Multiple Choice/e-CARE/e_CARE_Multiple_Choice_Using_BERT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZD4twFq3N_Y"
      },
      "source": [
        "## e-CARE: Multiple Choice\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/12ml/e-CARE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UD3toSDlqjP0"
      },
      "source": [
        "#### Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1shpgeOqjHG",
        "outputId": "7b894a8c-964a-429f-e33b-4d6f78edbaa1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: accelerate\n",
            "Successfully installed accelerate-0.21.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "%pip install pip -U\n",
        "%pip install torch datasets\n",
        "%pip install transformers tensorboard\n",
        "%pip install accelerate -U"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqsZU2oIqi_x"
      },
      "source": [
        "#### Enter HuggingFace Access Token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7BoyGabqi4Q",
        "outputId": "ab815c13-2c82-43ce-abce-099d76c83b15"
      },
      "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) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oLt7lOQ3N_a"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wJeoEVw3N_a",
        "outputId": "ccd632e3-51e6-4bc9-f48a-dabca9f4291b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys, random, ast\n",
        "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import datasets\n",
        "from datasets import Dataset, DatasetDict, load_dataset\n",
        "\n",
        "import torch\n",
        "\n",
        "from dataclasses import dataclass\n",
        "\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForMultipleChoice\n",
        "from transformers import Trainer, TrainingArguments, set_seed\n",
        "from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy\n",
        "\n",
        "from typing import Optional, Union\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-zPaQ163N_c"
      },
      "source": [
        "#### Display Library Versions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NPXamJr3N_c",
        "outputId": "583635b1-aa9e-4dc6-e25c-477309cee959"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        Python: 3.10.1\n",
            "         NumPy: 1.22.4\n",
            "        Pandas: 1.5.3\n",
            "         Torch: 2.0.1+cu118\n",
            "      Datasets: 2.14.2\n",
            "  Transformers: 4.31.0\n"
          ]
        }
      ],
      "source": [
        "print(\"Python:\".rjust(15), sys.version[0:6])\n",
        "print(\"NumPy:\".rjust(15), np.__version__)\n",
        "print(\"Pandas:\".rjust(15), pd.__version__)\n",
        "print(\"Torch:\".rjust(15), torch.__version__)\n",
        "print(\"Datasets:\".rjust(15), datasets.__version__)\n",
        "print(\"Transformers:\".rjust(15), transformers.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2wzL6zW3N_c"
      },
      "source": [
        "#### Load Dataset & Convert To Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        },
        "id": "NDxDlQVb3N_c",
        "outputId": "e66b7162-4c74-4c65-e740-79ae425984d0"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "a8fb8fe42ff94a1cae45f1d768f391ee",
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
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          },
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        },
        {
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            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
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        {
          "data": {
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              "Downloading data:   0%|          | 0.00/2.86M [00:00<?, ?B/s]"
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              "Downloading data:   0%|          | 0.00/404k [00:00<?, ?B/s]"
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        {
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            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
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          },
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        {
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            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>context</th>\n",
              "      <th>question</th>\n",
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              "      <th>1</th>\n",
              "      <th>labels</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>There is a light rain today.</td>\n",
              "      <td>effect</td>\n",
              "      <td>The roots of many plants are not moistened by ...</td>\n",
              "      <td>Tourists have seen many ripples.</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>Susan wants to buy a restricted pesticide.</td>\n",
              "      <td>effect</td>\n",
              "      <td>She bought rotenone.</td>\n",
              "      <td>He decided to buy amylase.</td>\n",
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              "    <tr>\n",
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              "      <td>They disappear immediately after finishing eat...</td>\n",
              "      <td>They moved to a cave.</td>\n",
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              "      <th>4</th>\n",
              "      <td>She mainly studied drugs.</td>\n",
              "      <td>cause</td>\n",
