hate_speech.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yuwang1028/694_Team15_DBMS_2024/blob/main/hate_speech.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc2c012b67e854b3",
      "metadata": {
        "collapsed": false,
        "id": "dc2c012b67e854b3"
      },
      "source": [
        "1. Data Process\n",
        "we split the raw dataset into training, development and test sets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "hWC8tGYgh0cV",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWC8tGYgh0cV",
        "outputId": "56f45e91-a36b-48e3-cd0c-f440fa07c21a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install accelerate"
      ]
    },
    {
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      "id": "ES31FY-AVaOx",
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              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving DynamicallyHateDataset.csv to DynamicallyHateDataset.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "initial_id",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "initial_id",
        "outputId": "5258e707-834c-405d-d20f-cc24b5f7d73b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set saved to train.csv\n",
            "Dev set saved to dev.csv\n",
            "Test set saved to test.csv\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the dataset\n",
        "file_path = 'DynamicallyHateDataset.csv'  # Update with your file's path\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Split data based on the 'split' column into train, dev, and test sets\n",
        "train_data = data[data['split'] == 'train']\n",
        "dev_data = data[data['split'] == 'dev']\n",
        "test_data = data[data['split'] == 'test']\n",
        "\n",
        "# Save each set into separate CSV files\n",
        "train_file_path = 'train.csv'\n",
        "dev_file_path = 'dev.csv'\n",
        "test_file_path = 'test.csv'\n",
        "\n",
        "train_data.to_csv(train_file_path, index=False)\n",
        "dev_data.to_csv(dev_file_path, index=False)\n",
        "test_data.to_csv(test_file_path, index=False)\n",
        "\n",
        "print(f\"Train set saved to {train_file_path}\")\n",
        "print(f\"Dev set saved to {dev_file_path}\")\n",
        "print(f\"Test set saved to {test_file_path}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "qtOdi2KQ5UWd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtOdi2KQ5UWd",
        "outputId": "87e98458-baad-4b8a-edd3-0e91a1aad983"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41144 entries, 0 to 41143\n",
            "Data columns (total 13 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   Unnamed: 0      41144 non-null  int64 \n",
            " 1   acl.id          41144 non-null  object\n",
            " 2   X1              41144 non-null  int64 \n",
            " 3   text            41144 non-null  object\n",
            " 4   label           41144 non-null  object\n",
            " 5   type            41144 non-null  object\n",
            " 6   target          41136 non-null  object\n",
            " 7   level           41144 non-null  object\n",
            " 8   split           41144 non-null  object\n",
            " 9   round.base      41144 non-null  int64 \n",
            " 10  annotator       41144 non-null  object\n",
            " 11  round           41144 non-null  object\n",
            " 12  acl.id.matched  30098 non-null  object\n",
            "dtypes: int64(3), object(10)\n",
            "memory usage: 4.1+ MB\n",
            "None\n",
            "          Unnamed: 0            X1    round.base\n",
            "count   41144.000000  41144.000000  41144.000000\n",
            "mean    20703.991056  20619.562269      2.466848\n",
            "std     12580.884040  11896.799460      1.131089\n",
            "min         1.000000      1.000000      1.000000\n",
            "25%     10295.750000  10317.750000      1.000000\n",
            "50%     20593.500000  20612.500000      2.000000\n",
            "75%     30894.250000  30910.250000      3.000000\n",
            "max    331000.000000  41255.000000      4.000000\n"
          ]
        }
      ],
      "source": [
        "print(data.info())\n",
        "print(data.describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "vwygTUETVYst",
      "metadata": {
        "id": "vwygTUETVYst"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "eP2izyWxewfU",
      "metadata": {
        "id": "eP2izyWxewfU"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4664995626ccf6f7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "4664995626ccf6f7",
        "outputId": "6630c1be-65f2-4d8b-9e32-3ea6633e912c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0    acl.id     X1  \\\n",
              "0               1      acl1      1   \n",
              "1               2     acl10      2   \n",
              "2               3    acl100      3   \n",
              "3               4   acl1000      4   \n",
              "14             15   acl1001     15   \n",
              "...           ...       ...    ...   \n",
              "41129      107100   acl8894  40028   \n",
              "41140      118100  acl11574   1752   \n",
              "41141      119100  acl13446   3832   \n",
              "41142        1205  acl13457   3844   \n",
              "41143      121100  acl32095  24553   \n",
              "\n",
              "                                                    text    label  \\\n",
              "0                                    dalits are lowlives     hate   \n",
              "1                 gay people are a burden to our society     hate   \n",
              "2                                  Arabs are not welcome     hate   \n",
              "3      I'm not saying we should actually eliminate he...     hate   \n",
              "14     It's such a rape culture with these immigrants...     hate   \n",
              "...                                                  ...      ...   \n",
