Multiple Choice/Vitamin C Fact Verification/Vitamin_C_Fact_Verification_Multiple_Choice_Using_BERT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ar6qutLOSotA"
      },
      "source": [
        "## Vitamin C Fact Verification: Multiple Choice\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/tasksource/bigbench/viewer/vitaminc_fact_verification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQYWDtU-SpmC"
      },
      "source": [
        "#### Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lw2YCN1zSpYB",
        "outputId": "bb052200-0941-4699-fb85-250ca4d6744d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "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[31m4.3 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 evaluate\n",
        "%pip install accelerate -U"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDJH97oVTZ36"
      },
      "source": [
        "#### Enter Huggingface Access Token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cttrYkbITZxZ",
        "outputId": "31fe8887-15e4-457c-d526-e52286dafe83"
      },
      "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": "Wz4_BAlKS_0d"
      },
      "source": [
        "#### Mount Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnYiure7TBW4",
        "outputId": "8d1313c6-2c10-466d-f671-3f54aab5e1d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmaQHFvSotD"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8rianVvSotE",
        "outputId": "0e48d073-dede-4e07-d939-6c993f438a09"
      },
      "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": "7DLziAgOSotF"
      },
      "source": [
        "#### Display Library Versions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZu6BjH1SotG",
        "outputId": "b6b55f60-7016-4b94-b0ed-c624034fa2ac"
      },
      "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": "9qYZBfPHSotG"
      },
      "source": [
        "#### Load Dataset & Convert To Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668,
          "referenced_widgets": [
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            "166281fe71104fa0820f1ea1e554351d",
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            "26ac1af58339490697fe6aba59d59f56",
            "a978988ea86546528672a0bfd4931f43",
            "73bbe774c85a443a86141a96a94789fc",
            "6684261cdea74955989df55b7f63ae81",
            "a074ebc7dfcc4c679ae5d52700fe5666",
            "2fb841176e0e4f34863a487a1e400f98",
            "c1d52c6e76f3478789c0be9d0ed0f673",
            "8f3dc2e955074a80a3d353ac3c9a790b",
            "2450616da0dd4cf3a020246fa606a1fe",
            "34f10212f8a842c6a6a580b6e6356101",
            "d1cd6b4390d2420b8b8f375e6ea59cb9",
            "ab9805f0345045dea1f3e3d55bc2fb3a",
            "f2df024920ff44428615a4a25d932bc3",
            "b00b185341474a06987cd2a658999450"
          ]
        },
        "id": "x5van2YHSotG",
        "outputId": "8196f15b-0837-4bd6-964b-778d6717533d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Found cached dataset bigbench (/Users/briandunn/.cache/huggingface/datasets/tasksource___bigbench/vitaminc_fact_verification/1.0.0/c5da5ac497141c7435da10444495b8577405d4ed01e524265b144a7063718c0c)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7ddee9e0e10d4ab6acc976dbde0b3d34",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<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>context</th>\n",
              "      <th>labels</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Based only on the information contained in a b...</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>Neither</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Based only on the information contained in a b...</td>\n",
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              "      <td>Neither</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>2</td>\n",
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              "      <th>3</th>\n",
              "      <td>Based only on the information contained in a b...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Based only on the information contained in a b...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43730</th>\n",
              "      <td>Based only on the information contained in a b...</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>Neither</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43731</th>\n",
              "      <td>Based only on the information contained in a b...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>43732</th>\n",
              "      <td>Based only on the information contained in a b...</td>\n",
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              "      <th>43733</th>\n",
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              "      <td>Neither</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43734</th>\n",
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              "      <td>1</td>\n",
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              "      <td>False</td>\n",
