Multiple Choice/Discourse Marker QA/Discourse_Marker_QA_Multiple_Choice_Using_BERT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6PRVSEAr5JQ6"
      },
      "source": [
        "## Discourse Marker QA: Multiple Choice\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/sileod/discourse_marker_qa"
      ]
    },
    {
      "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": "8be022b6-00f0-45d7-e76e-866194f06d9a"
      },
      "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[31m5.1 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": "c988f7ba-13d5-4391-85b6-7980542a1c43"
      },
      "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": "yVIhUxHb5JQ8"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQZ2l0ID5JQ8",
        "outputId": "70209f37-2dfd-4f9c-b543-1ac4d6641157"
      },
      "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": "ENYbBWDr5JQ9"
      },
      "source": [
        "#### Display Library Versions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unwyUe9r5JQ9",
        "outputId": "23cac780-4324-4882-fb2f-52a809520f8b"
      },
      "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": "y4jzBnw-5JQ9"
      },
      "source": [
        "#### Load Dataset & Convert To Pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "2f81eafe5a044102907cfe4efc491eea",
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            "8141fc87db57415c870ce7ae4733f77e",
            "fe6c3535b83d46f9afaecdca719d96d0",
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            "7131383adfcc46e68c347d20d8c66265",
            "2f4b7112c3854e7db428773aa29d6181",
            "36e2a0703f924eb0a9ad87d1420c05ea",
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            "13320d07ea7840f2bf34d5530c5c7650",
            "2c22f7f949e241cab1e80ecb7de1deb6",
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            "b26e402808174722a0edab5739fefbea",
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            "849c9b50e1b048c8ae20eaa654189a56",
            "4b742509a3dc40adb3ca62dca001f1e1",
            "636802d699a34b228bde60bf49490322",
            "f7332816d4b043aa899ebae4c041dc18",
            "515c41a1d02940e29d938770872c7524",
            "432f247e6a6744939cae9b14ca42466f",
            "fac78ea963a648daa115bd998ae9a49a",
            "3a3e77f3761a4a55b5f9b3d71ea469e7",
            "f3fd6eff8efc4ea48686a61179da451a",
            "6d5cae06e9b641ec86c7d8c781fc3fd0",
            "4efa35f1688f420d99664eddae89b8ef",
            "e02eab0a42d94065b44e22f41271f67f",
            "89eff3c47d1544eeb5623c181eeb47ab"
          ]
        },
        "id": "oba_1qaz5JQ9",
        "outputId": "7272aedd-0152-4daf-8c9d-205f80979c64"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f81eafe5a044102907cfe4efc491eea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.56k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fe6c3535b83d46f9afaecdca719d96d0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52405be954d4458d83c0a18244696d11",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.10M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "636802d699a34b228bde60bf49490322",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "      <th>0</th>\n",
              "      <td>Here, however, a charging circuit is provides ...</td>\n",
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              "      <td>, it's about these orange shorts :.</td>\n",
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              "      <td>Lately, neither the shorts, nor I, made the fi...</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The panel has considered the allegation by the...</td>\n",
              "      <td>In particular, the Panel has considered the Co...</td>\n",
              "      <td>Again, the Panel has considered the Complainan...</td>\n",
              "      <td>Increasingly, the Panel has considered the Com...</td>\n",
              "      <td>Amazingly, the Panel has considered the Compla...</td>\n",
              "      <td>Once, the Panel has considered the Complainant...</td>\n",
              "      <td>Currently, the Panel has considered the Compla...</td>\n",
              "      <td>Realistically, the Panel has considered the Co...</td>\n",
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              "      <td>Suddenly, the Panel has considered the Complai...</td>\n",
              "      <td>Historically, the Panel has considered the Com...</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>Smishing is sometimes referred to as``vishing ...</td>\n",
              "      <td>Admittedly, a criminal begins by setting up an...</td>\n",
              "      <td>Frequently, a criminal begins by setting up an...</td>\n",
              "      <td>Regardless, a criminal begins by setting up an...</td>\n",
              "      <td>Perhaps, a criminal begins by setting up an au...</td>\n",
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              "      <td>Arguably, a criminal begins by setting up an a...</td>\n",
              "      <td>Typically, a criminal begins by setting up an ...</td>\n",
              "      <td>Curiously, a criminal begins by setting up an ...</td>\n",
              "      <td>Once, a criminal begins by setting up an autom...</td>\n",
              "      <td>Traditionally, a criminal begins by setting up...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Whitley and Nathan decide to forget about what...</td>\n",
              "      <td>Probably, whitley can't seem to forget about t...</td>\n",
              "      <td>Eventually, whitley can't seem to forget about...</td>\n",
              "      <td>Meaning, whitley can't seem to forget about th...</td>\n",
              "      <td>Unfortunately, whitley can't seem to forget ab...</td>\n",
              "      <td>Accordingly whitley can't seem to forget about...</td>\n",
              "      <td>Only, whitley can't seem to forget about that ...</td>\n",
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              "      <td>Increasingly, whitley can't seem to forget abo...</td>\n",
              "      <td>5</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>852</th>\n",
              "      <td>My epic journey across the countryside was hel...</td>\n",
              "      <td>Meantime, when I found a riftstone, I dismisse...</td>\n",
              "      <td>On the other hand when I found a riftstone, I ...</td>\n",
              "      <td>Thirdly, when I found a riftstone, I dismissed...</td>\n",
              "      <td>Likewise, when I found a riftstone, I dismisse...</td>\n",
              "      <td>Sadly, when I found a riftstone, I dismissed h...</td>\n",
              "      <td>Later, when I found a riftstone, I dismissed h...</td>\n",
              "      <td>Specifically, when I found a riftstone, I dism...</td>\n",
              "      <td>Undoubtedly, when I found a riftstone, I dismi...</td>\n",
              "      <td>Surprisingly, when I found a riftstone, I dism...</td>\n",
              "      <td>Frequently, when I found a riftstone, I dismis...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>853</th>\n",
              "      <td>Alfred Molina, who plays Viggo, also starred w...</td>\n",
              "      <td>Overall, the film premiered the same year as H...</td>\n",
              "      <td>Plus, the film premiered the same year as How ...</td>\n",
              "      <td>Sadly, the film premiered the same year as How...</td>\n",
              "      <td>Secondly, the film premiered the same year as ...</td>\n",