              "41129                                Women are deceitful     hate   \n",
              "41140  Women don't want equality, they just want to b...     hate   \n",
              "41141                                         fuck covid  nothate   \n",
              "41142                           This computer is garbage  nothate   \n",
              "41143                 The only good Muslim is a dead one     hate   \n",
              "\n",
              "              type    target     level  split  round.base   annotator round  \\\n",
              "0         notgiven  notgiven  original  train           1  GNZuCtwed3     1   \n",
              "1         notgiven  notgiven  original  train           1  GNZuCtwed3     1   \n",
              "2         notgiven  notgiven  original  train           1  vDe7GN0NrL     1   \n",
              "3         notgiven  notgiven  original  train           1  oemYWm1Tjg     1   \n",
              "14        notgiven  notgiven  original  train           1  CAgNlUizNm     1   \n",
              "...            ...       ...       ...    ...         ...         ...   ...   \n",
              "41129     notgiven  notgiven  original  train           1  CAgNlUizNm     1   \n",
              "41140   derogation       wom  original  train           2  CAgNlUizNm    2a   \n",
              "41141         none      none  original  train           2  CAgNlUizNm    2a   \n",
              "41142         none      none  original  train           2  eLGzdD8Tvb    2a   \n",
              "41143  threatening       mus  original  train           4  IBsVsBliwX    4a   \n",
              "\n",
              "      acl.id.matched  \n",
              "0                NaN  \n",
              "1                NaN  \n",
              "2                NaN  \n",
              "3                NaN  \n",
              "14               NaN  \n",
              "...              ...  \n",
              "41129            NaN  \n",
              "41140       acl11575  \n",
              "41141       acl13447  \n",
              "41142       acl13458  \n",
              "41143       acl32096  \n",
              "\n",
              "[32924 rows x 13 columns]"
            ],
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              "  <div id=\"df-3b3e656b-df08-43d4-941c-9212acdaea31\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>Unnamed: 0</th>\n",
              "      <th>acl.id</th>\n",
              "      <th>X1</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>type</th>\n",
              "      <th>target</th>\n",
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              "      <th>annotator</th>\n",
              "      <th>round</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>acl1</td>\n",
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              "      <td>dalits are lowlives</td>\n",
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              "      <th>1</th>\n",
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              "      <td>acl10</td>\n",
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              "      <td>gay people are a burden to our society</td>\n",
              "      <td>hate</td>\n",
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              "      <td>1</td>\n",
              "      <td>GNZuCtwed3</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>acl100</td>\n",
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              "      <td>Arabs are not welcome</td>\n",
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              "      <td>vDe7GN0NrL</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>acl1001</td>\n",
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              "      <td>It's such a rape culture with these immigrants...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>1205</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>32924 rows × 13 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "        const element = document.querySelector('#df-3b3e656b-df08-43d4-941c-9212acdaea31');\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",
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              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "      try {\n",
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              "            'suggestCharts', [key], {});\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
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              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train_data');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_data",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 32924,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12373,\n        \"min\": 1,\n        \"max\": 331000,\n        \"num_unique_values\": 32924,\n        \"samples\": [\n          15967,\n          14376,\n          21117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acl.id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 32924,\n        \"samples\": [\n          \"acl24368\",\n          \"acl22936\",\n          \"acl29002\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11578,\n        \"min\": 1,\n        \"max\": 41245,\n        \"num_unique_values\": 32924,\n        \"samples\": [\n          15967,\n          14376,\n          21117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 32916,\n        \"samples\": [\n          \"We're a small island, we can't support all these Foreigners\",\n          \"This pencil is fucking useless\",\n          \"Men who talk shit about women who have onlyfans are usually ugly and broke af\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"nothate\",\n          \"hate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"notgiven\",\n          \"none\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 360,\n        \"samples\": [\n          \"bla, asi.south\",\n          \"bla, asi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"perturbation\",\n          \"original\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"round.base\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"annotator\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"GNZuCtwed3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"round\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acl.id.matched\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24080,\n        \"samples\": [\n          \"acl40059\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c15c15f50ddddbf8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "id": "c15c15f50ddddbf8",
