Sentence_level_multi_label_classification.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "519rMrokGOqk",
        "outputId": "ef1c79b0-d80a-4002-e151-52f512e6d45f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading frozenlist-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (158 kB)\n",
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            "Installing collected packages: tokenizers, xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, transformers, aiohttp, datasets, evaluate\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.11.0 dill-0.3.6 evaluate-0.4.0 frozenlist-1.3.3 huggingface-hub-0.13.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.27.4 xxhash-3.2.0 yarl-1.8.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
      ],
      "metadata": {
        "id": "y2a_h1A1GPVp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "bd8f5be1b8b2457ea893f20cbfcad6cb",
            "cc199b5f1b634bfb801346d0bc60688c",
            "89e7444c4c9e4fb5a2c706b1e87bbb13",
            "bbbf21ba34414715a20bd8302a217ac2",
            "df6c7d5a10ed4862b88be075bfdb5ae8",
            "416a0ddb211a4a42a906bf436e3219c3",
            "0be81ee5ae2c419bbd606bfeb70330f5",
            "27602f133f2f4e76938059ecb5191c43",
            "26ad9180f4d84bb0ba7f82b1b4fb70af",
            "cefe8516d8f449918e82ecb2960c035e",
            "19fe40050c49488f931c0c27910e597d",
            "45dae41f3c804e9492bcb996219afeb7",
            "5818e9c25086417e9dabdb3249eab8f4",
            "bcf64fcfd1ce41a48efa9f15300f84dc",
            "aaa1aa7a6d5a4905833d45fbb2372a53",
            "2c7064ceafb142c8b186603e15b987ce",
            "d54804639d1c4c4fb6c3f9c7d51dc24b",
            "21d384c8dac6496eab8d7c9b29fe7829",
            "b2ba35683dbf42dfa02762c80a52e57b",
            "ed99f38f22a04a12b049f395eb53b45c",
            "bc703039012d4536b00d137d00cf2a89",
            "0b5e5d6200d7446982e029c26fe040e9",
            "220302cb332941758073267d5805b1ab",
            "15ebe25145ed456580f8358ec9a46a9f",
            "d2c5ed995896459eb9f8b4c3ccc3d9cc",
            "c1e1761d119d41de8ac3273e67d8cde1",
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            "4af80ec075c349b997af034f8ab2a221",
            "84b728bea212415ca5ff9b4523b16bc8",
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            "fadfbbcfb97b460fb7d5b062b9870cb6",
            "022b833151fd40a999640e416aa96c02",
            "030a9db7a2fd4f399449ff11edbed76f",
            "be9a5253cbf74b42930b9b506df20679",
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            "edc59bb650ac4f48baf8f46264883309",
            "6f92acfa777e457b971187384d341b46"
          ]
        },
        "outputId": "e55ba5a0-4b9e-4122-d27f-9e4870d02a91"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd8f5be1b8b2457ea893f20cbfcad6cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45dae41f3c804e9492bcb996219afeb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "220302cb332941758073267d5805b1ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "022b833151fd40a999640e416aa96c02"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6UWzsSuCeSq",
        "outputId": "4bbcd2bc-8779-4d85-b07e-5c9ca766e33c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (6.0.4)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (3.10.7)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (1.26.15)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import datasets\n",
        "from datasets import Dataset, DatasetDict\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import json\n",
        "\n",
        "\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# just use df = pd.read_csv('st1_data_train_sent.csv') when running in the repo\n",
        "df = pd.read_csv('/content/drive/My Drive/UP_w22/PM/task 8/data/st1_data_train_sent.csv')\n"
      ],
      "metadata": {
        "id": "k_7Fl8M08NZE"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Unnamed: 0', 'post_id', 'subreddit_id', 'Components'], axis=1)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "buGCF645Ec_Z",
        "outputId": "7f3c77d0-4fb4-46e3-88bf-8e7ad0225cd5"
      },
      "execution_count": 7,
      "outputs": [
        {
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              "                                                 Sentence     Label\n",
              "0       De-Nial\\nI wrote this a few years ago and just...      none\n",
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              "4       Growing up I was an only child with older pare...      none\n",
              "...                                                   ...       ...\n",
              "127354  Just how effective these drugs are and how do ...  question\n",
              "127355                                  Edit2: Thank you.      none\n",
              "127356  It is wonderful that there are drugs that can ...      none\n",
              "127357                I am glad that Trikafta exists NOW.      none\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns={\"Sentence\": \"text\", \"Label\": \"label\"})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "E7vTUL9fEoEt",
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              "                                                     text     label\n",
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              "127355                                  Edit2: Thank you.      none\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
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        "labels = ['question', 'claim', 'claim_per_exp', 'per_exp']\n",
        "\n",
        "for idx, row in df.iterrows(): \n",
        "  text = df.loc[idx, 'text']\n",
        "  label = df.loc[idx, 'label']\n",
        "  if label in labels:\n",
        "    df.loc[idx, 'label'] = labels.index(label) +1\n",
        "  else:\n",
        "    df.loc[idx, 'label'] = 0\n",
        "    \n",
        "df"
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>De-Nial\\nI wrote this a few years ago and just...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I thought I'd share...\\n\\n&amp;#x200B;\\n\\nWhen I w...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Like the opening line of the old Frank Sanatra...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I was getting ready to graduate high school, I...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Growing up I was an only child with older pare...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127354</th>\n",