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              "      <td>Susan majored in pharmacology at university.</td>\n",
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              "      <td>They have unified structures.</td>\n",
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              "      <td>effect</td>\n",
              "      <td>He was poisoned.</td>\n",
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              "    <tr>\n",
              "      <th>2119</th>\n",
              "      <td>He tried to caught the bass.</td>\n",
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              "      <th>2120</th>\n",
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              "      <td>effect</td>\n",
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              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b719064f-c353-42c1-86ad-e4a0f3ed5733 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-b0202a2d-a37e-4e4e-83c7-6d671e990d94 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-b0202a2d-a37e-4e4e-83c7-6d671e990d94');\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"
            ],
            "text/plain": [
              "                                                context question  \\\n",
              "0                          There is a light rain today.   effect   \n",
              "1            Susan wants to buy a restricted pesticide.   effect   \n",
              "2                              His parents stopped him.    cause   \n",
              "3     The shamans were to start their sacred ceremon...   effect   \n",
              "4                             She mainly studied drugs.    cause   \n",
              "...                                                 ...      ...   \n",
              "2114           Her body had excessive radiation levels.    cause   \n",
              "2115                  Their company has a team culture.   effect   \n",
              "2116        The old man ate Benzodiazepines by mistake.   effect   \n",
              "2119                       He tried to caught the bass.   effect   \n",
              "2120         Tom went to the dentist to fix his caries.   effect   \n",
              "\n",
              "                                                      0  \\\n",
              "0     The roots of many plants are not moistened by ...   \n",
              "1                                  She bought rotenone.   \n",
              "2                         The child ran towards hippos.   \n",
              "3     They disappear immediately after finishing eat...   \n",
              "4                     Lily likes the art form of truth.   \n",
              "...                                                 ...   \n",
              "2114  Amy overexposed her skin to the ultraviolet ra...   \n",
              "2115                      They have unified structures.   \n",
              "2116                                   He was poisoned.   \n",
              "2119              He bass bit him with its small mouth.   \n",
              "2120       The treatment process producedamalgam waste.   \n",
              "\n",
              "                                                      1  labels  \n",
              "0                      Tourists have seen many ripples.       0  \n",
              "1                            He decided to buy amylase.       0  \n",
              "2     Mike wanted to have a bird who has the lest nu...       0  \n",
              "3                                 They moved to a cave.       1  \n",
              "4          Susan majored in pharmacology at university.       1  \n",
              "...                                                 ...     ...  \n",
              "2114          Madame Curie studied radium all her life.       1  \n",
              "2115    Everyone, including the boss, is a team player.       1  \n",
              "2116                                    He had amnesia.       1  \n",
              "2119    The bass opened its mouth and guzzled his hand.       1  \n",
              "2120  The doctor said that Tom had high blood pressure.       0  \n",
              "\n",
              "[15704 rows x 5 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = load_dataset(\"12ml/e-CARE\")\n",
        "\n",
        "data = data.rename_columns({\"premise\": \"context\",\n",
        "                            \"choice1\": \"0\",\n",
        "                            \"choice2\": \"1\",\n",
        "                            \"label\": \"labels\",\n",
        "                            })\n",
        "\n",
        "data.set_format(type=\"pandas\")\n",
        "\n",
        "df = pd.concat([data['train'][:], data['validation'][:]])\n",
        "\n",
        "df = df.dropna(how='all')\n",
        "\n",
        "df = df.drop_duplicates(subset='context')\n",
        "\n",
        "df = df.drop(columns=[\"idx\", \"conceptual_explanation\"])\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_QRQyBY3N_d"
      },
      "source": [
        "#### Visual: Word Length of Training Dataset Inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "fAJOlONK3N_d",
        "outputId": "2aad4454-3089-45f8-dc98-92ffb9aff134"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The maximum length of the questions (in words) is 28 words.\n"
          ]
        }
      ],
      "source": [