        "outputId": "7179d95e-0f29-4a06-816a-59334a3b888a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0    acl.id    X1  \\\n",
              "49             51  acl10042    51   \n",
              "50             52  acl10043    52   \n",
              "51             53  acl10044    53   \n",
              "52             54  acl10045    54   \n",
              "53             55  acl10046    55   \n",
              "...           ...       ...   ...   \n",
              "41025        3125  acl10762   850   \n",
              "41026         430  acl10793   884   \n",
              "41027         533  acl11081  1205   \n",
              "41028        6991  acl11100  1227   \n",
              "41029         711  acl11119  1247   \n",
              "\n",
              "                                                    text    label      type  \\\n",
              "49     don't get me wrong I don't hate Indians, but I...     hate  notgiven   \n",
              "50     I can't tell the difference between a letterbo...     hate  notgiven   \n",
              "51                             brown people are the shit  nothate      none   \n",
              "52                                      The NHS is waste  nothate      none   \n",
              "53     I\u0019ll punch anyone that says Indians are filthy...  nothate      none   \n",
              "...                                                  ...      ...       ...   \n",
              "41025           Millenials were born in a comatose state     hate  notgiven   \n",
              "41026                              All Zionists are lazy     hate  notgiven   \n",
              "41027       White purity is under attack from dark skins     hate  notgiven   \n",
              "41028                           Blacks are such pondlife     hate  notgiven   \n",
              "41029  It is totally valid to say that black people a...     hate  notgiven   \n",
              "\n",
              "         target     level split  round.base   annotator round acl.id.matched  \n",
              "49     notgiven  original   dev           1  QiOKkCi7F8     1            NaN  \n",
              "50     notgiven  original   dev           1  TrRF46JWfP     1            NaN  \n",
              "51         none  original   dev           1  GNZuCtwed3     1            NaN  \n",
              "52         none  original   dev           1  TrRF46JWfP     1            NaN  \n",
              "53         none  original   dev           1  oemYWm1Tjg     1            NaN  \n",
              "...         ...       ...   ...         ...         ...   ...            ...  \n",
              "41025  notgiven  original   dev           1  oemYWm1Tjg     1            NaN  \n",
              "41026  notgiven  original   dev           1  TrRF46JWfP     1            NaN  \n",
              "41027  notgiven  original   dev           1  CAgNlUizNm     1            NaN  \n",
              "41028  notgiven  original   dev           1  GNZuCtwed3     1            NaN  \n",
              "41029  notgiven  original   dev           1  CAgNlUizNm     1            NaN  \n",
              "\n",
              "[4100 rows x 13 columns]"
            ],
            "text/html": [
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              "</table>\n",
              "<p>4100 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-507be212-2b00-4e66-8183-346f08d36aa6')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-507be212-2b00-4e66-8183-346f08d36aa6 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-507be212-2b00-4e66-8183-346f08d36aa6');\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",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1eeeecea-8829-47b8-9d4c-2faa214bbb24 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8eafbbd2-6742-4615-b110-a65fb3410930\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dev_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8eafbbd2-6742-4615-b110-a65fb3410930 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dev_data');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dev_data",
              "summary": "{\n  \"name\": \"dev_data\",\n  \"rows\": 4100,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12607,\n        \"min\": 51,\n        \"max\": 34731,\n        \"num_unique_values\": 4100,\n        \"samples\": [\n          23165,\n          1065,\n          22929\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acl.id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4100,\n        \"samples\": [\n          \"acl30846\",\n          \"acl10956\",\n          \"acl30633\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"X1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12609,\n        \"min\": 51,\n        \"max\": 34731,\n        \"num_unique_values\": 4100,\n        \"samples\": [\n          23165,\n          1065,\n          22929\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4100,\n        \"samples\": [\n          \"The \\\"perfect\\\" woman doesn't exist. let me you that. We are not objects moldable to your desires. Every single girl has her own personality. But we get attention from pervs as soon as we grow a pair of tits, and that continues to be the case until our late 20s to early 30s when men think we're expired meat. Here's a major mindset shift for you guys: what would think we rather  do, put all our attention on looks, invest a ton of time, money and energy,  and possibly still not get anywhere with you\\\"\\u2248\\u00b0 or give less attention, and put our values on something better?