              "      <td>Neither</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43735 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 context  labels     0      1  \\\n",
              "0      Based only on the information contained in a b...       1  True  False   \n",
              "1      Based only on the information contained in a b...       0  True  False   \n",
              "2      Based only on the information contained in a b...       2  True  False   \n",
              "3      Based only on the information contained in a b...       2  True  False   \n",
              "4      Based only on the information contained in a b...       1  True  False   \n",
              "...                                                  ...     ...   ...    ...   \n",
              "43730  Based only on the information contained in a b...       1  True  False   \n",
              "43731  Based only on the information contained in a b...       1  True  False   \n",
              "43732  Based only on the information contained in a b...       0  True  False   \n",
              "43733  Based only on the information contained in a b...       0  True  False   \n",
              "43734  Based only on the information contained in a b...       1  True  False   \n",
              "\n",
              "             2  \n",
              "0      Neither  \n",
              "1      Neither  \n",
              "2      Neither  \n",
              "3      Neither  \n",
              "4      Neither  \n",
              "...        ...  \n",
              "43730  Neither  \n",
              "43731  Neither  \n",
              "43732  Neither  \n",
              "43733  Neither  \n",
              "43734  Neither  \n",
              "\n",
              "[43735 rows x 5 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = load_dataset(\"tasksource/bigbench\", \"vitaminc_fact_verification\")\n",
        "\n",
        "data = data.rename_column(\"inputs\", \"context\")\n",
        "\n",
        "data.set_format(type=\"pandas\")\n",
        "\n",
        "df = data['train'][:]\n",
        "\n",
        "df = df.dropna(how='all')\n",
        "\n",
        "df = df.drop_duplicates(subset='context')\n",
        "\n",
        "df['labels'] = df['targets'].apply(lambda x: x[0])\n",
        "\n",
        "mc_target_values = ['True', 'False', 'Neither']\n",
        "\n",
        "df['0'] = df['multiple_choice_scores'].apply(lambda x: mc_target_values[0])\n",
        "df['1'] = df['multiple_choice_scores'].apply(lambda x: mc_target_values[1])\n",
        "df['2'] = df['multiple_choice_scores'].apply(lambda x: mc_target_values[2])\n",
        "\n",
        "df['labels'] = df['targets'].apply(lambda x: mc_target_values.index(x))\n",
        "\n",
        "df = df.drop(columns=[\"multiple_choice_targets\", \"idx\", \"multiple_choice_scores\", \"targets\"])\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhVjaZdcSotH"
      },
      "source": [
        "#### Visual: Word Length of Training Dataset Inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "NnDXYoBrSotH",
        "outputId": "f9f140b5-f012-4fc0-861d-3eebab39a144"
      },
      "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 inputs (in words) is 317 words.\n"
          ]
        }
      ],
      "source": [
        "df['input_length'] = df['context'].str.split().str.len()\n",
        "\n",
        "n_bin= 318\n",
        "df.hist('input_length', grid=False, figsize=(18,8), range=(0,n_bin), bins=n_bin)\n",
        "plt.title('Histogram: Length of Inputs')\n",
        "plt.xlabel('# of Words in Inputs')\n",
        "plt.ylabel('# of Samples')\n",
        "plt.show()\n",
        "\n",
        "print(f\"The maximum length of the inputs (in words) is {max(df['input_length'])} words.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ip7DxcdvSotI"
      },
      "source": [
        "#### Trim Overly Lengthy Samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smLKUNJVSotI",
        "outputId": "36342faa-ee6a-41a1-9619-1b745c96c181"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 43400 entries, 0 to 43399\n",
            "Data columns (total 5 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   context  43400 non-null  object\n",
            " 1   labels   43400 non-null  int64 \n",
            " 2   0        43400 non-null  object\n",
            " 3   1        43400 non-null  object\n",
            " 4   2        43400 non-null  object\n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 1.7+ MB\n"
          ]
        }
      ],
      "source": [
        "df = df[df['input_length'] < 151]\n",
        "\n",
        "df = df.drop(columns=['input_length']).reset_index(drop=True)\n",
        "\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lkS-LTgWSotI"
      },
      "source": [
        "#### Convert from DataFrame to Dataset & Split into Training/Testing/Evaluation Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ejj1uXCCSotI",
        "outputId": "8adc6719-cef1-4895-a3a8-e6cfce4dfde3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Shape: (34720, 5)\n",