              "      <td>Coincidentally, the film premiered the same ye...</td>\n",
              "      <td>Notably, the film premiered the same year as H...</td>\n",
              "      <td>Presumably, the film premiered the same year a...</td>\n",
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              "      <td>Lastly, the film premiered the same year as Ho...</td>\n",
              "      <td>Furthermore the film premiered the same year a...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>854</th>\n",
              "      <td>Obviously I'm not entering the giveaway here, ...</td>\n",
              "      <td>Seriously, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Typically, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Particularly, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Fortunately, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Sometimes, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Ideally, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Usually, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Locally, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Although, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>Elsewhere, that Dinner Part 2 picture - I die.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>855</th>\n",
              "      <td>The loss of Rooney is a massive one, when adde...</td>\n",
              "      <td>Again, chins up, grit your teeth, and let's al...</td>\n",
              "      <td>Subsequently, chins up, grit your teeth, and l...</td>\n",
              "      <td>Together, chins up, grit your teeth, and let's...</td>\n",
              "      <td>Traditionally, chins up, grit your teeth, and ...</td>\n",
              "      <td>Clearly, chins up, grit your teeth, and let's ...</td>\n",
              "      <td>Alternately, chins up, grit your teeth, and le...</td>\n",
              "      <td>So, chins up, grit your teeth, and let's all g...</td>\n",
              "      <td>Curiously, chins up, grit your teeth, and let'...</td>\n",
              "      <td>Actually, chins up, grit your teeth, and let's...</td>\n",
              "      <td>Fortunately, chins up, grit your teeth, and le...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>856</th>\n",
              "      <td>On the other hand, the ConEd Astoria West faci...</td>\n",
              "      <td>Normally, as reported by the news media, the e...</td>\n",
              "      <td>Overall, as reported by the news media, the ev...</td>\n",
              "      <td>Remarkably, as reported by the news media, the...</td>\n",
              "      <td>Moreover as reported by the news media, the ev...</td>\n",
              "      <td>Nonetheless as reported by the news media, the...</td>\n",
              "      <td>Fortunately, as reported by the news media, th...</td>\n",
              "      <td>By contrast, as reported by the news media, th...</td>\n",
              "      <td>Lately, as reported by the news media, the eve...</td>\n",
              "      <td>Surprisingly, as reported by the news media, t...</td>\n",
              "      <td>Sometimes, as reported by the news media, the ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>857 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9ce2003-a0b0-45f3-b835-2908317582f6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3e865f3e-108e-43bd-8780-640ee19ece35\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e865f3e-108e-43bd-8780-640ee19ece35')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-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-3e865f3e-108e-43bd-8780-640ee19ece35 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-d9ce2003-a0b0-45f3-b835-2908317582f6 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-d9ce2003-a0b0-45f3-b835-2908317582f6');\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  \\\n",
              "0    Here, however, a charging circuit is provides ...   \n",
              "1                  , it's about these orange shorts :.   \n",
              "2    The panel has considered the allegation by the...   \n",
              "3    Smishing is sometimes referred to as``vishing ...   \n",
              "4    Whitley and Nathan decide to forget about what...   \n",
              "..                                                 ...   \n",
              "852  My epic journey across the countryside was hel...   \n",
              "853  Alfred Molina, who plays Viggo, also starred w...   \n",
              "854  Obviously I'm not entering the giveaway here, ...   \n",
              "855  The loss of Rooney is a massive one, when adde...   \n",
              "856  On the other hand, the ConEd Astoria West faci...   \n",
              "\n",
              "                                                     0  \\\n",
              "0    Preferably, an amplifier within a current sens...   \n",
              "1    Accordingly neither the shorts, nor I, made th...   \n",
              "2    In particular, the Panel has considered the Co...   \n",
              "3    Admittedly, a criminal begins by setting up an...   \n",
              "4    Probably, whitley can't seem to forget about t...   \n",
              "..                                                 ...   \n",
              "852  Meantime, when I found a riftstone, I dismisse...   \n",
              "853  Overall, the film premiered the same year as H...   \n",
              "854     Seriously, that Dinner Part 2 picture - I die.   \n",
              "855  Again, chins up, grit your teeth, and let's al...   \n",
              "856  Normally, as reported by the news media, the e...   \n",
              "\n",
              "                                                     1  \\\n",
              "0    Namely, an amplifier within a current sensor i...   \n",
              "1    This, neither the shorts, nor I, made the fina...   \n",
              "2    Again, the Panel has considered the Complainan...   \n",
              "3    Frequently, a criminal begins by setting up an...   \n",
              "4    Eventually, whitley can't seem to forget about...   \n",
              "..                                                 ...   \n",
              "852  On the other hand when I found a riftstone, I ...   \n",
              "853  Plus, the film premiered the same year as How ...   \n",
              "854     Typically, that Dinner Part 2 picture - I die.   \n",
              "855  Subsequently, chins up, grit your teeth, and l...   \n",
              "856  Overall, as reported by the news media, the ev...   \n",
              "\n",
              "                                                     2  \\\n",
              "0    Absolutely, an amplifier within a current sens...   \n",
              "1    Because of this neither the shorts, nor I, mad...   \n",
              "2    Increasingly, the Panel has considered the Com...   \n",
              "3    Regardless, a criminal begins by setting up an...   \n",
              "4    Meaning, whitley can't seem to forget about th...   \n",
              "..                                                 ...   \n",
              "852  Thirdly, when I found a riftstone, I dismissed...   \n",
              "853  Sadly, the film premiered the same year as How...   \n",
              "854  Particularly, that Dinner Part 2 picture - I die.   \n",
              "855  Together, chins up, grit your teeth, and let's...   \n",
              "856  Remarkably, as reported by the news media, the...   \n",
              "\n",
              "                                                     3  \\\n",