\",\n          \"Mental how the North get shafted by then horribly slimy Tory cunts every time and people still vote the pricks in\",\n          \"Why does so much trans activism focus on the useless pronoun usage and language like he and she and they , and not pushing for OTC Hormones?  It is because most activism is pushed by cis people who really understand that talking about a medical side of being transgender doesn't make sense as neither do aspiring transgenders\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"nothate\",\n          \"hate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"notgiven\",\n          \"none\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 124,\n        \"samples\": [\n          \"gay.man\",\n          \"notargetrecorded\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"perturbation\",\n          \"original\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"split\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"dev\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"round.base\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"annotator\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"QiOKkCi7F8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"round\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acl.id.matched\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3009,\n        \"samples\": [\n          \"acl12794\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dev_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8d69aa426ad2f748",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8d69aa426ad2f748",
        "outputId": "2d69e3cc-7b5f-4003-e727-4dd3c6f7abc5"
      },
      "outputs": [
        {
          "data": {
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              "      <th>annotator</th>\n",
              "      <th>round</th>\n",
              "      <th>acl.id.matched</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>acl10000</td>\n",
              "      <td>5</td>\n",
              "      <td>bananas are for black people</td>\n",
              "      <td>hate</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>QiOKkCi7F8</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>acl10001</td>\n",
              "      <td>6</td>\n",
              "      <td>asian people are the shit</td>\n",
              "      <td>nothate</td>\n",
              "      <td>none</td>\n",
              "      <td>none</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>GNZuCtwed3</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>acl10002</td>\n",
              "      <td>7</td>\n",
              "      <td>women can not reproduce kids but men can in al...</td>\n",
              "      <td>hate</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>vDe7GN0NrL</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>acl10003</td>\n",
              "      <td>8</td>\n",
              "      <td>i hate wearing black in the summer!</td>\n",
              "      <td>nothate</td>\n",
              "      <td>none</td>\n",
              "      <td>none</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>LqLKTtrOmx</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>acl10004</td>\n",
              "      <td>9</td>\n",
              "      <td>Who cares what Chinese people think?</td>\n",
              "      <td>hate</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>CAgNlUizNm</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41135</th>\n",
              "      <td>113100</td>\n",
              "      <td>acl9721</td>\n",
              "      <td>40948</td>\n",
              "      <td>I endorse the genocide of Muslims</td>\n",
              "      <td>hate</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>CAgNlUizNm</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41136</th>\n",
              "      <td>11414</td>\n",
              "      <td>acl9781</td>\n",
              "      <td>41014</td>\n",
              "      <td>Mandatory masks should be for mozzies only</td>\n",
              "      <td>hate</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>LqLKTtrOmx</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41137</th>\n",
              "      <td>115100</td>\n",
              "      <td>acl9790</td>\n",
              "      <td>41024</td>\n",
              "      <td>Women are dishonest</td>\n",
              "      <td>hate</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>CAgNlUizNm</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41138</th>\n",
              "      <td>116100</td>\n",
              "      <td>acl9867</td>\n",
              "      <td>41109</td>\n",
              "      <td>We must not fail to keep in mind that not all ...</td>\n",
              "      <td>nothate</td>\n",
              "      <td>none</td>\n",
              "      <td>none</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>CAgNlUizNm</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41139</th>\n",
              "      <td>117100</td>\n",
              "      <td>acl9974</td>\n",
              "      <td>41228</td>\n",
              "      <td>Indian culture is so backwards</td>\n",
              "      <td>hate</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>notgiven</td>\n",
              "      <td>original</td>\n",
              "      <td>test</td>\n",
              "      <td>1</td>\n",
              "      <td>GNZuCtwed3</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4120 rows × 13 columns</p>\n",
              "</div>\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_aa110343-8ba5-4a1d-afbb-ff43a92d2e58\">\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('test_data');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       Unnamed: 0    acl.id     X1  \\\n",
              "4               5  acl10000      5   \n",
              "5               6  acl10001      6   \n",