              "      <td>Just how effective these drugs are and how do ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127355</th>\n",
              "      <td>Edit2: Thank you.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127356</th>\n",
              "      <td>It is wonderful that there are drugs that can ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127357</th>\n",
              "      <td>I am glad that Trikafta exists NOW.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127358</th>\n",
              "      <td>At least most people with CF don't have to hop...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>127359 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5865a429-6a52-413c-b22c-ab403a0e4a69')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5865a429-6a52-413c-b22c-ab403a0e4a69 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-5865a429-6a52-413c-b22c-ab403a0e4a69');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxIApKeuJQSh",
        "outputId": "0cc2642b-a59d-4ffb-e105-a5d10ce27064"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     127359\n",
              "unique         5\n",
              "top            0\n",
              "freq      107681\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove this cell if imbalanced data is wanted\n",
        "smallest = 100000\n",
        "i_len = []\n",
        "\n",
        "for i in [0,1,2,3,4]:\n",
        "  new_len = len(df[df[\"label\"]==i])\n",
        "  i_len.append(new_len)\n",
        "  if new_len < smallest:\n",
        "    smallest = new_len\n",
        "\n",
        "df_four = df[df[\"label\"]==4]\n",
        "df_four = df_four[:smallest]\n",
        "df_three = df[df[\"label\"]==3]\n",
        "df_three = df_three[:smallest]\n",
        "df_two = df[df[\"label\"]==2]\n",
        "df_two = df_two[:smallest]\n",
        "df_one = df[df[\"label\"]==1]\n",
        "df_one = df_one[:smallest]\n",
        "df_zero = df[df[\"label\"]==0]\n",
        "df_zero = df_zero[:smallest]\n",
        "\n",
        "df_balanced = pd.concat([df_zero, df_one, df_two, df_three, df_four])\n",
        "df_balanced = df_balanced.reset_index(drop=True)\n",
        "\n",
        "df = df_balanced.sample(frac=1).reset_index(drop=True)\n",
        "df\n",
        "print(i_len)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9S_nzaqNO-75",
        "outputId": "17dc91d4-b39c-4407-ab97-b16b0d2ca53e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[107681, 6503, 579, 2389, 10207]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7_c0Ow-P6gq",
        "outputId": "3140a2f8-fba6-4085-c0b0-28193d056f1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     2895\n",
              "unique       5\n",
              "top          0\n",
              "freq       579\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "df_train, df_test = train_test_split(df, test_size=0.2)\n",
        "print(len(df_train),len(df_test)) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rC1ddGLETyF",
        "outputId": "81cc46f8-5b41-47d2-f749-8b118b8beed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2316 579\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_train[df_train['label']==1])/len(df_train[df_train['label']==0]))\n",
        "print(len(df_train[df_train['label']==2])/len(df_train[df_train['label']==0]))\n",
        "print(len(df_train[df_train['label']==3])/len(df_train[df_train['label']==0]))\n",
        "print(len(df_train[df_train['label']==4])/len(df_train[df_train['label']==0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Z3eyqbCGKuW",
        "outputId": "22865dd3-b09c-47c2-88a4-1e1d4b96af64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9501039501039501\n",
            "0.9646569646569647\n",
            "0.9708939708939709\n",
            "0.9293139293139293\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df_test[df_test['label']==1])/len(df_test[df_test['label']==0]))\n",
        "print(len(df_test[df_test['label']==2])/len(df_test[df_test['label']==0]))\n",
        "print(len(df_test[df_test['label']==3])/len(df_test[df_test['label']==0]))\n",
        "print(len(df_test[df_test['label']==4])/len(df_test[df_test['label']==0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9hF_dDsGPwF",
        "outputId": "268bd701-0a42-441f-f560-6a5ad8d5871d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.2448979591836735\n",
            "1.1734693877551021\n",
            "1.1428571428571428\n",
            "1.346938775510204\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oHT5orVLGT-o",
        "outputId": "a0a036d5-562c-4b73-dea3-c37ce3e5f304"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text label\n",
              "968   I have so much stuff to do before my move in t...     4\n",
              "1103  What should I be talking with my new doctor ab...     1\n",
              "812   Just out of pure curiosity, whats the highest ...     1\n",
              "1632  I was told that the first ten years were the w...     0\n",
              "1128  26M currently dealing with my first ever Gout ...     0\n",
              "...                                                 ...   ...\n",
              "384   While the Rituxan has held the line in the sen...     0\n",
              "2058  I'm not a fan of taking drugs as the side effe...     0\n",
              "1569  First year I stupidly tried to mitigate with d...     3\n",
              "2113  So I researched it online and apparently the o...     2\n",
              "889            Anyone here with low bp take Ivabradine?     1\n",
              "\n",
              "[579 rows x 2 columns]"
            ],
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              "      <td>I have so much stuff to do before my move in t...</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>Just out of pure curiosity, whats the highest ...</td>\n",
              "      <td>1</td>\n",
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              "      <th>1632</th>\n",