        "df['input_length'] = df['context'].str.split().str.len()\n",
        "\n",
        "n_bin= 30\n",
        "df.hist('input_length', grid=False, figsize=(18,8), range=(0,n_bin), bins=n_bin)\n",
        "plt.title('Histogram: Length of Contexts & Questions Combined')\n",
        "plt.xlabel('# of Words in Contexts & Questions Combined')\n",
        "plt.ylabel('# of Samples')\n",
        "plt.show()\n",
        "\n",
        "print(f\"The maximum length of the questions (in words) is {max(df['input_length'])} words.\")\n",
        "\n",
        "df = df.drop(columns=['input_length'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDhHc-m93N_d"
      },
      "source": [
        "#### Convert from DataFrame to Dataset & Split into Training/Testing/Evaluation Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efPhxjaJ3N_d",
        "outputId": "c5237cf1-c72e-4e71-ebea-4900880b9e55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Shape: (12563, 6)\n",
            "Testing Dataset Shape: (1570, 6)\n",
            "Evaluation Dataset Shape: (1571, 6)\n",
            "{'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), '0': Value(dtype='string', id=None), '1': Value(dtype='string', id=None), 'labels': Value(dtype='int64', id=None), '__index_level_0__': Value(dtype='int64', id=None)}\n"
          ]
        }
      ],
      "source": [
        "dataset = Dataset.from_pandas(df)\n",
        "\n",
        "train_testeval = dataset.train_test_split(test_size=0.20)\n",
        "test_eval = train_testeval['test'].train_test_split(test_size=0.50)\n",
        "\n",
        "ds = DatasetDict({\n",
        "    'train' : train_testeval['train'],\n",
        "    'test' : test_eval['train'],\n",
        "    'eval' : test_eval['test'],\n",
        "})\n",
        "\n",
        "print(\"Training Dataset Shape:\", ds['train'].shape)\n",
        "print(\"Testing Dataset Shape:\", ds['test'].shape)\n",
        "print(\"Evaluation Dataset Shape:\", ds['eval'].shape)\n",
        "\n",
        "print(ds['train'].features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWMqMAZk3N_d"
      },
      "source": [
        "#### Display Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1o1YjIj3N_d",
        "outputId": "3ee42fad-53b0-44d0-84c5-46b2e80df167"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'context': 'Rose is a millionaire.',\n",
              " 'question': 'effect',\n",
              " '0': 'Rose has a lot of big houses and companies.',\n",
              " '1': 'Her sunglasses protect the delicate skin around the eyes.',\n",
              " 'labels': 0,\n",
              " '__index_level_0__': 2186}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ds['train'][12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1M-2gL-3N_d"
      },
      "source": [
        "#### Create Function to Display an Example Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "kI90oagW3N_d"
      },
      "outputs": [],
      "source": [
        "def show_one_example(sample):\n",
        "    print(f\"Context: {sample['context']} {sample['question']}:\")\n",
        "    print(f\"Option 0: {sample['0']}\")\n",
        "    print(f\"Option 1: {sample['1']}\")\n",
        "    print(f\"\\nGround Truth: Option {sample['labels']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NabjT-2g3N_e"
      },
      "source": [
        "#### Display an Example Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBL9iVKj3N_e",
        "outputId": "c687a226-91e5-413a-9a9c-d3ba1024a335"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Context: Rose is a millionaire. effect:\n",
            "Option 0: Rose has a lot of big houses and companies.\n",
            "Option 1: Her sunglasses protect the delicate skin around the eyes.\n",
            "\n",
            "Ground Truth: Option 0\n"
          ]
        }
      ],
      "source": [
        "show_one_example(ds['train'][12])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTtopMV13N_e"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "5-9tnDc53N_e"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT = \"bert-base-uncased\"\n",
        "MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-e_CARE\"\n",
        "\n",
        "STRATEGY = \"epoch\"\n",
        "LEARNING_RATE = 5e-5\n",
        "\n",
        "BATCH_SIZE = 16\n",
        "NUM_OF_EPOCHS = 5\n",
        "\n",
        "WEIGHT_DECAY = 0.01\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "\n",
        "set_seed = 42"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bq6M6Sp23N_e"
      },
      "source": [
        "#### Instantiate Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "1e6a996353ca4eef9769160f4985cdde",
            "e8696a47cf584c17ba2366de93f2cbc3",
            "9ed25b8e1c5c421babcad8c792d2cb6c"
          ]
        },
        "id": "PnsRZqGo3N_e",
        "outputId": "f21d94de-1eaa-4db2-b9fa-359b0a35645e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "999c5070659c426caa4deb82c758a184",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0f9bcf7f2d5497eb52815622b15b325",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7aa845326b7643738395dccc6afa5c9d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6550ea6a4e8145a2a04fefc2856d1caf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT,\n",
        "                                          use_fast=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfis23iQ3N_e"
      },
      "source": [
        "#### Create Preprocessing Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "z67IVjq33N_e"