              "6               7  acl10002      7   \n",
              "7               8  acl10003      8   \n",
              "8               9  acl10004      9   \n",
              "...           ...       ...    ...   \n",
              "41135      113100   acl9721  40948   \n",
              "41136       11414   acl9781  41014   \n",
              "41137      115100   acl9790  41024   \n",
              "41138      116100   acl9867  41109   \n",
              "41139      117100   acl9974  41228   \n",
              "\n",
              "                                                    text    label      type  \\\n",
              "4                           bananas are for black people     hate  notgiven   \n",
              "5                              asian people are the shit  nothate      none   \n",
              "6      women can not reproduce kids but men can in al...     hate  notgiven   \n",
              "7                    i hate wearing black in the summer!  nothate      none   \n",
              "8                   Who cares what Chinese people think?     hate  notgiven   \n",
              "...                                                  ...      ...       ...   \n",
              "41135                  I endorse the genocide of Muslims     hate  notgiven   \n",
              "41136         Mandatory masks should be for mozzies only     hate  notgiven   \n",
              "41137                                Women are dishonest     hate  notgiven   \n",
              "41138  We must not fail to keep in mind that not all ...  nothate      none   \n",
              "41139                     Indian culture is so backwards     hate  notgiven   \n",
              "\n",
              "         target     level split  round.base   annotator round acl.id.matched  \n",
              "4      notgiven  original  test           1  QiOKkCi7F8     1            NaN  \n",
              "5          none  original  test           1  GNZuCtwed3     1            NaN  \n",
              "6      notgiven  original  test           1  vDe7GN0NrL     1            NaN  \n",
              "7          none  original  test           1  LqLKTtrOmx     1            NaN  \n",
              "8      notgiven  original  test           1  CAgNlUizNm     1            NaN  \n",
              "...         ...       ...   ...         ...         ...   ...            ...  \n",
              "41135  notgiven  original  test           1  CAgNlUizNm     1            NaN  \n",
              "41136  notgiven  original  test           1  LqLKTtrOmx     1            NaN  \n",
              "41137  notgiven  original  test           1  CAgNlUizNm     1            NaN  \n",
              "41138      none  original  test           1  CAgNlUizNm     1            NaN  \n",
              "41139  notgiven  original  test           1  GNZuCtwed3     1            NaN  \n",
              "\n",
              "[4120 rows x 13 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99c971d403ccf334",
      "metadata": {
        "collapsed": false,
        "id": "99c971d403ccf334"
      },
      "source": [
        "1.2 data analyst\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "89a39bb631401116",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "89a39bb631401116",
        "outputId": "c432ffae-3d59-4424-f0b4-832ea9d3e81f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv('DynamicallyHateDataset.csv')\n",
        "\n",
        "# Count data by split and label\n",
        "count_data = data.groupby(['split', 'label']).size().reset_index(name='count')\n",
        "\n",
        "# Set up the plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(data=count_data, x='split', y='count', hue='label', palette='coolwarm')\n",
        "\n",
        "# Add labels and title\n",
        "plt.title('Distribution of Classes in the Dynamically Hate Dataset', fontsize=14)\n",
        "plt.xlabel('Split', fontsize=12)\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "plt.legend(title='Hate Speech Labels', labels=['Hate', 'Not Hate'], fontsize=10)\n",
        "\n",
        "# Show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a447ac16bad1726",
      "metadata": {
        "collapsed": false,
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      "source": [
        "2. Training\n",
        "In this section, we are going to adopt two pre-trained language models and fine-tune both on the Dynamically Generated Hate dataset (train and dev splits).\n",
        "\n",
        "We used the Transformers library provided by the Hugging Face with the following pre-trained models:\n",
        "\n",
        "bert-base-uncased: BERT is a transformers model pretrained on a large corpus of English data in a self-supervised style.\n",
        "distilbert-base-uncased: DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "V-L701I-s4wt",
      "metadata": {
        "colab": {
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        },
        "id": "V-L701I-s4wt",
        "outputId": "7475f5cd-fea8-42fb-c69b-2a37da41ae2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "python3: can't open file '/content/fine_tune_transformer.py': [Errno 2] No such file or directory\n"
          ]
        }
      ],
      "source": [
        "!python fine_tune_transformer.py distilbert-base-uncased\n"
      ]
    },
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      "cell_type": "code",
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      "source": [
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
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            "Some weights of BertForSequenceClassification 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",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "<ipython-input-9-ac3c6e69db96>:82: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