              "      <td>I was told that the first ten years were the w...</td>\n",
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              "      <th>1128</th>\n",
              "      <td>26M currently dealing with my first ever Gout ...</td>\n",
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              "      <th>...</th>\n",
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              "      <td>While the Rituxan has held the line in the sen...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2058</th>\n",
              "      <td>I'm not a fan of taking drugs as the side effe...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1569</th>\n",
              "      <td>First year I stupidly tried to mitigate with d...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2113</th>\n",
              "      <td>So I researched it online and apparently the o...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Anyone here with low bp take Ivabradine?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>579 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b17a0e45-7ead-4e2c-a2d4-b7184141dd3f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b17a0e45-7ead-4e2c-a2d4-b7184141dd3f 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-b17a0e45-7ead-4e2c-a2d4-b7184141dd3f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = DatasetDict()\n",
        "\n",
        "df['train'] = Dataset.from_pandas(df_train)\n",
        "df['test'] = Dataset.from_pandas(df_test)"
      ],
      "metadata": {
        "id": "Fi_fIGG2GsWB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcdcNlxbFs47",
        "outputId": "a00868eb-9288-4f90-d803-06788f478c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', '__index_level_0__'],\n",
              "        num_rows: 2316\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', '__index_level_0__'],\n",
              "        num_rows: 579\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['train'][151]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZ7GGCrFFcAJ",
        "outputId": "1b804985-7f56-47a1-f397-41a13d3684ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'Doing great - update\\nWanted to give a little update as it has been about 5 months since being diagnosed with gout.',\n",
              " 'label': 4,\n",
              " '__index_level_0__': 2263}"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True)"
      ],
      "metadata": {
        "id": "HL1Fdy_zGkxH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_df = df.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "7a70bf4e6fd54de697aa4853cbe7b2c2",
            "f349d7480fde4ef9a4551d856e95db56",
            "a3f9786080b84fbd81036aeec8979574",
            "d529e54540f44eabb594f11168606791",
            "b826882727194ab2b8f18ca1cdda4943",
            "8dd7e0a6308149389d7762cea4df4205",
            "90d76abb1bfa4b4e913caa2a2544a118",
            "06453e0ddce24248b1944929aa612933",
            "294b3e0b5e0842f6a6c2832229fc892b",
            "fc307eec1be94d54b9747892ca5d637a",
            "dccd90321d9f44a8a9684711692db073",
            "dbc8e44097c142fa82470fed1549747d",
            "fc77132380eb42f0be9a1a14b62443e3",
            "4eeed552001c438c8fd60c35e49596c6",
            "9fafe80227d947b4b2bf3fdf7eb421a9",
            "6154fedc896846fb9930eff5a71ecf96",
            "370589d881ee4c2f9a5a833beab23ecb",
            "2245ad582a9e4c9b812fa235622ac9f3",
            "1624888428bd4e3f9fa968b5f80bcd4e",
            "f9c6a510a2b142da9357870fe8d9ed23",
            "de653ecb9a144b4b8c786b97dad1bfac",
            "6613c93b12f14356a60563cd979a1cb0"
          ]
        },
        "id": "31-8_H3BGlEB",
        "outputId": "6e7a74cd-5fca-4fc3-db7f-ebecc0271e44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2316 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a70bf4e6fd54de697aa4853cbe7b2c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/579 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbc8e44097c142fa82470fed1549747d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "xTFDdrXLGwh2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "accuracy = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "id": "2QmO9oufG6wI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "Be8KUEVcHAcU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {0: \"none\", 1: \"question\", 2: \"claim\", 3: \"claim per experience\", 4: \"per experience\"}\n",
        "label2id = {\"none\": 0, \"question\": 1, \"claim\": 2, \"claim per experience\": 3, \"per experience\": 4}"
      ],
      "metadata": {
        "id": "aTiCBZ0VHDJa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    \"distilbert-base-uncased\", num_labels=5, id2label=id2label, label2id=label2id\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eVo4gRzhHH3s",
        "outputId": "54b071b9-b9de-4c74-f5d5-a074320a958a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_transform.weight', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'pre_classifier.weight', 'pre_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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"my_model\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=2,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_df[\"train\"],\n",
        "    eval_dataset=tokenized_df[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "DyGFF4QvHL6P",
        "outputId": "98d7f468-f9b9-4f65-c944-e0133a1440c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/Chung-Fan/my_model into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/Chung-Fan/my_model into local empty directory.\n",
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='290' max='290' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [290/290 00:49, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.057231</td>\n",
              "      <td>0.569948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.980725</td>\n",
              "      <td>0.623489</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=290, training_loss=1.1237328891096443, metrics={'train_runtime': 49.6752, 'train_samples_per_second': 93.246, 'train_steps_per_second': 5.838, 'total_flos': 91864846133040.0, 'train_loss': 1.1237328891096443, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    }
  ]
}