      },
      "outputs": [],
      "source": [
        "ending_names = [\"0\", \"1\"]\n",
        "\n",
        "def preprocess_function(samples):\n",
        "    first_sentences = [[context] * len(ending_names) for context in samples['context']]\n",
        "\n",
        "    question_headers = samples['question']\n",
        "    second_sentences = [[f\"{header} {samples[end][i]}\" for end in ending_names] for i, header in enumerate(question_headers)]\n",
        "\n",
        "    first_sentences = sum(first_sentences, [])\n",
        "    second_sentences = sum(second_sentences, [])\n",
        "\n",
        "    tokenized_samples = tokenizer(first_sentences, second_sentences, truncation=True)\n",
        "    return {k: [v[i:i + len(ending_names)] for i in range(0, len(v), len(ending_names))] for k, v in tokenized_samples.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQ6DirzE3N_e"
      },
      "source": [
        "#### Apply Preprocessing Function to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        },
        "id": "qHuwNhJh3N_e",
        "outputId": "45c0c934-18e5-4b03-abb2-33ae7a5dc3ff"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7da9966ac1324a17b9c7973898297a9c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/12563 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e4861e494f942dca0fe05c6470afe36",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1570 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e4f28953a9d0468ba86924a64f2f905d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1571 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "encoded_ds = ds.map(preprocess_function,\n",
        "                    batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWijn90l3N_e",
        "outputId": "0ea56050-ff4a-4a84-b603-ca86a67cee6d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'context': Value(dtype='string', id=None),\n",
              " 'question': Value(dtype='string', id=None),\n",
              " '0': Value(dtype='string', id=None),\n",
              " '1': Value(dtype='string', id=None),\n",
              " 'labels': Value(dtype='int64', id=None),\n",
              " '__index_level_0__': Value(dtype='int64', id=None),\n",
              " 'input_ids': Sequence(feature=Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), length=-1, id=None),\n",
              " 'token_type_ids': Sequence(feature=Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), length=-1, id=None),\n",
              " 'attention_mask': Sequence(feature=Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), length=-1, id=None)}"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "encoded_ds['train'].features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hG8gbn_v3N_e"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "24c50ae01bbb490b993a03263378e1a5",
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            "5b87f5167d93409a8f6440aab4f5fb9e",
            "ab415d8be8e34d1fb9f2098efcf6a882",
            "a5eaf1f7b7c54bb7a77f0a97dea8117b",
            "d733bad7aed045048851a9e761ee1dc7"
          ]
        },
        "id": "t74gCqmM3N_e",
        "outputId": "d506b147-884f-423c-cb65-21c4f9e81141"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "24c50ae01bbb490b993a03263378e1a5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForMultipleChoice were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = AutoModelForMultipleChoice.from_pretrained(MODEL_CKPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01iHXN5K3N_e"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "INX-mlu53N_e"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    output_dir=MODEL_NAME,\n",
        "    evaluation_strategy=STRATEGY,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    auto_find_batch_size=True,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    report_to=REPORTS_TO,\n",
        "    logging_first_step=True,\n",
        "    logging_steps=125,\n",
        "    save_strategy=STRATEGY,\n",
        "    load_best_model_at_end=True,\n",
        "    hub_private_repo=True,\n",
        "    group_by_length=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdL-ZE4T3N_e"
      },
      "source": [
        "#### Define Data Collator Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "nMBbInh13N_f"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class DataCollatorForMultipleChoice:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad inputs\n",
        "    for the multiple choices received.\n",
        "    \"\"\"\n",
        "\n",
        "    tokenizer: PreTrainedTokenizerBase\n",
        "    padding: Union[bool, str, PaddingStrategy] = True\n",
        "    max_length: Optional[int] = None\n",
        "    pad_to_multiple_of: Optional[int] = None\n",
        "\n",
        "    def __call__(self, features):\n",
        "        label_name = \"label\" if \"label\" in features[0].keys() else \"labels\"\n",
        "        labels = [feature.pop(label_name) for feature in features]\n",
        "        batch_size = len(features)\n",
        "        num_choices = len(features[0][\"input_ids\"])\n",
        "        flattened_features = [[{k: v[i] for k, v in feature.items()}\n",
        "                               for i in range(num_choices)] for feature in features]\n",
        "        flattened_features = sum(flattened_features, [])\n",
        "\n",
        "        batch = self.tokenizer.pad(\n",