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              "      [   2/5000 : < :, Epoch 0.00/8]\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",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   2121\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2122\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2123\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   2124\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2479\u001b[0m                     )\n\u001b[1;32m   2480\u001b[0m                     \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2481\u001b[0;31m                         \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2483\u001b[0m                     if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   3610\u001b[0m                 \u001b[0mscaled_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3611\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3612\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3613\u001b[0m             \u001b[0;31m# Finally we need to normalize the loss for reporting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3614\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m   2239\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlomo_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2240\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2241\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2243\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_trigger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    579\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m             )\n\u001b[0;32m--> 581\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    582\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    345\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m     _engine_run_backward(\n\u001b[0m\u001b[1;32m    348\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py\u001b[0m in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0munregister_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_register_logging_hooks_on_whole_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m         return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    826\u001b[0m             \u001b[0mt_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m         )  # Calls into the C++ engine to run the backward pass\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# BERT\n",
        "# api: e643d3de736824607ddd82dd2117da5e63ef881e\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, EarlyStoppingCallback\n",
        "from datasets import Dataset\n",
        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
        "import pandas as pd\n",
        "\n",
        "# Step 1: Load and Prepare the Dataset\n",
        "train_data = pd.read_csv('train.csv')\n",
        "dev_data = pd.read_csv('dev.csv')\n",
        "\n",
        "train_texts = train_data['text'].tolist()\n",
        "train_labels = train_data['label'].apply(lambda x: 1 if x == 'hate' else 0).tolist()\n",
        "dev_texts = dev_data['text'].tolist()\n",
        "dev_labels = dev_data['label'].apply(lambda x: 1 if x == 'hate' else 0).tolist()\n",
        "\n",
        "train_dataset = Dataset.from_dict({'text': train_texts, 'label': train_labels})\n",
        "dev_dataset = Dataset.from_dict({'text': dev_texts, 'label': dev_labels})\n",
        "\n",
        "# Step 2: Tokenize the Dataset\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example['text'], padding=\"max_length\", truncation=True)\n",
        "\n",
        "tokenized_train = train_dataset.map(tokenize_function, batched=True)\n",
        "tokenized_dev = dev_dataset.map(tokenize_function, batched=True)\n",
        "\n",
        "# Step 3: Load the Pre-trained Model with Dropout Regularization\n",
        "bert_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    \"bert-base-uncased\",\n",
        "    num_labels=2,\n",
        "    hidden_dropout_prob=0.3  # Increased dropout to 30%\n",
        ")\n",
        "\n",
        "# Step 4: Define Training Arguments with Improvements\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    eval_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=3e-6,                   # Lower learning rate\n",
        "    per_device_train_batch_size=8,       # Smaller batch size\n",
        "    per_device_eval_batch_size=8,\n",
        "    num_train_epochs=8,                  # Reduced number of epochs\n",
        "    logging_dir=\"./logs\",\n",
        "    logging_steps=50,\n",
        "    save_total_limit=2,\n",
        "    fp16=True,\n",
        "    load_best_model_at_end=True,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    lr_scheduler_type=\"cosine\",          # Cosine decay scheduler\n",
        "    warmup_steps=200,                    # Reduced warmup steps\n",
        "    weight_decay=0.1,                    # Stronger weight decay\n",
        "    metric_for_best_model=\"eval_loss\",\n",
        "    greater_is_better=False,\n",
        "    max_grad_norm=0.5,                   # Gradient clipping\n",
        ")\n",
        "\n",
        "# Step 5: Reduce Dataset Size for Debugging (Optional)\n",
        "small_train_dataset = tokenized_train.shuffle(seed=42).select(range(5000))  # Use 5000 examples\n",
        "small_dev_dataset = tokenized_dev.shuffle(seed=42).select(range(1000))      # Use 1000 examples\n",
        "\n",
        "# Step 6: Define the compute_metrics Function\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"\n",
        "    Compute accuracy, precision, recall, and F1-score for evaluation.\n",
        "    \"\"\"\n",
        "    logits, labels = eval_pred\n",
        "    predictions = logits.argmax(axis=-1)  # Convert logits to class predictions\n",
        "    acc = accuracy_score(labels, predictions)  # Accuracy\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(\n",
        "        labels, predictions, average=\"weighted\"\n",
        "    )\n",
        "    return {\n",
        "        \"accuracy\": acc,\n",
        "        \"precision\": precision,\n",
        "        \"recall\": recall,\n",
        "        \"f1\": f1,\n",