              "0    In the end, an amplifier within a current sens...   \n",
              "1    Because of that neither the shorts, nor I, mad...   \n",
              "2    Amazingly, the Panel has considered the Compla...   \n",
              "3    Perhaps, a criminal begins by setting up an au...   \n",
              "4    Unfortunately, whitley can't seem to forget ab...   \n",
              "..                                                 ...   \n",
              "852  Likewise, when I found a riftstone, I dismisse...   \n",
              "853  Secondly, the film premiered the same year as ...   \n",
              "854   Fortunately, that Dinner Part 2 picture - I die.   \n",
              "855  Traditionally, chins up, grit your teeth, and ...   \n",
              "856  Moreover as reported by the news media, the ev...   \n",
              "\n",
              "                                                     4  \\\n",
              "0    Secondly, an amplifier within a current sensor...   \n",
              "1    Interestingly, neither the shorts, nor I, made...   \n",
              "2    Once, the Panel has considered the Complainant...   \n",
              "3    Realistically, a criminal begins by setting up...   \n",
              "4    Accordingly whitley can't seem to forget about...   \n",
              "..                                                 ...   \n",
              "852  Sadly, when I found a riftstone, I dismissed h...   \n",
              "853  Coincidentally, the film premiered the same ye...   \n",
              "854     Sometimes, that Dinner Part 2 picture - I die.   \n",
              "855  Clearly, chins up, grit your teeth, and let's ...   \n",
              "856  Nonetheless as reported by the news media, the...   \n",
              "\n",
              "                                                     5  \\\n",
              "0    Normally, an amplifier within a current sensor...   \n",
              "1    Unsurprisingly, neither the shorts, nor I, mad...   \n",
              "2    Currently, the Panel has considered the Compla...   \n",
              "3    Arguably, a criminal begins by setting up an a...   \n",
              "4    Only, whitley can't seem to forget about that ...   \n",
              "..                                                 ...   \n",
              "852  Later, when I found a riftstone, I dismissed h...   \n",
              "853  Notably, the film premiered the same year as H...   \n",
              "854       Ideally, that Dinner Part 2 picture - I die.   \n",
              "855  Alternately, chins up, grit your teeth, and le...   \n",
              "856  Fortunately, as reported by the news media, th...   \n",
              "\n",
              "                                                     6  \\\n",
              "0    Frequently, an amplifier within a current sens...   \n",
              "1    Preferably, neither the shorts, nor I, made th...   \n",
              "2    Realistically, the Panel has considered the Co...   \n",
              "3    Typically, a criminal begins by setting up an ...   \n",
              "4    Because of this whitley can't seem to forget a...   \n",
              "..                                                 ...   \n",
              "852  Specifically, when I found a riftstone, I dism...   \n",
              "853  Presumably, the film premiered the same year a...   \n",
              "854       Usually, that Dinner Part 2 picture - I die.   \n",
              "855  So, chins up, grit your teeth, and let's all g...   \n",
              "856  By contrast, as reported by the news media, th...   \n",
              "\n",
              "                                                     7  \\\n",
              "0    Technically, an amplifier within a current sen...   \n",
              "1    Sadly, neither the shorts, nor I, made the fin...   \n",
              "2    Now, the Panel has considered the Complainant´...   \n",
              "3    Curiously, a criminal begins by setting up an ...   \n",
              "4    Particularly, whitley can't seem to forget abo...   \n",
              "..                                                 ...   \n",
              "852  Undoubtedly, when I found a riftstone, I dismi...   \n",
              "853  Accordingly the film premiered the same year a...   \n",
              "854       Locally, that Dinner Part 2 picture - I die.   \n",
              "855  Curiously, chins up, grit your teeth, and let'...   \n",
              "856  Lately, as reported by the news media, the eve...   \n",
              "\n",
              "                                                     8  \\\n",
              "0    Eventually, an amplifier within a current sens...   \n",
              "1    Afterward neither the shorts, nor I, made the ...   \n",
              "2    Suddenly, the Panel has considered the Complai...   \n",
              "3    Once, a criminal begins by setting up an autom...   \n",
              "4    Furthermore whitley can't seem to forget about...   \n",
              "..                                                 ...   \n",
              "852  Surprisingly, when I found a riftstone, I dism...   \n",
              "853  Lastly, the film premiered the same year as Ho...   \n",
              "854      Although, that Dinner Part 2 picture - I die.   \n",
              "855  Actually, chins up, grit your teeth, and let's...   \n",
              "856  Surprisingly, as reported by the news media, t...   \n",
              "\n",
              "                                                     9  labels  \n",
              "0    Meaning, an amplifier within a current sensor ...       1  \n",
              "1    Lately, neither the shorts, nor I, made the fi...       7  \n",
              "2    Historically, the Panel has considered the Com...       0  \n",
              "3    Traditionally, a criminal begins by setting up...       6  \n",
              "4    Increasingly, whitley can't seem to forget abo...       5  \n",
              "..                                                 ...     ...  \n",
              "852  Frequently, when I found a riftstone, I dismis...       5  \n",
              "853  Furthermore the film premiered the same year a...       4  \n",
              "854     Elsewhere, that Dinner Part 2 picture - I die.       0  \n",
              "855  Fortunately, chins up, grit your teeth, and le...       6  \n",
              "856  Sometimes, as reported by the news media, the ...       4  \n",
              "\n",
              "[857 rows x 12 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = load_dataset(\"sileod/discourse_marker_qa\")\n",
        "\n",
        "data = data.rename_columns({\n",
        "    \"label\": \"labels\",\n",
        "    \"answer_0\": \"0\",\n",
        "    \"answer_1\": \"1\",\n",
        "    \"answer_2\": \"2\",\n",
        "    \"answer_3\": \"3\",\n",
        "    \"answer_4\": \"4\",\n",
        "    \"answer_5\": \"5\",\n",
        "    \"answer_6\": \"6\",\n",
        "    \"answer_7\": \"7\",\n",
        "    \"answer_8\": \"8\",\n",
        "    \"answer_9\": \"9\",\n",
        "    })\n",
        "\n",
        "data.set_format(type=\"pandas\")\n",
        "\n",
        "df = data['test'][:]\n",
        "\n",
        "df = df.dropna(how='all')\n",
        "\n",
        "df = df.drop_duplicates()\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJ4-vCj55JQ-"
      },
      "source": [
        "#### Visual: Word Length of Training Dataset Inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "4RJYqd1M5JQ-",