        "            flattened_features,\n",
        "            padding=self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "\n",
        "        # Unflatten\n",
        "        batch = {k: v.view(batch_size,\n",
        "                           num_choices,\n",
        "                           -1)\n",
        "                 for k, v in batch.items()}\n",
        "        # Add back labels\n",
        "        batch[\"labels\"] = torch.tensor(labels,\n",
        "                                       dtype=torch.int64)\n",
        "        return batch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUFKaQHF3N_f"
      },
      "source": [
        "#### Create Function to Compute Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "6VfbmL603N_f"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(predictions):\n",
        "    preds, labels = predictions\n",
        "    preds = np.argmax(preds, axis=1)\n",
        "    return {\"accuracy\": (preds == labels).astype(np.float32).mean().item()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lVSxwcG3N_f"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUgVJrxP3N_f",
        "outputId": "161d529b-294a-42dd-900c-e776ecc7afa3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/bert-base-uncased-e_CARE into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/bert-base-uncased-e_CARE into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=encoded_ds[\"train\"],\n",
        "    eval_dataset=encoded_ds[\"eval\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=DataCollatorForMultipleChoice(tokenizer),\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwHTS15c3N_f"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "joUVXegn3N_f",
        "outputId": "a9542a12-e574-43b6-d987-f7062d2d6b6e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7855' max='7855' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7855/7855 20:13, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.563700</td>\n",
              "      <td>0.528169</td>\n",
              "      <td>0.724379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.345000</td>\n",
              "      <td>0.666718</td>\n",
              "      <td>0.732018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.109800</td>\n",
              "      <td>1.311321</td>\n",
              "      <td>0.725652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.021200</td>\n",
              "      <td>1.819388</td>\n",
              "      <td>0.722470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>1.767700</td>\n",
              "      <td>0.721197</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
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      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdBwgxpC3N_f"
      },
      "source": [
        "#### Push to HuggingFace Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "kFvlJ3Fx3N_j",
        "outputId": "0a59b985-57d5-452a-d052-6428b7dd9a21"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "19d5d7acbef64df7a7e6cb982e79a7b2",
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            "text/plain": [
              "Upload file runs/Aug02_21-21-28_3a821f191617/events.out.tfevents.1691011303.3a821f191617.187.0:   0%|         …"
            ]
          },
          "metadata": {},
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-uncased-e_CARE\n",
            "   08972f7..d5da9ae  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-e_CARE\n",
            "   08972f7..d5da9ae  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/bert-base-uncased-e_CARE\n",
            "   d5da9ae..660175a  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-e_CARE\n",
            "   d5da9ae..660175a  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/DunnBC22/bert-base-uncased-e_CARE/commit/d5da9ae9b0a07a0e4f7dca406d1a127b092bd31e'"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RsVYnGf3N_j"
      },
      "source": [
        "#### Save & Log Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCNmXUix3N_j",
        "outputId": "5df1e67d-f548-47ec-bd70-170ecf652412"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =        5.0\n",
            "  total_flos               =  1954171GF\n",
            "  train_loss               =     0.2091\n",
            "  train_runtime            = 0:20:16.26\n",
            "  train_samples_per_second =     51.646\n",
            "  train_steps_per_second   =      6.458\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model()\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AooGJhNx3N_j"
      },
      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "- Since each sample has two options for answers, I was expecting better results.\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYYxusVx3N_j"
      },
      "source": [
        "#### Citation\n",
        "\n",
        "- Model Checkpoint\n",
        "    > @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805%7D, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib%7D, bibsource = {dblp computer science bibliography, https://dblp.org%7D}\n",
        "\n",
        "- Dataset References\n",
        "    > Du, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2022). e-CARE: a New Dataset for Exploring Explainable Causal Reasoning. arXiv preprint arXiv:2205.05849.\n",
        "\n",
        "    > Roemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011."
      ]
    }
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