        "    }\n",
        "\n",
        "# Step 7: Initialize the Trainer with Built-in EarlyStoppingCallback\n",
        "trainer = Trainer(\n",
        "    model=bert_model,\n",
        "    args=training_args,\n",
        "    train_dataset=small_train_dataset,\n",
        "    eval_dataset=small_dev_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,  # Add the compute_metrics function\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],  # Patience for stopping\n",
        ")\n",
        "\n",
        "# Step 8: Train the Model\n",
        "trainer.train()\n",
        "\n",
        "# Step 9: Evaluate the Model\n",
        "eval_results = trainer.evaluate()\n",
        "print(\"Evaluation Results:\", eval_results)\n",
        "\n",
        "# Step 10: Save the Model\n",
        "trainer.save_model(\"./bert-finetuned\")  # Save the fine-tuned model\n",
        "tokenizer.save_pretrained(\"./bert-finetuned\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "r8ML4ktKnWQy",
      "metadata": {
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      },
      "outputs": [
        {
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          "text": [
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "<ipython-input-6-e20aa886de8f>:83: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
            "  trainer = Trainer(\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3750' max='3750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3750/3750 08:29, Epoch 6/6]\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",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.641300</td>\n",
              "      <td>0.644724</td>\n",
              "      <td>0.623000</td>\n",
              "      <td>0.636606</td>\n",
              "      <td>0.623000</td>\n",
              "      <td>0.611218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.557400</td>\n",
              "      <td>0.584273</td>\n",
              "      <td>0.691000</td>\n",
              "      <td>0.713599</td>\n",
              "      <td>0.691000</td>\n",
              "      <td>0.681344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.492600</td>\n",
              "      <td>0.554155</td>\n",
              "      <td>0.718000</td>\n",
              "      <td>0.726311</td>\n",
              "      <td>0.718000</td>\n",
              "      <td>0.714743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>0.565668</td>\n",
              "      <td>0.719000</td>\n",
              "      <td>0.730797</td>\n",
              "      <td>0.719000</td>\n",
              "      <td>0.714618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.444200</td>\n",
              "      <td>0.566259</td>\n",
              "      <td>0.718000</td>\n",
              "      <td>0.729081</td>\n",
              "      <td>0.718000</td>\n",
              "      <td>0.713817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.423600</td>\n",
              "      <td>0.562676</td>\n",
              "      <td>0.723000</td>\n",
              "      <td>0.729948</td>\n",
              "      <td>0.723000</td>\n",
              "      <td>0.720324</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
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          "metadata": {}
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          "data": {
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              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='125' max='125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [125/125 00:04]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation Results: {'eval_loss': 0.554155170917511, 'eval_accuracy': 0.718, 'eval_precision': 0.726311011062624, 'eval_recall': 0.718, 'eval_f1': 0.7147428703429773, 'eval_runtime': 4.2594, 'eval_samples_per_second': 234.773, 'eval_steps_per_second': 29.347, 'epoch': 6.0}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./distilbert-finetuned/tokenizer_config.json',\n",
              " './distilbert-finetuned/special_tokens_map.json',\n",
              " './distilbert-finetuned/vocab.txt',\n",
              " './distilbert-finetuned/added_tokens.json',\n",
              " './distilbert-finetuned/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Install required libraries\n",
        "!pip install transformers datasets scikit-learn\n",
        "\n",
        "# Import necessary libraries\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, EarlyStoppingCallback\n",
        "from datasets import Dataset\n",
        "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
        "import pandas as pd\n",
        "\n",
        "# Step 1: Load and Prepare the Dataset\n",
        "train_data = pd.read_csv('train.csv')\n",
        "dev_data = pd.read_csv('dev.csv')\n",
        "\n",
        "train_texts = train_data['text'].tolist()\n",
        "train_labels = train_data['label'].apply(lambda x: 1 if x == 'hate' else 0).tolist()\n",
        "dev_texts = dev_data['text'].tolist()\n",
        "dev_labels = dev_data['label'].apply(lambda x: 1 if x == 'hate' else 0).tolist()\n",
        "\n",
        "train_dataset = Dataset.from_dict({'text': train_texts, 'label': train_labels})\n",
        "dev_dataset = Dataset.from_dict({'text': dev_texts, 'label': dev_labels})\n",
        "\n",
        "# Step 2: Tokenize the Dataset\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example['text'], padding=\"max_length\", truncation=True)\n",
        "\n",
        "tokenized_train = train_dataset.map(tokenize_function, batched=True)\n",
        "tokenized_dev = dev_dataset.map(tokenize_function, batched=True)\n",
        "\n",
        "# Step 3: Load the Pre-trained DistilBERT Model\n",
        "distilbert_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    \"distilbert-base-uncased\",\n",
        "    num_labels=2  # Binary classification\n",
        ")\n",
        "\n",
        "# Step 4: Define Training Arguments with Improvements\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    eval_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=3e-6,                   # Lower learning rate\n",