            "Testing Dataset Shape: (4340, 5)\n",
            "Evaluation Dataset Shape: (4340, 5)\n",
            "{'context': Value(dtype='string', id=None), 'labels': Value(dtype='int64', id=None), '0': Value(dtype='string', id=None), '1': Value(dtype='string', id=None), '2': Value(dtype='string', 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": "dGc5wrwlSotJ"
      },
      "source": [
        "#### Display Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRlALb-5SotJ",
        "outputId": "a070b3d5-fc94-46fd-c6cb-3bad04eba5aa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'context': 'Based only on the information contained in a brief quote from Wikipedia, answer whether the related claim is True, False or Neither. Use Neither when the Wikipedia quote does not provide the necessary information to resolve the question.\\n\\n\\nPassage: Indiana Pacers: The Pacers were first established in 1967 as a member of the American Basketball Association -LRB- ABA -RRB- and became a member of the NBA in 1976 as a result of the ABA -- NBA merger .\\nClaim: The Indiana Pacers were established .\\nTrue, False, or Neither?',\n",
              " 'labels': 0,\n",
              " '0': 'True',\n",
              " '1': 'False',\n",
              " '2': 'Neither'}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ds['train'][12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMS40Sd6SotJ"
      },
      "source": [
        "#### Create Function to Display an Example Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JmMHGnAtSotJ"
      },
      "outputs": [],
      "source": [
        "def show_one_example(sample):\n",
        "    print(f\"Context: {sample['context']}\")\n",
        "    print(f\"\\nGround Truth: {mc_target_values[sample['labels']]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzByZMe0SotJ"
      },
      "source": [
        "#### Display an Example Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mrRqZTeSotJ",
        "outputId": "0c982496-f25f-4c2e-eaf3-525c6634980a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Context: Based only on the information contained in a brief quote from Wikipedia, answer whether the related claim is True, False or Neither. Use Neither when the Wikipedia quote does not provide the necessary information to resolve the question.\n",
            "\n",
            "\n",
            "Passage: Indiana Pacers: The Pacers were first established in 1967 as a member of the American Basketball Association -LRB- ABA -RRB- and became a member of the NBA in 1976 as a result of the ABA -- NBA merger .\n",
            "Claim: The Indiana Pacers were established .\n",
            "True, False, or Neither?\n",
            "\n",
            "Ground Truth: True\n"
          ]
        }
      ],
      "source": [
        "show_one_example(ds['train'][12])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATCHZgHqSotJ"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "G4mTL74hSotJ"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT = \"bert-base-uncased\"\n",
        "MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-Vitamin_C_Fact_Verification\"\n",
        "\n",
        "STRATEGY = \"epoch\"\n",
        "LEARNING_RATE = 5e-5\n",
        "\n",
        "BATCH_SIZE = 16\n",
        "NUM_OF_EPOCHS = 2\n",
        "\n",
        "WEIGHT_DECAY = 0.01\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "\n",
        "set_seed = 42"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQdx23hISotK"
      },
      "source": [
        "#### Instantiate Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257,
          "referenced_widgets": [
            "a3f4193ed5aa43a38ffd6fff283417c9",
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            "3ffaa18371fc4402aa27a59c855fd5d5",
            "817b08fba98c48ffbf58a75d9b4251d6",
            "0285362a222147359b61481ad8b1a424",
            "0230f6dfed7a4bc69da2c1d4c37d5fd3",
            "e50a579c41d04dbb9bc58e464ed4a8a4",
            "d327c8835c3e403faebc8132e4addd72",
            "3a853d69b7814ffab4cea29fc24811e4",
            "f739b119ca524d5ab5f99102bbc483ed",
            "7aaebbc85092435abb998a8242f6d422",
            "9c1d6973e3744fa0a0028ce954445b99",
            "7d8c0de464674d8fa600fe0310cf0dcd",
            "cba2e0d590004741802fc554feb2c5bc",
            "2999b35c4e30484d86b262156046639d",
            "94bf50244efb4d69b4b28a43767f555d",
            "72d1490d812b4f18a21ee04cb734d386",
            "25b92f6dc81e412280e6b62010a771e2",
            "afcd7c2693ae43ddaecc38ec11089348"
          ]
        },
        "id": "45R5h0_6SotK",
        "outputId": "a8a655f1-54ba-4425-ce77-63e08143aa5c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3f4193ed5aa43a38ffd6fff283417c9",
              "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": "7d389ffbc94444529450498bec211e25",
              "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": "14d4cbe5a4614b37b392dfdd3d52c61b",
              "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": "3a853d69b7814ffab4cea29fc24811e4",