        "outputId": "fca27d8a-50f8-4bad-85c3-8d1e99194b1c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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8//vfT2VlZcaMGfORjv9RdOvWLYccckjOOOOMrF27NlOnTk3r1q1zwQUXVO3z5S9/OVdccUWGDh2aUaNGZenSpbn66quz7777VvuCzEaNGqVnz575+c9/nr333jutWrVKr1698o9//COHHXZYTjrppPTs2TN169bNHXfckSVLluSUU04px68NAFBYojYAADVy9tln51e/+lXuu+++rF27Np07d853vvOdnH/++VX7nHjiiTnrrLNy66235mc/+1lKpVJOOeWU1KlTJ7/61a9y8cUX5+c//3muu+66VFZW5nvf+17OPffcau9zww03pF27drnllltyxx13ZMiQIfn5z3+e7t27p2HDhls1680335yzzjor06ZNS6lUyhFHHJG777477du3r9XP5MO45557cs8992y2vbKyMr169cr06dNz5ZVX5oYbbsgdd9yRxo0bZ88998zXv/717L333jV+v759++buu+/Oeeedl4suuigdO3bMpEmT8uyzz252h/kVV1yR008/Pd/61rfy9ttvZ/jw4R85aifJiBEj0rhx40yZMiXf+MY30qRJk3z2s5/NJZdckpYtW37k439Yp512WurUqZOpU6dm6dKlOfDAA/Nf//Vf2WOPPar22WeffXLDDTfk4osvztixY9OzZ8/ceOONufnmmzN79uxqx/vxj3+cs846K2PGjMm6desyfvz4nHXWWfn85z+fmTNn5sYbb0zdunXTo0eP3HbbbRk2bNjH/BsDABRbRWl7+jYXAAB4H/Pnz8+nPvWp/OxnP8sXv/jFco+zQzjhhBPyzDPPFGKt8dq2YMGCdOnSJd/73vdy3nnnlXscAAC2kjW1AQDYLr399tubbZs6dWrq1KmTAQMGlGGi4nv3Z/riiy/mrrvuyqBBg8ozEAAAfAiWHwEAYLt06aWXZu7cuRk8eHDq1q2bu+++O3fffXdOP/30dOzYsdzjFdKee+6ZESNGZM8998z//u//5qqrrkr9+vWrrR0NAADbO1EbAIDt0r/+67/m/vvvz7e//e2sWrUqnTp1yoQJE/LNb36z3KMV1pFHHplbbrklixcvToMGDdK/f/9897vfzV577VXu0QAAYKtZUxsAAAAAgMKwpjYAAAAAAIUhagMAAAAAUBg7/JraGzduzKuvvppmzZqloqKi3OMAAAAAALAFpVIpK1euTPv27VOnznvfj73DR+1XX301HTt2LPcYAAAAAABshUWLFqVDhw7v+fwOH7WbNWuW5J8fRPPmzcs8DQAAAAAAW7JixYp07Nixqum+lx0+am9acqR58+aiNgAAAADAdu6DlpH2RZEAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGHULfcAAACUT+W4GeUeYTMLphxT7hEAAIDtmDu1AQAAAAAoDFEbAAAAAIDCELUBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCELUBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwqhb7gEAAIAdR+W4GeUeYTMLphxT7hEAAKhF7tQGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKo265BwAAgHeqHDej3CNsZsGUY8o9AgAA8P9xpzYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYdcs9AAAAANuHynEzyj3CZhZMOabcIwAA2xl3agMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGNtN1J4yZUoqKipyzjnnVG1bs2ZNRo8endatW6dp06YZNmxYlixZUr4hAQAAAAAoq+0iaj/xxBO55ppr0rt372rbx4wZk1//+te5/fbbM2fOnLz66qs58cQTyzQlAAAAAADlVvaovWrVqnzxi1/Mj370o+y6665V25cvX55rr702V1xxRQ499ND07ds31113XX73u9/l0UcfLePEAAAAAACUS9mj9ujRo3PMMcdkyJAh1bbPnTs369evr7a9R48e6dSpUx555JH3PN7atWuzYsWKaj8AAAAAAOwY6pbzzW+99dbMmzcvTzzxxGbPLV68OPXr10/Lli2rbW/btm0WL178nsecPHlyJk6cWNujAgDAdqVy3IxyjwAAAGVRtju1Fy1alK9//eu56aab0rBhw1o77oUXXpjly5dX/SxatKjWjg0AAAAAQHmVLWrPnTs3S5cuzb/8y7+kbt26qVu3bubMmZMf/OAHqVu3btq2bZt169Zl2bJl1V63ZMmStGvX7j2P26BBgzRv3rzaDwAAAAAAO4ayLT9y2GGH5Q9/+EO1bSNHjkyPHj3yjW98Ix07dky9evUyc+bMDBs2LEny/PPPZ+HChenfv385RgYAAAAAoMzKFrWbNWuWXr16VdvWpEmTtG7dumr7qFGjMnbs2LRq1SrNmzfPWWedlf79+6dfv37lGBkAAAAAgDIr6xdFfpArr7wyderUybBhw7J27doMHTo0P/zhD8s9FgAAAAAAZbJdRe3Zs2dXe9ywYcNMmzYt06ZNK89AAAAAAABsV8r2RZEAAAAAAFBTojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFUbfcAwAAAOxsKsfNKPcIAACF5U5tAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKo265BwAAAAA+mspxM8o9whYtmHJMuUcAYAfkTm0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAw6pZ7AAAAgG2pctyMco/AR7A9/v0WTDmm3CMAwE7NndoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBh1C33AABAsVWOm1HuETazYMox5R4BAACAbcSd2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYdQt9wAAADuDynEzyj0CH4G/HwAAbD/cqQ0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABRG3XIPAABQ2yrHzSj3CAAAW217/d8uC6YcU+4RALbIndoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFEZZo/ZVV12V3r17p3nz5mnevHn69++fu+++u+r5NWvWZPTo0WndunWaNm2aYcOGZcmSJWWcGAAAAACAcipr1O7QoUOmTJmSuXPn5sknn8yhhx6a448/Ps8880ySZMyYMfn1r3+d22+/PXPmzMmrr76aE088sZwjAwAAAABQRnXL+ebHHntstcf/8R//kauuuiqPPvpoOnTokGuvvTY333xzDj300CTJddddl3322SePPvpo+vXrV46RAQAAAAAoo+1mTe0NGzbk1ltvzerVq9O/f//MnTs369evz5AhQ6r26dGjRzp16pRHHnnkPY+zdu3arFixotoPAAAAAAA7hrLeqZ0kf/jDH9K/f/+sWbMmTZs2zR133JGePXtm/vz5qV+/flq2bFlt/7Zt22bx4sXvebzJkydn4sSJ23hqAAAAoIgqx80o9wgAfERlv1O7e/fumT9/fh577LGcccYZGT58eP70pz996ONdeOGFWb58edXPokWLanFaAAAAAADKqex3atevXz/dunVLkvTt2zdPPPFEvv/97+fkk0/OunXrsmzZsmp3ay9ZsiTt2rV7z+M1aNAgDRo02NZjAwAAAABQBmW/U/vdNm7cmLVr16Zv376pV69eZs6cWfXc888/n4ULF6Z///5lnBAAAAAAgHIp653aF154YY466qh06tQpK1euzM0335zZs2fn3nvvTYsWLTJq1KiMHTs2rVq1SvPmzXPWWWelf//+6devXznHBgAAAACgTMoatZcuXZrTTjstr732Wlq0aJHevXvn3nvvzeGHH54kufLKK1OnTp0MGzYsa9euzdChQ/PDH/6wnCMDAAAAAFBGZY3a11577fs+37Bhw0ybNi3Tpk37mCYCAAAAAGB7tt2tqQ0AAAAAAO9F1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAw6pZ7AAAAACiSynEzyj0CAOzU3KkNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAURt1yDwAAAAAARVU5bka5R9iiBVOOKfcIsM24UxsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCELUBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCqHHUXrRoUf76179WPX788cdzzjnn5L//+79rdTAAAAAAAHi3ujV9wRe+8IWcfvrpOfXUU7N48eIcfvjh2XfffXPTTTdl8eLFufjii7fFnADwsaocN6PcI2xmwZRjyj0CAAAAlF2N79T+4x//mAMPPDBJctttt6VXr1753e9+l5tuuinXX399bc8HAAAAAABVahy1169fnwYNGiRJfvOb3+S4445LkvTo0SOvvfZa7U4HAAAAAADvUOOove++++bqq6/OQw89lPvvvz9HHnlkkuTVV19N69ata31AAAAAAADYpMZR+5JLLsk111yTQYMG5fOf/3z222+/JMmvfvWrqmVJAAAAAABgW6jxF0UOGjQof//737NixYrsuuuuVdtPP/30NG7cuFaHAwAAAACAd6rxndpJUiqVMnfu3FxzzTVZuXJlkqR+/fqiNgAAAAAA21SN79T+3//93xx55JFZuHBh1q5dm8MPPzzNmjXLJZdckrVr1+bqq6/eFnMCAAAAAEDN79T++te/nv333z9vvvlmGjVqVLX9s5/9bGbOnFmrwwEAAAAAwDvV+E7thx56KL/73e9Sv379atsrKyvzyiuv1NpgAAAAAADwbjW+U3vjxo3ZsGHDZtv/+te/plmzZrUyFAAAAAAAbEmNo/YRRxyRqVOnVj2uqKjIqlWrMn78+Bx99NG1ORsAAAAAAFRT4+VHLr/88gwdOjQ9e/bMmjVr8oUvfCEvvvhidtttt9xyyy3bYkYAAAAAAEjyIaJ2hw4d8tRTT+XWW2/N008/nVWrVmXUqFH54he/WO2LIwEAAAAAoLbVOGonSd26dfOlL32ptmcBAAAAAID3tVVR+1e/+tVWH/C444770MMAAAAAAMD72aqofcIJJ2zVwSoqKrJhw4aPMg8AAAAAALynrYraGzdu3NZzAAAAAADAB6pT7gEAAAAAAGBrfaioPXPmzHzmM59J165d07Vr13zmM5/Jb37zm9qeDQAAAAAAqqlx1P7hD3+YI488Ms2aNcvXv/71fP3rX0/z5s1z9NFHZ9q0adtiRgAAAAAASLKVa2q/03e/+91ceeWVOfPMM6u2nX322Tn44IPz3e9+N6NHj67VAQEAAAAAYJMa36m9bNmyHHnkkZttP+KII7J8+fJaGQoAAAAAALakxlH7uOOOyx133LHZ9l/+8pf5zGc+UytDAQAAAADAltR4+ZGePXvmP/7jPzJ79uz0798/SfLoo4/m4Ycfzrnnnpsf/OAHVfueffbZtTcpAAAAAAA7vRpH7WuvvTa77rpr/vSnP+VPf/pT1faWLVvm2muvrXpcUVEhagMAAAAAUKtqHLVffvnlbTEHAAAAANuRynEzyj3CZhZMOabcIwDbgRqvqQ0AAAAAAOVS4zu1S6VS/ud//iezZs3K0qVLs3HjxmrP/+IXv6i14QAAAAAA4J1qHLXPOeecXHPNNRk8eHDatm2bioqKbTEXAAAAAABspsZR+8Ybb8wvfvGLHH300dtiHgAAAAAAeE81XlO7RYsW2XPPPbfFLAAAAAAA8L5qHLUnTJiQiRMn5u23394W8wAAAAAAwHuq8fIjJ510Um655Za0adMmlZWVqVevXrXn582bV2vDAQAAAADAO9U4ag8fPjxz587Nl770JV8UCQAAAADAx6rGUXvGjBm59957c8ghh2yLeQAAAAAA4D3VeE3tjh07pnnz5ttiFgAAAAAAeF81jtqXX355LrjggixYsGAbjAMAAAAAAO+txsuPfOlLX8pbb72Vrl27pnHjxpt9UeQbb7xRa8MBAAAAAMA71ThqT506dRuMAQAAAOxoKsfNKPcIAOyAahy1hw8fvi3mAAAAAACAD1TjqP1Oa9asybp166pt8yWSAAAAAABsKzX+osjVq1fnzDPPTJs2bdKkSZPsuuuu1X4AAAAAAGBbqXHUvuCCC/LAAw/kqquuSoMGDfLjH/84EydOTPv27XPDDTdsixkBAAAAACDJh1h+5Ne//nVuuOGGDBo0KCNHjsynP/3pdOvWLZ07d85NN92UL37xi9tiTgAAAAAAqPmd2m+88Ub23HPPJP9cP/uNN95IkhxyyCF58MEHa3c6AAAAAAB4hxpH7T333DMvv/xykqRHjx657bbbkvzzDu6WLVvW6nAAAAAAAPBONY7aI0eOzFNPPZUkGTduXKZNm5aGDRtmzJgxOf/882t9QAAAAAAA2KTGa2qPGTOm6t9DhgzJs88+m3nz5qVbt27p3bt3rQ4HAAAAAADvVOOo/W6VlZWprKyshVEAAAAAAOD9bfXyI4888kjuvPPOattuuOGGdOnSJW3atMnpp5+etWvX1vqAAAAAAACwyVZH7UmTJuWZZ56pevyHP/who0aNypAhQzJu3Lj8+te/zuTJk7fJkAAAAAAAkNRg+ZH58+fn29/+dtXjW2+9NQcddFB+9KMfJUk6duyY8ePHZ8KECbU+JACQVI6bUe4RAAAAoOy2+k7tN998M23btq16PGfOnBx11FFVjw844IAsWrSodqcDAAAAAIB32Oqo3bZt27z88stJknXr1mXevHnp169f1fMrV65MvXr1an9CAAAAAAD4/2x11D766KMzbty4PPTQQ7nwwgvTuHHjfPrTn656/umnn07Xrl23yZAAAAAAAJDUYE3tb3/72znxxBMzcODANG3aND/96U9Tv379qud/8pOf5IgjjtgmQwIAAAAAQFKDqL3bbrvlwQcfzPLly9O0adPssssu1Z6//fbb07Rp01ofEAAAAAAANtnqqL1JixYttri9VatWH3kYAAAAAAB4P1u9pjYAAAAAAJSbqA0AAAAAQGGI2gAAAAAAFMZWRe1/+Zd/yZtvvpkkmTRpUt56661tOhQAAAAAAGzJVkXtZ599NqtXr06STJw4MatWrdqmQwEAAAAAwJbU3Zqd+vTpk5EjR+aQQw5JqVTKZZddlqZNm25x34svvrhWBwQAAAAAgE22Kmpff/31GT9+fO68885UVFTk7rvvTt26m7+0oqJC1AbKonLcjHKPAAAAAMDHYKuidvfu3XPrrbcmSerUqZOZM2emTZs223QwAAAAAAB4t62K2u+0cePGbTEHAAAAAAB8oBpH7SR56aWXMnXq1Dz77LNJkp49e+brX/96unbtWqvDAQAAAADAO9Wp6Qvuvffe9OzZM48//nh69+6d3r1757HHHsu+++6b+++/f1vMCAAAAAAAST7Endrjxo3LmDFjMmXKlM22f+Mb38jhhx9ea8MBAAAAAMA71fhO7WeffTajRo3abPuXv/zl/OlPf6qVoQAAAAAAYEtqHLV33333zJ8/f7Pt8+fPT5s2bWpjJgAAAAAA2KIaLz/yla98Jaeffnr+8pe/5F//9V+TJA8//HAuueSSjB07ttYHBAAAAACATWoctS+66KI0a9Ysl19+eS688MIkSfv27TNhwoScffbZtT4gAAAAAABsUuOoXVFRkTFjxmTMmDFZuXJlkqRZs2a1PhgAAAAAALxbjaP2O4nZAAAAAAB8nGr8RZEAAAAAAFAuojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhfGhovaZZ56ZN954o7ZnAQAAAACA97XVUfuvf/1r1b9vvvnmrFq1KknyyU9+MosWLar9yQAAAAAA4F3qbu2OPXr0SOvWrXPwwQdnzZo1WbRoUTp16pQFCxZk/fr123JGAAAAAABIUoM7tZctW5bbb789ffv2zcaNG3P00Udn7733ztq1a3PvvfdmyZIl23JOAAAAAADY+qi9fv36HHjggTn33HPTqFGj/P73v891112XXXbZJT/5yU/SpUuXdO/efVvOCgAAAADATm6rlx9p2bJl+vTpk4MPPjjr1q3L22+/nYMPPjh169bNz3/+83ziE5/IE088sS1nBQAAAABgJ7fVd2q/8sor+da3vpUGDRrkH//4R/r27ZtPf/rTWbduXebNm5eKiooccsghNXrzyZMn54ADDkizZs3Spk2bnHDCCXn++eer7bNmzZqMHj06rVu3TtOmTTNs2DBLnQAAAAAA7KS2OmrvtttuOfbYYzN58uQ0btw4TzzxRM4666xUVFTkvPPOS4sWLTJw4MAavfmcOXMyevToPProo7n//vuzfv36HHHEEVm9enXVPmPGjMmvf/3r3H777ZkzZ05effXVnHjiiTV6HwAAAAAAdgxbvfzIu7Vo0SInnXRSRo0alQceeCCNGzfOnDlzanSMe+65p9rj66+/Pm3atMncuXMzYMCALF++PNdee21uvvnmHHrooUmS6667Lvvss08effTR9OvX78OODwAAAABAAW31ndrv9PTTT6dDhw5Jks6dO6devXpp165dTj755I80zPLly5MkrVq1SpLMnTs369evz5AhQ6r26dGjRzp16pRHHnlki8dYu3ZtVqxYUe0HAAAAAIAdw4eK2h07dkydOv986R//+Md07NjxIw+ycePGnHPOOTn44IPTq1evJMnixYtTv379tGzZstq+bdu2zeLFi7d4nMmTJ6dFixZVP7UxGwAAAAAA24cPFbW3hdGjR+ePf/xjbr311o90nAsvvDDLly+v+lm0aFEtTQgAAAAAQLl96DW1a9OZZ56ZO++8Mw8++GDVsiZJ0q5du6xbty7Lli2rdrf2kiVL0q5duy0eq0GDBmnQoMG2HhkAAAAAgDIo653apVIpZ555Zu6444488MAD6dKlS7Xn+/btm3r16mXmzJlV255//vksXLgw/fv3/7jHBQAAAACgzMp6p/bo0aNz880355e//GWaNWtWtU52ixYt0qhRo7Ro0SKjRo3K2LFj06pVqzRv3jxnnXVW+vfvn379+pVzdAAAAAAAyqCsUfuqq65KkgwaNKja9uuuuy4jRoxIklx55ZWpU6dOhg0blrVr12bo0KH54Q9/+DFPCgAAAADA9qCsUbtUKn3gPg0bNsy0adMybdq0j2EiAAAAAAC2Z2VdUxsAAAAAAGpC1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMOqWewAAAAAAoHZVjptR7hE2s2DKMeUegR2EO7UBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCELUBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCELUBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCELUBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCELUBAAAAACiMuuUeACieynEzyj0CAAAAADspd2oDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFUbfcAwAAAADA1qgcN6PcIwDbAXdqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYZY3aDz74YI499ti0b98+FRUVmT59erXnS6VSLr744uyxxx5p1KhRhgwZkhdffLE8wwIAAAAAUHZljdqrV6/Ofvvtl2nTpm3x+UsvvTQ/+MEPcvXVV+exxx5LkyZNMnTo0KxZs+ZjnhQAAAAAgO1B3XK++VFHHZWjjjpqi8+VSqVMnTo13/rWt3L88ccnSW644Ya0bds206dPzymnnPJxjgoAAAAAwHZgu11T++WXX87ixYszZMiQqm0tWrTIQQcdlEceeeQ9X7d27dqsWLGi2g8AAAAAADuG7TZqL168OEnStm3batvbtm1b9dyWTJ48OS1atKj66dix4zadEwAAAACAj892G7U/rAsvvDDLly+v+lm0aFG5RwIAAAAAoJZst1G7Xbt2SZIlS5ZU275kyZKq57akQYMGad68ebUfAAAAAAB2DNtt1O7SpUvatWuXmTNnVm1bsWJFHnvssfTv37+MkwEAAAAAUC51y/nmq1atyp///Oeqxy+//HLmz5+fVq1apVOnTjnnnHPyne98J3vttVe6dOmSiy66KO3bt88JJ5xQvqEBAAAAACibskbtJ598MoMHD656PHbs2CTJ8OHDc/311+eCCy7I6tWrc/rpp2fZsmU55JBDcs8996Rhw4blGhkAAAAAgDKqKJVKpXIPsS2tWLEiLVq0yPLly62vDbWkctyMco8AAAAAFMyCKceUewS2c1vbcrfbNbUBAAAAAODdRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKo265BwDeW+W4GeUeAQAAAAC2K+7UBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDDqlnsAAAAAAGDHVzluRrlH2MyCKceUewQ+BHdqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGHXLPQBsLyrHzSj3CAAAAADAB3CnNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBiiNgAAAAAAhSFqAwAAAABQGKI2AAAAAACFIWoDAAAAAFAYojYAAAAAAIUhagMAAAAAUBh1yz0AAAAAAEA5VI6bUe4RNrNgyjHlHmG7505tAAAAAAAKQ9QGAAAAAKAwRG0AAAAAAApD1AYAAAAAoDBEbQAAAAAACkPUBgAAAACgMERtAAAAAAAKQ9QGAAAAAKAwChG1p02blsrKyjRs2DAHHXRQHn/88XKPBAAAAABAGWz3UfvnP/95xo4dm/Hjx2fevHnZb7/9MnTo0CxdurTcowEAAAAA8DHb7qP2FVdcka985SsZOXJkevbsmauvvjqNGzfOT37yk3KPBgAAAADAx6xuuQd4P+vWrcvcuXNz4YUXVm2rU6dOhgwZkkceeWSLr1m7dm3Wrl1b9Xj58uVJkhUrVmzbYSm8jWvfKvcIAAAAAOzkduaOuel3L5VK77vfdh21//73v2fDhg1p27Ztte1t27bNc889t8XXTJ48ORMnTtxse8eOHbfJjAAAAAAAtaXF1HJPUH4rV65MixYt3vP57TpqfxgXXnhhxo4dW/V448aNeeONN9K6detUVFSUcbKP34oVK9KxY8csWrQozZs3L/c4sM0419kZOM/ZWTjX2Rk4z9lZONfZGTjP2Vl8XOd6qVTKypUr0759+/fdb7uO2rvttlt22WWXLFmypNr2JUuWpF27dlt8TYMGDdKgQYNq21q2bLmtRiyE5s2bu7CyU3CuszNwnrOzcK6zM3Ces7NwrrMzcJ6zs/g4zvX3u0N7k+36iyLr16+fvn37ZubMmVXbNm7cmJkzZ6Z///5lnAwAAAAAgHLYru/UTpKxY8dm+PDh2X///XPggQdm6tSpWb16dUaOHFnu0QAAAAAA+Jht91H75JNPzt/+9rdcfPHFWbx4cfr06ZN77rlnsy+PZHMNGjTI+PHjN1uOBXY0znV2Bs5zdhbOdXYGznN2Fs51dgbOc3YW29u5XlEqlUrlHgIAAAAAALbGdr2mNgAAAAAAvJOoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNo7sGnTpqWysjINGzbMQQcdlMcff7zcI0GtmTBhQioqKqr99OjRo9xjwUf24IMP5thjj0379u1TUVGR6dOnV3u+VCrl4osvzh577JFGjRplyJAhefHFF8szLHwEH3SujxgxYrPr/JFHHlmeYeFDmjx5cg444IA0a9Ysbdq0yQknnJDnn3++2j5r1qzJ6NGj07p16zRt2jTDhg3LkiVLyjQx1NzWnOeDBg3a7Jr+1a9+tUwTw4dz1VVXpXfv3mnevHmaN2+e/v375+6776563vWcHcEHnefb0/Vc1N5B/fznP8/YsWMzfvz4zJs3L/vtt1+GDh2apUuXlns0qDX77rtvXnvttaqf3/72t+UeCT6y1atXZ7/99su0adO2+Pyll16aH/zgB7n66qvz2GOPpUmTJhk6dGjWrFnzMU8KH80HnetJcuSRR1a7zt9yyy0f44Tw0c2ZMyejR4/Oo48+mvvvvz/r16/PEUcckdWrV1ftM2bMmPz617/O7bffnjlz5uTVV1/NiSeeWMapoWa25jxPkq985SvVrumXXnppmSaGD6dDhw6ZMmVK5s6dmyeffDKHHnpojj/++DzzzDNJXM/ZMXzQeZ5sP9fzilKpVCrLO7NNHXTQQTnggAPyX//1X0mSjRs3pmPHjjnrrLMybty4Mk8HH92ECRMyffr0zJ8/v9yjwDZTUVGRO+64IyeccEKSf96l3b59+5x77rk577zzkiTLly9P27Ztc/311+eUU04p47Tw4b37XE/+eaf2smXLNruDG4rsb3/7W9q0aZM5c+ZkwIABWb58eXbffffcfPPN+dznPpckee6557LPPvvkkUceSb9+/co8MdTcu8/z5J939vXp0ydTp04t73BQy1q1apXvfe97+dznPud6zg5r03k+atSo7ep67k7tHdC6desyd+7cDBkypGpbnTp1MmTIkDzyyCNlnAxq14svvpj27dtnzz33zBe/+MUsXLiw3CPBNvXyyy9n8eLF1a7vLVq0yEEHHeT6zg5p9uzZadOmTbp3754zzjgjr7/+erlHgo9k+fLlSf75/xwmydy5c7N+/fpq1/UePXqkU6dOrusU1rvP801uuumm7LbbbunVq1cuvPDCvPXWW+UYD2rFhg0bcuutt2b16tXp37+/6zk7pHef55tsL9fzumV5V7apv//979mwYUPatm1bbXvbtm3z3HPPlWkqqF0HHXRQrr/++nTv3j2vvfZaJk6cmE9/+tP54x//mGbNmpV7PNgmFi9enCRbvL5veg52FEceeWROPPHEdOnSJS+99FL+/d//PUcddVQeeeSR7LLLLuUeD2ps48aNOeecc3LwwQenV69eSf55Xa9fv35atmxZbV/XdYpqS+d5knzhC19I586d0759+zz99NP5xje+keeffz6/+MUvyjgt1Nwf/vCH9O/fP2vWrEnTpk1zxx13pGfPnpk/f77rOTuM9zrPk+3rei5qA4V01FFHVf27d+/eOeigg9K5c+fcdtttGTVqVBknA6A2vHM5nU9+8pPp3bt3unbtmtmzZ+ewww4r42Tw4YwePTp//OMffQcIO7T3Os9PP/30qn9/8pOfzB577JHDDjssL730Urp27fpxjwkfWvfu3TN//vwsX748//M//5Phw4dnzpw55R4LatV7nec9e/bcrq7nlh/ZAe22227ZZZddNvuW3SVLlqRdu3Zlmgq2rZYtW2bvvffOn//853KPAtvMpmu46zs7oz333DO77bab6zyFdOaZZ+bOO+/MrFmz0qFDh6rt7dq1y7p167Js2bJq+7uuU0TvdZ5vyUEHHZQkrukUTv369dOtW7f07ds3kydPzn777Zfvf//7rufsUN7rPN+Scl7PRe0dUP369dO3b9/MnDmzatvGjRszc+bMamvgwI5k1apVeemll7LHHnuUexTYZrp06ZJ27dpVu76vWLEijz32mOs7O7y//vWvef31113nKZRSqZQzzzwzd9xxRx544IF06dKl2vN9+/ZNvXr1ql3Xn3/++SxcuNB1ncL4oPN8SzZ92btrOkW3cePGrF271vWcHdqm83xLynk9t/zIDmrs2LEZPnx49t9//xx44IGZOnVqVq9enZEjR5Z7NKgV5513Xo499th07tw5r776asaPH59ddtkln//858s9Gnwkq1atqvZfuV9++eXMnz8/rVq1SqdOnXLOOefkO9/5Tvbaa6906dIlF110Udq3b58TTjihfEPDh/B+53qrVq0yceLEDBs2LO3atctLL72UCy64IN26dcvQoUPLODXUzOjRo3PzzTfnl7/8ZZo1a1a1rmqLFi3SqFGjtGjRIqNGjcrYsWPTqlWrNG/ePGeddVb69++ffv36lXl62DofdJ6/9NJLufnmm3P00UendevWefrppzNmzJgMGDAgvXv3LvP0sPUuvPDCHHXUUenUqVNWrlyZm2++ObNnz869997res4O4/3O8+3uel5ih/Wf//mfpU6dOpXq169fOvDAA0uPPvpouUeCWnPyySeX9thjj1L9+vVLn/jEJ0onn3xy6c9//nO5x4KPbNasWaUkm/0MHz68VCqVShs3bixddNFFpbZt25YaNGhQOuyww0rPP/98eYeGD+H9zvW33nqrdMQRR5R23333Ur169UqdO3cufeUrXyktXry43GNDjWzpHE9Suu6666r2efvtt0tf+9rXSrvuumupcePGpc9+9rOl1157rXxDQw190Hm+cOHC0oABA0qtWrUqNWjQoNStW7fS+eefX1q+fHl5B4ca+vKXv1zq3LlzqX79+qXdd9+9dNhhh5Xuu+++quddz9kRvN95vr1dzytKpVLp44zoAAAAAADwYVlTGwAAAACAwhC1AQAAAAAoDFEbAAAAAIDCELUBAAAAACgMURsAAAAAgMIQtQEAAAAAKAxRGwAAAACAwhC1AQAAAAAoDFEbAACSPPfcc+nXr18aNmyYPn36lHucLRo0aFDOOeecD/Xa66+/Pi1btqzVeQAAoBxEbQAACuVvf/tb6tevn9WrV2f9+vVp0qRJFi5c+JGPO378+DRp0iTPP/98Zs6cudnzV199dZo1a5Z//OMfVdtWrVqVevXqZdCgQdX2nT17dioqKvLSSy995Llqy8knn5wXXnjhIx1j0++1bNmy2hmqBioqKjJ9+vSP/X0BANj+iNoAABTKI488kv322y9NmjTJvHnz0qpVq3Tq1OkjH/ell17KIYccks6dO6d169abPT948OCsWrUqTz75ZNW2hx56KO3atctjjz2WNWvWVG2fNWtWOnXqlK5du9Z4jlKpVC2c15ZGjRqlTZs2tX5cAAD4uInaAAAUyu9+97scfPDBSZLf/va3Vf9+Pxs3bsykSZPSoUOHNGjQIH369Mk999xT9XxFRUXmzp2bSZMmpaKiIhMmTNjsGN27d88ee+yR2bNnV22bPXt2jj/++HTp0iWPPvpote2DBw9OkqxduzZnn3122rRpk4YNG+aQQw7JE088UW3fioqK3H333enbt28aNGiQ3/72t1m9enVOO+20NG3aNHvssUcuv/zyzWb64Q9/mL322isNGzZM27Zt87nPfe49P4N3Lz8yYcKE9OnTJzfeeGMqKyvTokWLnHLKKVm5cuUHfp7vPua9996bffbZJ02bNs2RRx6Z1157rWqfESNG5IQTTsjEiROz++67p3nz5vnqV7+adevWVe1TWVmZqVOnVjt2nz59qv4OlZWVSZLPfvazqaioqHr81FNPZfDgwWnWrFmaN2+evn37VvuPDgAA7JhEbQAAtnsLFy5My5Yt07Jly1xxxRW55ppr0rJly/z7v/97pk+fnpYtW+ZrX/vae77++9//fi6//PJcdtllefrppzN06NAcd9xxefHFF5Mkr732Wvbdd9+ce+65ee2113Leeedt8TiDBw/OrFmzqh7PmjUrgwYNysCBA6u2v/3223nssceqovYFF1yQ//f//l9++tOfZt68eenWrVuGDh2aN954o9qxx40blylTpuTZZ59N7969c/7552fOnDn55S9/mfvuuy+zZ8/OvHnzqvZ/8sknc/bZZ2fSpEl5/vnnc88992TAgAE1+lxfeumlTJ8+PXfeeWfuvPPOzJkzJ1OmTKnRMd56661cdtllufHGG/Pggw9m4cKFm31+M2fOzLPPPpvZs2fnlltuyS9+8YtMnDhxq99j038EuO666/Laa69VPf7iF7+YDh065IknnsjcuXMzbty41KtXr0bzAwBQPHXLPQAAAHyQ9u3bZ/78+VmxYkX233//PPbYY2nSpEn69OmTGTNmpFOnTmnatOl7vv6yyy7LN77xjZxyyilJkksuuSSzZs3K1KlTM23atLRr1y5169ZN06ZN065du/c8zuDBg3POOefkH//4R95+++38/ve/z8CBA7N+/fpcffXVSf65PMratWszePDgrF69OldddVWuv/76HHXUUUmSH/3oR7n//vtz7bXX5vzzz6869qRJk3L44Ycn+eda3ddee21+9rOf5bDDDkuS/PSnP02HDh2q9l+4cGGaNGmSz3zmM2nWrFk6d+6cT33qUzX6XDdu3Jjrr78+zZo1S5KceuqpmTlzZv7jP/5jq4+x6XfftNTKmWeemUmTJlXbp379+vnJT36Sxo0bZ999982kSZNy/vnn59vf/nbq1Png+2x23333JEnLli2r/X0WLlyY888/Pz169EiS7LXXXls9NwAAxeVObQAAtnt169ZNZWVlnnvuuRxwwAHp3bt3Fi9enLZt22bAgAGprKzMbrvttsXXrlixIq+++upmy5QcfPDBefbZZ2s0x6BBg7J69eo88cQTeeihh7L33ntn9913z8CBA6vW1Z49e3b23HPPdOrUKS+99FLWr19f7b3r1auXAw88cLP33n///av+/dJLL2XdunU56KCDqra1atUq3bt3r3p8+OGHp3Pnztlzzz1z6qmn5qabbspbb71Vo9+nsrKyKmgnyR577JGlS5fW6BiNGzeutnb4lo6x3377pXHjxlWP+/fvn1WrVmXRokU1eq93Gzt2bP7t3/4tQ4YMyZQpU7arL+YEAGDbEbUBANju7bvvvmnatGlOPfXUPP7442natGkOO+ywLFiwIE2bNs2+++77sczRrVu3dOjQIbNmzcqsWbMycODAJP+8k7xjx4753e9+l1mzZuXQQw+t8bGbNGlSo/2bNWuWefPm5ZZbbskee+yRiy++OPvtt1+WLVu21cd491IdFRUV2bhxY43m2NIxSqVSjY5Rp06dzV6zfv36D3zdhAkT8swzz+SYY47JAw88kJ49e+aOO+6o0XsDAFA8ojYAANu9u+66K/Pnz0+7du3ys5/9LPPnz0+vXr0yderUzJ8/P3fdddd7vrZ58+Zp3759Hn744WrbH3744fTs2bPGswwePDizZ8/O7NmzM2jQoKrtAwYMyN13353HH3+8aj3trl27pn79+tXee/369XniiSfe9727du2aevXq5bHHHqva9uabb+aFF16otl/dunUzZMiQXHrppXn66aezYMGCPPDAAzX+nba1p556Km+//XbV40cffTRNmzZNx44dk/xzeZF3frnkihUr8vLLL1c7Rr169bJhw4bNjr333ntnzJgxue+++3LiiSfmuuuu20a/BQAA2wtragMAsN3r3LlzFi9enCVLluT4449PRUVFnnnmmQwbNix77LHHB77+/PPPz/jx49O1a9f06dMn1113XebPn5+bbrqpxrMMHjw4o0ePzvr166vu1E6SgQMH5swzz8y6deuqonaTJk1yxhln5Pzzz0+rVq3SqVOnXHrppXnrrbcyatSo93yPpk2bZtSoUTn//PPTunXrtGnTJt/85jerrT9955135i9/+UsGDBiQXXfdNXfddVc2btxYbYmS7cW6desyatSofOtb38qCBQsyfvz4nHnmmVW/z6GHHprrr78+xx57bFq2bJmLL744u+yyS7VjVFZWZubMmTn44IPToEGDNGzYMOeff34+97nPpUuXLvnrX/+aJ554IsOGDSvHrwgAwMdI1AYAoBBmz56dAw44IA0bNsxDDz2UDh06bFXQTpKzzz47y5cvz7nnnpulS5emZ8+e+dWvfvWhvlhw8ODBefvtt9OjR4+0bdu2avvAgQOzcuXKdO/evdpcU6ZMycaNG3Pqqadm5cqV2X///XPvvfdm1113fd/3+d73vpdVq1bl2GOPTbNmzXLuuedm+fLlVc+3bNkyv/jFLzJhwoSsWbMme+21V2655ZaPbSmWmjjssMOy1157ZcCAAVm7dm0+//nPZ8KECVXPX3jhhXn55Zfzmc98Ji1atMi3v/3tze7UvvzyyzN27Nj86Ec/yic+8Ym88MILef3113PaaadlyZIl2W233XLiiSdm4sSJH/NvBwDAx62iVNMF7wAAALbSiBEjsmzZskyfPr3cowAAsIOwpjYAAAAAAIUhagMAAAAAUBiWHwEAAAAAoDDcqQ0AAAAAQGGI2gAAAAAAFIaoDQAAAABAYYjaAAAAAAAUhqgNAAAAAEBhiNoAAAAAABSGqA0AAAAAQGGI2gAAAAAAFMb/D3iwqqFeTZcaAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The maximum length of inputs is 33 words.\n"
          ]
        }
      ],
      "source": [
        "df['input_length'] = df['context'].str.split().str.len()\n",
        "\n",
        "n_bin = max(df['input_length']) + 1\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 inputs is {max(df['input_length'])} words.\")\n",
        "\n",
        "df = df.drop(columns=['input_length'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBf1z80a5JQ-"
      },
      "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": "3bfmC_JW5JQ-",
        "outputId": "355a28d3-3178-4bc0-aaf4-adc9ca3cab2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Shape: (702, 13)\n",
            "Testing Dataset Shape: (116, 13)\n",
            "Evaluation Dataset Shape: (39, 13)\n",
            "{'context': Value(dtype='string', id=None), '0': Value(dtype='string', id=None), '1': Value(dtype='string', id=None), '2': Value(dtype='string', id=None), '3': Value(dtype='string', id=None), '4': Value(dtype='string', id=None), '5': Value(dtype='string', id=None), '6': Value(dtype='string', id=None), '7': Value(dtype='string', id=None), '8': Value(dtype='string', id=None), '9': Value(dtype='string', id=None), 'labels': Value(dtype='int32', 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=0.18)\n",
        "test_eval = train_testeval['test'].train_test_split(test_size=0.25)\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": "d6EtNu845JQ-"
      },
      "source": [
        "#### Display Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wC2n1gGf5JQ-",
        "outputId": "0df211b5-178a-4801-b867-6790224ac86a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'context': 'I lost my parents when I was sent away partly and probably for safety from a war-torn country (highest crime rate in the world).',\n",
              " '0': \"Because of that i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '1': \"Really, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '2': \"Currently, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '3': \"Soon, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '4': \"Hopefully, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '5': \"Admittedly, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '6': \"Thus, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '7': \"Presumably, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '8': \"Curiously, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " '9': \"Eventually, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\",\n",
              " 'labels': 0,\n",
              " '__index_level_0__': 371}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ds['train'][12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LA-hYD6X5JQ-"
      },
      "source": [
        "#### Create Function to Display an Example Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "BTgGwEwb5JQ-"
      },
      "outputs": [],
      "source": [
        "def show_one_example(sample):\n",
        "    print(f\"Context: {sample['context']}\")\n",
        "    print(f\"Option 0: {sample['0']}\")\n",
        "    print(f\"Option 1: {sample['1']}\")\n",
        "    print(f\"Option 2: {sample['2']}\")\n",
        "    print(f\"Option 3: {sample['3']}\")\n",
        "    print(f\"Option 4: {sample['4']}\")\n",
        "    print(f\"Option 5: {sample['5']}\")\n",
        "    print(f\"Option 6: {sample['6']}\")\n",
        "    print(f\"Option 7: {sample['7']}\")\n",
        "    print(f\"Option 8: {sample['8']}\")\n",
        "    print(f\"Option 9: {sample['9']}\")\n",
        "    print(f\"\\nGround Truth: Option {sample['labels']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iIB4e7O75JQ_"
      },
      "source": [
        "#### Display an Example Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgYxyfDc5JQ_",
        "outputId": "5f5f694f-34e0-425f-f9c5-9436b3c9adf4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Context: I lost my parents when I was sent away partly and probably for safety from a war-torn country (highest crime rate in the world).\n",
