proc=4,\n",
        "                      remove_columns=[\"text\"])\n",
        "\n",
        "print(tokenized_ds[\"train\"])\n",
        "print(tokenized_ds[\"eval\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfY6kLz1Pmb2"
      },
      "source": [
        "#### Preprocess Tokenized Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "qBxlfl1fPmb2"
      },
      "outputs": [],
      "source": [
        "def group_texts(samples):\n",
        "    concatenated_samples = {k: sum(samples[k], []) for k in samples.keys()}\n",
        "    total_length = len(concatenated_samples[list(samples.keys())[0]])\n",
        "    \n",
        "    if total_length >= BLOCK_SIZE:\n",
        "        total_length = (total_length // BLOCK_SIZE) * BLOCK_SIZE\n",
        "    \n",
        "    result = {\n",
        "        k: [t[i : i + BLOCK_SIZE] for i in range(0, total_length, BLOCK_SIZE)]\n",
        "        for k, t in concatenated_samples.items()\n",
        "    }\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fpHRysNPmb2"
      },
      "source": [
        "#### Apply Preprocessing Function to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "5c88e19358b9491e99ee127fd8c616b0",
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            "4df6955145f545078f3e9deabd5ba22d",
            "0c6da46917a44bf292fbe38076624e9c",
            "96c1095cad17475692ea8f72b1204592",
            "7f8122f74f4d4525a37f9b92c1cc4878",
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            "1f599ddb12f3460bbb09121328b11814",
            "bd15320af7554ee0b43760613f7cba04",
            "d83245d0d8064a529940694d32bc9cf4",
            "0792e455919b4e2780637bc404233964",
            "09b3ced24c354daba0d73e0c49d1581e",
            "f3d0e8130518447fae76c2f9cc82e4e2",
            "2d01c0ed66824f8fad37def49564e859",
            "da17bef174a64163aa9d4fec5e1e317f",
            "e55811a02c6b4c0ca5db1b5e337750b9",
            "da4ccc8f3ca34d50810e762fa2db34b9",
            "16f82c4f937e4aa7bb49124ff9205ddf",
            "914a62131ac54cfe9c25087452060392",
            "36949f9c1120417e94c8975df3ac06af",
            "25cc00220aa94097841e5beaab727a2f"
          ]
        },
        "id": "7VREBTAlPmb2",
        "outputId": "bfa15e62-98b9-43d0-e02f-6f50dee3102f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/6400 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c88e19358b9491e99ee127fd8c616b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/1600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0792e455919b4e2780637bc404233964"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "mlm_ds = tokenized_ds.map(\n",
        "    group_texts,\n",
        "    batched=True,\n",
        "    num_proc=4,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BtpGC2KPmb3"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IDNQxTLcPmb3",
        "outputId": "e151fa35-7ea0-4c1d-d7aa-41f2e7fce76e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149,
          "referenced_widgets": [
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "model = (\n",
        "    AutoModelForMaskedLM.from_pretrained(MODEL_CKPT)\n",
        "    ).to(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lQpRLfVPmb3"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "g4xixMg2Pmb3"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    output_dir=MODEL_NAME,\n",
        "    evaluation_strategy=STRATEGY,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    report_to=REPORTS_TO,\n",
        "    logging_first_step=True,\n",
        "    logging_strategy=STRATEGY,\n",
        "    save_strategy=STRATEGY,\n",
        "    hub_private_repo=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JA4_o-HPPmb3"
      },
      "source": [
        "#### Define Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "iwMGBGvPPmb3"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZVp_TzVPmb3"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "V6rkJ4xsPmb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76c87838-b70a-4a7f-a14f-eadb4e863e12"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=mlm_ds[\"train\"],\n",
        "    eval_dataset=mlm_ds[\"eval\"],\n",
        "    data_collator=data_collator\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uddY9AeCPmb3"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "igK4JrIQPmb3",
        "outputId": "be785a3f-c4ea-4b07-d7f3-5f6332a6cd8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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",
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6048' max='6048' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6048/6048 28:27, 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>2.183300</td>\n",
              "      <td>1.952892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.004000</td>\n",
              "      <td>1.900214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.941000</td>\n",
              "      <td>1.859981</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-p9T3PPHPmb3"
      },
      "source": [
        "#### Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "hIE6U1ZmPmb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "f2e36e75-944c-41ea-ff9c-5d442293298c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 00:35]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perplexity: 6.25\n"
          ]
        }
      ],
      "source": [
        "evaluation_results = trainer.evaluate()\n",
        "print(f\"Perplexity: {math.exp(evaluation_results['eval_loss']):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LoMIM61Pmb3"
      },
      "source": [
        "#### Push Model to HuggingFace Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
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        "outputId": "ed3b5741-6528-4cea-ed29-05a4eb7f0409"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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/418M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Mar16_00-26-54_2de48317bc15/events.out.tfevents.1678926422.2de48317bc15.390.0: 100%|#########…"
            ],
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        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Upload file runs/Mar16_00-26-54_2de48317bc15/events.out.tfevents.1678928168.2de48317bc15.390.2: 100%|#########…"
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          "metadata": {}
        },
        {
          "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/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles\n",
            "   bea5700..a602116  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/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles\n",
            "   bea5700..a602116  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles\n",
            "   a602116..77a6e74  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles\n",
            "   a602116..77a6e74  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles/commit/a602116fac66e54bc67f77d4dec157568a3e9792'"
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          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzntNzljPmb3"
      },
      "source": [
        "### Citations\n",
        "\n",
        "- Model Checkpoint\n",
        "> @article{DBLP:journals/corr/abs-1810-04805,\n",
        "  author    = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova},\n",
        "  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding},\n",
        "  journal   = {CoRR},\n",
        "  volume    = {abs/1810.04805},\n",
        "  year      = {2018},\n",
        "  url       = {http://arxiv.org/abs/1810.04805},\n",
        "  archivePrefix = {arXiv},\n",
        "  eprint    = {1810.04805},\n",
        "  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},\n",
        "  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},\n",
        "  bibsource = {dblp computer science bibliography, https://dblp.org}\n",
        "}\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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