              "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": "NYH-b8eUSotK"
      },
      "source": [
        "#### Create Preprocessing Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "FsklN8bUSotK"
      },
      "outputs": [],
      "source": [
        "ending_names = [\"0\", \"1\", \"2\"]\n",
        "\n",
        "def preprocess_function(samples):\n",
        "    input_headers = samples[\"context\"]\n",
        "    input = [[f\"{header} {samples[end][i]}\" for end in ending_names]\n",
        "             for i, header in enumerate(input_headers)]\n",
        "\n",
        "    input = sum(input, [])\n",
        "\n",
        "    tokenized_samples = tokenizer(input, truncation=True)\n",
        "\n",
        "    return {k: [v[i:i + len(ending_names)] for i in range(0,\n",
        "                                                          len(v),\n",
        "                                                          len(ending_names))]\n",
        "            for k, v in tokenized_samples.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6IpmN7PSotK"
      },
      "source": [
        "#### Apply Preprocessing Function to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "f4681da194b142c7b6a02cf107ef983c",
            "06364ec0e7b1463eb85640bbf1767951",
            "8511b42232214ff99521dc487da64da1",
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            "f47c4866b262442eb85046665a1b03e6",
            "1228c6877ec34f0c81f3e008b734f436",
            "86d284bf01e4453eb4bbc235e2a6ec33",
            "2bb2be9f37954f1199ba55d6c92c912e",
            "1039eec3ab064d5ba68dd8f0db8d7c30",
            "c57a6eb5ce4749f58f8ea9c17dcb5160",
            "16284ff831a8428cb37de3262cbb6498",
            "02671dbd83904b5e9e6b82defa0fea05",
            "25783d0b3a6047e29910ea707e68e0bc",
            "e667f3ef0fea48a39e5c0eb863cf71a8",
            "4b27594487d0470cb11275e308349ed0",
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            "035815a42c734941928a5273e508eea8",
            "c7357f67573c4aa68d7fe895e573be41",
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            "a4ab6c41707b4905aa3327da464b5301",
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            "066b82d741e04ba1a59bd320f716b84c",
            "7324c099b566414d9be90f7388921ca2"
          ]
        },
        "id": "4twbvW19SotK",
        "outputId": "26013b1b-122c-4f17-ebd4-df596a1901cd"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f4681da194b142c7b6a02cf107ef983c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/34720 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "02671dbd83904b5e9e6b82defa0fea05",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/4340 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e9ab3a119e904b7fa3579d095d8c9b77",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/4340 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "encoded_ds = ds.map(preprocess_function,\n",
        "                    batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfZkINDcSotK",
        "outputId": "c9ce2b72-7cee-4acc-bb19-ffb9576de44d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'context': Value(dtype='string', id=None),\n",
              " 'labels': Value(dtype='int64', id=None),\n",
              " '0': Value(dtype='string', id=None),\n",
              " '1': Value(dtype='string', id=None),\n",
              " '2': Value(dtype='string', 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": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "encoded_ds['train'].features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaD82UR5SotK"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132,
          "referenced_widgets": [
            "2b55f5f023634384bf99703871f09766",
            "450ed704945349d39242d1e44b6bd6a1",
            "a2001beca1994833b6e1f913a4902b30",
            "a0a9c23e167d47feac84d0656f951fec",
            "77d05ebed17c47b09f27974ff67e7c2a",
            "340f51de087245ddb045bca242c87c6f",
            "1f08f083877b4856acd2588a9b6e1ffe",
            "ccb8cc12567b4d6fbfc5ab9296a99c1f",
            "dd1e973d41234971a4035dbcca98c8d1",
            "9bf6f83608fa419c83dea43ac66aa6d5",
            "f88cd8f202df44a0b2d3419573d2f5aa"
          ]
        },
        "id": "Gkrqc1HNSotK",
        "outputId": "a89ef210-7629-469f-91b7-7656e8710c40"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2b55f5f023634384bf99703871f09766",
              "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": "RpR2gW9QSotK"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "PoNgO4wmSotL"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    output_dir=MODEL_NAME,\n",
        "    evaluation_strategy=STRATEGY,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=BATCH_SIZE,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    report_to=REPORTS_TO,\n",
        "    logging_first_step=True,\n",
        "    hub_private_repo=True,\n",
        "    group_by_length=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXueg3LzSotL"
      },
      "source": [
        "#### Define Data Collator Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "PIjxl4qWSotL"
      },
      "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)]\n",
        "                              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": "xrmUlGBGSotL"
      },
      "source": [
        "#### Create Function to Compute Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "sEfV_DHVSotL"
      },
      "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": "rNWsapT1SotL"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVyRqkrZSotL",
        "outputId": "b2aefb2a-c389-4167-f70b-f7e49f861b28"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification 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": "IXvfiB22SotL"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "Poa4Ww5tSotR",
        "outputId": "9a30766b-4253-4d65-c28e-f05470a75e7d"
      },
      "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='4340' max='4340' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [4340/4340 1:38:34, Epoch 2/2]\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>0.698500</td>\n",
              "      <td>0.689356</td>\n",
              "      <td>0.686406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.555500</td>\n",
              "      <td>0.632921</td>\n",
              "      <td>0.723963</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrcQQAoBSotR"
      },
      "source": [
        "#### Push to HuggingFace Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429,
          "referenced_widgets": [
            "48deac2bb83142ec8dab40b058001963",
            "f005600e111b452ebc127cfe56325638",
            "608051b7dd664b308e9ebd4a3204d396",
            "ef1f51ef3fae407983d6641e83af5dc7",
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              "model_id": "48deac2bb83142ec8dab40b058001963",
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              "Upload file pytorch_model.bin:   0%|          | 1.00/418M [00:00<?, ?B/s]"
            ]
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              "Upload file runs/Aug01_18-18-45_88d15003d599/events.out.tfevents.1690913970.88d15003d599.608.0:   0%|         …"
            ]
          },
          "metadata": {},
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        {
          "name": "stderr",
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          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification\n",
            "   4e37efd..63dde3a  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification\n",
            "   4e37efd..63dde3a  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification\n",
            "   63dde3a..5333b3f  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification\n",
            "   63dde3a..5333b3f  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
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              "'https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification/commit/63dde3a41dad5f77d280e52d41987b54d6a7a3d9'"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tehxwJf-VBg3"
      },
      "source": [
        "#### Save Model & Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yIs-IEJUVBU0",
        "outputId": "23681a41-fd63-4573-b084-3fff0576528a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification\n",
            "   5333b3f..9253a0e  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-Vitamin_C_Fact_Verification\n",
            "   5333b3f..9253a0e  main -> main\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
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          "text": [
            "***** train metrics *****\n",
            "  epoch                    =        2.0\n",
            "  total_flos               = 15225885GF\n",
            "  train_loss               =     0.6937\n",
            "  train_runtime            = 1:38:39.06\n",
            "  train_samples_per_second =     11.732\n",
            "  train_steps_per_second   =      0.733\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": "TlnJB-bNSotR"
      },
      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "- I am glad to see that the metric results for this project are better than the results of past Multiple Choice models that I have trained. I think I found what was causing the issue.\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4JxwNqwSotR"
      },
      "source": [
        "#### Citation\n",
        "\n",
        "- Model Checkpoint\n",
        "\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\n",
        "\n",
        "    > @article{srivastava2022beyond, title={Beyond the imitation game: Quantifying and extrapolating the capabilities of language models}, author={Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R and Santoro, Adam and Gupta, Aditya and Garriga-Alonso, Adri{\\`a} and others}, journal={arXiv preprint arXiv:2206.04615}, year={2022}}"
      ]
    }
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