            "Option 0: Because of that i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 1: Really, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 2: Currently, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 3: Soon, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 4: Hopefully, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 5: Admittedly, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 6: Thus, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 7: Presumably, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 8: Curiously, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "Option 9: Eventually, i've always felt like an outsider and have always traveled trying to find my place in the world and where I fit in.\n",
            "\n",
            "Ground Truth: Option 0\n"
          ]
        }
      ],
      "source": [
        "show_one_example(ds['train'][12])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SxRaC1i55JQ_"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "dtOCOmKt5JQ_"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT = \"bert-base-uncased\"\n",
        "MODEL_NAME = MODEL_CKPT.split(\"/\")[-1] + \"-Discourse_Marker_QA\"\n",
        "\n",
        "STRATEGY = \"epoch\"\n",
        "LEARNING_RATE = 5e-5\n",
        "\n",
        "BATCH_SIZE = 16\n",
        "NUM_OF_EPOCHS = 20\n",
        "\n",
        "WEIGHT_DECAY = 0.01\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "\n",
        "set_seed = 42"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lwzrrbil5JQ_"
      },
      "source": [
        "#### Instantiate Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "8d72cc251dcc44b4bb858de32c0eef82",
            "c8e7f059171f43a582ae295d31ac4fc6",
            "9880708a8fcc473bbe92ac17f9907a63",
            "2cf197bef208491da40b2697b1135e7f",
            "decbe63441394563b10ae91a664f194c",
            "5428dd95aa2046e29054ff0104275ff0",
            "1e2789acc9f8416c879cbff1b84842c5",
            "8aff2d9c75eb4e128fa4b9209a87112b",
            "c188d1ca66ba4c649619f928d4e9f484",
            "f6970a48823c4c0e9c27d92cb10b0554",
            "4e2f2e23fe4047bf9d86dacd7ebc7c1e",
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            "c9726a2b9115471c9137ede508b74b1d",
            "0ef6761c8c384cc6a444fab8881442f7",
            "5f3384b34bc04d5fbb0cc501d705b6f4",
            "fe311d3f270c4ee0b09b510fdd3f6893",
            "bd0378335c124b01a5812271bca6127c",
            "8ee33067d78a4684a010f537abff70bf",
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            "d10678a9673c44e490aae3fbc0e63148",
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            "7bc7df5c8ed245349541672fd6451548",
            "6ae5a927b3074d62a50bd3a0fee98050",
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            "d970d07b12664aee82d168eae64d432f",
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            "6ea4bdef81084893ab15bebb2f0e7534",
            "5d841113c0fc41df9e86e106b848743b",
            "f80317becf6241a0b3ef5a03a36358b3",
            "e1ce2e9b8dbf4149b6c84fcef6ba0fe8",
            "8a1c3dfa94d946028eab413632756ed0",
            "99c04d58cd7348d8a4af03f8b962de67",
            "a80e86e349bc4821b596f4d30ca7b484",
            "c0942829e1f04f0b800323a62309dea2"
          ]
        },
        "id": "wneejyxj5JQ_",
        "outputId": "1c4ccb7f-6927-4249-fb9f-996e525766bb"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8d72cc251dcc44b4bb858de32c0eef82",
              "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": "bac2f972f6e84648bd28e7f149f13f76",
              "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": "0c5db2cd216c4265b86e2931869efa5c",
              "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": "d970d07b12664aee82d168eae64d432f",
              "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": "GQr4KOAG5JQ_"
      },
      "source": [
        "#### Create Preprocessing Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "xDSz9Ih95JQ_"
      },
      "outputs": [],
      "source": [
        "ending_names = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n",
        "\n",
        "def preprocess_function(samples):\n",
        "    input_headers = samples[\"context\"]\n",
        "    input = [[f\"{header} {samples[end][i]}\" for end in ending_names] 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, len(v), len(ending_names))]\n",
        "            for k, v in tokenized_samples.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ac-k8ZP35JQ_"
      },
      "source": [
        "#### Apply Preprocessing Function to Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "e0937136a9da42ec900429da003b7e1d",
            "47829ff8f8ac4f53b65a2304a6a4a1cb",
            "54e5f47061be46049cbabd15beee5bce",
            "7a397b6c919446abbbc178816f4efe6f",
            "0e9e3c4b6abf4b44a40b5592dc82baab",
            "5a04eda5feeb4816adb1fc2a1a040bcc",
            "93392e2a28fe42d7addd087ed654c2b4",
            "e7fe917e190c4dad9eb44b2f9e2a5f00",
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            "b70f881a46e3493d80fd4f7f4afb0f87",
            "ca9c92184b2346e9a89093c4ac59afa8",
            "a5b1498690174bd59c7362e42759ebc3",
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            "163d68bcfa874b99bab6fa8cf27d2051",
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            "4b33cfd7d3af4b729404e093ded29d03",
            "bcb9e0e9a4a14bf3971a9bfc6676eb20",
            "2e674c18143f4aa6a6ec9d9da83e54c5",
            "baacfa6a50264c2cb0f0535a18866af6",
            "0d6d81333db244d9a24bf945861e5646",
            "ecf5e24f053f4c4fa4ed14140ef39b48"
          ]
        },
        "id": "sK6lgOiU5JQ_",
        "outputId": "2425b23d-c04d-4754-e761-62b7aa3e7b00"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0937136a9da42ec900429da003b7e1d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/702 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4358aa4db634321b5d8b587f1a920ec",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/116 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d077b8a5c944d658fb58945bd26e7bf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/39 [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": "7IaT4t9T5JQ_",
        "outputId": "9a61de26-c509-43c1-8be8-279a592763ee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'context': Value(dtype='string', id=None),\n",
              " '0': Value(dtype='string', id=None),\n",
              " '1': Value(dtype='string', id=None),\n",
              " '2': Value(dtype='string', id=None),\n",
              " '3': Value(dtype='string', id=None),\n",
              " '4': Value(dtype='string', id=None),\n",
              " '5': Value(dtype='string', id=None),\n",
              " '6': Value(dtype='string', id=None),\n",
              " '7': Value(dtype='string', id=None),\n",
              " '8': Value(dtype='string', id=None),\n",
              " '9': Value(dtype='string', id=None),\n",
              " 'labels': Value(dtype='int32', 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": "j83vP9oZ5JQ_"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "3d5f7d6aaa3c4838a1a521fa79a02b93",
            "5b3094508e8e45778173474bb8337145",
            "b1729baf897941c6998c90529577092c",
            "010ded1dacd747dabd5ed7fdd30e14d4",
            "2255654270a74e6c928ab552c7c02fe3",
            "d8647b1be996495eb23851f543e70c5d",
            "a74d5c155e4f4dd6af3eab791165a986",
            "cfffb722aaaf4b938fae8c7a21703775",
            "18dc244fd408474fb997a59ff431b75d",
            "c424b3cf57504d59ab22e56d9a2e2eb1",
            "975604e970fc45d7819b6cee6c4c20e5"
          ]
        },
        "id": "XkxrhSHo5JQ_",
        "outputId": "b4c24bd8-a431-4879-ef3b-56a2801fd56c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3d5f7d6aaa3c4838a1a521fa79a02b93",
              "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": "H4avkygj5JQ_"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ma3vvIP85JQ_"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    output_dir=MODEL_NAME,\n",
        "    evaluation_strategy=STRATEGY,\n",
        "    save_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",
        "    hub_private_repo=True,\n",
        "    load_best_model_at_end=True,\n",
        "    logging_steps=25,\n",
        "    group_by_length=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBLlHuuM5JQ_"
      },
      "source": [
        "#### Define Data Collator Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "KedUZGbr5JQ_"
      },
      "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": "BX5g_5s15JQ_"
      },
      "source": [
        "#### Create Function to Compute Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "QJ7-xVn25JQ_"
      },
      "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": "B8rWoNn25JRA"
      },
      "source": [
        "#### Instantiate Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ca2tlXV95JRA",
        "outputId": "9c450794-1920-4f20-e4a8-89ff31e9368d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Discourse_Marker_QA into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/bert-base-uncased-Discourse_Marker_QA 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[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=DataCollatorForMultipleChoice(tokenizer),\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENjqQ_UB5JRA"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        },
        "id": "AA9TA3GO5JRA",
        "outputId": "af8a7f25-2064-4464-c965-783d58be5052"
      },
      "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='1760' max='1760' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1760/1760 43:59, Epoch 20/20]\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>1.917400</td>\n",
              "      <td>1.710502</td>\n",
              "      <td>0.387931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.088900</td>\n",
              "      <td>1.502229</td>\n",
              "      <td>0.482759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.517900</td>\n",
              "      <td>1.507482</td>\n",
              "      <td>0.568965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.227100</td>\n",
              "      <td>2.440201</td>\n",
              "      <td>0.491379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.164100</td>\n",
              "      <td>2.103619</td>\n",
              "      <td>0.568965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.053700</td>\n",
              "      <td>2.633247</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.035600</td>\n",
              "      <td>2.763919</td>\n",
              "      <td>0.560345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.042800</td>\n",
              "      <td>2.366212</td>\n",
              "      <td>0.603448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.007300</td>\n",
              "      <td>2.646668</td>\n",
              "      <td>0.594828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.001600</td>\n",
              "      <td>2.503780</td>\n",
              "      <td>0.603448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.009100</td>\n",
              "      <td>2.605759</td>\n",
              "      <td>0.568965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.003000</td>\n",
              "      <td>2.237900</td>\n",
              "      <td>0.594828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.000600</td>\n",
              "      <td>2.235073</td>\n",
              "      <td>0.612069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>2.346934</td>\n",
              "      <td>0.612069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>2.342007</td>\n",
              "      <td>0.620690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.363102</td>\n",
              "      <td>0.629310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.377109</td>\n",
              "      <td>0.646552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>2.400063</td>\n",
              "      <td>0.637931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.398378</td>\n",
              "      <td>0.620690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.395235</td>\n",
              "      <td>0.620690</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": "H89EY2e_5JRA"
      },
      "source": [
        "#### Push to HuggingFace Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373,
          "referenced_widgets": [
            "11845c89fd8b4e66aba4f2dd9f91d205",
            "3ae0054a0ced4b4eadd6341e5eee8598",
            "6136d7aeba2d45aeb59e7a45619852fb",
            "3f3cd84a6b814281bdb1658b632f6882",
            "779079bd8bc747809a7ca83862bfb70d",
            "2936dd0b260a45abaf4fe91e693a867f",
            "368d6ccf4b5246fea7cc9f00a67b5396",
            "8f5b89626a40428f88a6119f592fbad4",
            "d239c7a5ca7c4a14be5d3073dd9bc451",
            "d266bdbaf9984ae8aee1975b6e2b665c",
            "74ef3d4cc7f04fd2af8fbcdd223d7b59"
          ]
        },
        "id": "fDmnPu_35JRD",
        "outputId": "1d392dee-3dd4-4cc3-b519-9c9ab789c459"
      },
      "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": "11845c89fd8b4e66aba4f2dd9f91d205",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Aug02_20-33-06_1020a9ac92b7/events.out.tfevents.1691008404.1020a9ac92b7.267.0:   0%|         …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-uncased-Discourse_Marker_QA\n",
            "   3c49a49..6ca21b8  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-Discourse_Marker_QA\n",
            "   3c49a49..6ca21b8  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/bert-base-uncased-Discourse_Marker_QA\n",
            "   6ca21b8..b23a5ba  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-uncased-Discourse_Marker_QA\n",
            "   6ca21b8..b23a5ba  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/DunnBC22/bert-base-uncased-Discourse_Marker_QA/commit/6ca21b8a5e8ad87e1527243fc54b3ec9af1eb9ed'"
            ]
          },
          "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": "78d09896-7ebc-4497-a8ae-78ede53a4b17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =       20.0\n",
            "  total_flos               =  4789623GF\n",
            "  train_loss               =      0.214\n",
            "  train_runtime            = 0:44:02.89\n",
            "  train_samples_per_second =      5.312\n",
            "  train_steps_per_second   =      0.666\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": "7jQP7i3Z5JRE"
      },
      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "- While each sample has 10 different answer options, I was expected better results.\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBYNc2fK5JRE"
      },
      "source": [
        "#### Citation\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}"
      ]
    }
  ],
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      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
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      "mimetype": "text/x-python",
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      "pygments_lexer": "ipython3",
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