        "    per_device_train_batch_size=8,       # Smaller batch size\n",
        "    per_device_eval_batch_size=8,\n",
        "    num_train_epochs=6,                  # Reduced number of epochs\n",
        "    logging_dir=\"./logs\",\n",
        "    logging_steps=50,\n",
        "    save_total_limit=2,\n",
        "    fp16=True,\n",
        "    load_best_model_at_end=True,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    lr_scheduler_type=\"cosine\",          # Cosine decay scheduler\n",
        "    warmup_steps=200,                    # Reduced warmup steps\n",
        "    weight_decay=0.1,                    # Stronger weight decay\n",
        "    metric_for_best_model=\"eval_loss\",\n",
        "    greater_is_better=False,\n",
        "    max_grad_norm=0.5,                   # Gradient clipping\n",
        ")\n",
        "\n",
        "# Step 5: Reduce Dataset Size for Debugging (Optional)\n",
        "small_train_dataset = tokenized_train.shuffle(seed=42).select(range(5000))  # Use 5000 examples\n",
        "small_dev_dataset = tokenized_dev.shuffle(seed=42).select(range(1000))      # Use 1000 examples\n",
        "\n",
        "# Step 6: Define the compute_metrics Function\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"\n",
        "    Compute accuracy, precision, recall, and F1-score for evaluation.\n",
        "    \"\"\"\n",
        "    logits, labels = eval_pred\n",
        "    predictions = logits.argmax(axis=-1)  # Convert logits to class predictions\n",
        "    acc = accuracy_score(labels, predictions)  # Accuracy\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(\n",
        "        labels, predictions, average=\"weighted\"\n",
        "    )\n",
        "    return {\n",
        "        \"accuracy\": acc,\n",
        "        \"precision\": precision,\n",
        "        \"recall\": recall,\n",
        "        \"f1\": f1,\n",
        "    }\n",
        "\n",
        "# Step 7: Initialize the Trainer with Built-in EarlyStoppingCallback\n",
        "trainer = Trainer(\n",
        "    model=distilbert_model,\n",
        "    args=training_args,\n",
        "    train_dataset=small_train_dataset,\n",
        "    eval_dataset=small_dev_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,  # Add the compute_metrics function\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],  # Patience for stopping\n",
        ")\n",
        "\n",
        "# Step 8: Train the Model\n",
        "trainer.train()\n",
        "\n",
        "# Step 9: Evaluate the Model\n",
        "eval_results = trainer.evaluate()\n",
        "print(\"Evaluation Results:\", eval_results)\n",
        "\n",
        "# Step 10: Save the Model\n",
        "trainer.save_model(\"./distilbert-finetuned\")  # Save the fine-tuned model\n",
        "tokenizer.save_pretrained(\"./distilbert-finetuned\")\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "kbmY8Q894zPr",
      "metadata": {
        "id": "kbmY8Q894zPr"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "sPTPm96iyq3P",
      "metadata": {
        "id": "sPTPm96iyq3P"
      },
      "source": [
        "3. Prediction\n",
        "In this section, we are going to load the fine-tuned models and predict the probabilities of label 1 (Hate) and label 0 (Not-hate) for the HateCheck test cases.As a baseline, we adopted a Logistic Regression classifier."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# baseline prediction ：To provide a simple, interpretable, and fast model to compare against more complex models like BERT or DistilBERT.\n",
        "# Install required libraries\n",
        "!pip install scikit-learn pandas\n",
        "\n",
        "# Import necessary libraries\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "import pandas as pd\n",
        "import logging\n",
        "\n",
        "# Step 1: Load and Prepare the Dataset\n",
        "train_data = pd.read_csv('train.csv')\n",
        "dev_data = pd.read_csv('dev.csv')\n",
        "\n",
        "train_texts = train_data['text'].tolist()\n",
        "train_labels = train_data['label'].apply(lambda x: 1 if x == 'hate' else 0).tolist()\n",
        "dev_texts = dev_data['text'].tolist()\n",
        "dev_labels = dev_data['label'].apply(lambda x: 1 if x == 'hate' else 0).tolist()\n",
        "\n",
        "# Step 2: Define the Baseline Model Pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('vect', CountVectorizer()),         # Convert text to bag-of-words\n",
        "    ('tfidf', TfidfTransformer()),       # Convert bag-of-words to TF-IDF\n",
        "    ('lr', LogisticRegression(solver='saga', max_iter=1000))  # Logistic Regression with a saga solver\n",
        "])\n",
        "\n",
        "# Step 3: Train the Baseline Model\n",
        "logging.info(\"Training the baseline model...\")\n",
        "pipeline.fit(train_texts, train_labels)\n",
        "\n",
        "# Step 4: Evaluate the Baseline Model\n",
        "logging.info(\"Evaluating the baseline model...\")\n",
        "dev_predictions = pipeline.predict(dev_texts)\n",
        "accuracy = accuracy_score(dev_labels, dev_predictions)\n",
        "precision = precision_score(dev_labels, dev_predictions, average=\"weighted\")\n",
        "recall = recall_score(dev_labels, dev_predictions, average=\"weighted\")\n",
        "f1 = f1_score(dev_labels, dev_predictions, average=\"weighted\")\n",
        "\n",
        "print(\"Evaluation Results:\")\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1 Score: {f1:.4f}\")\n",
        "\n",
        "# Step 5: Save the Baseline Model\n",
        "import pickle\n",
        "with open('./baseline_model.pkl', 'wb') as f:\n",
        "    pickle.dump(pipeline, f)\n",
        "print(\"Baseline model saved to './baseline_model.pkl'\")\n",
        "\n",
        "# Step 6: Load and Predict with the Baseline Model\n",
        "def predict_baseline(sentence):\n",
        "    \"\"\"\n",
        "    Predict a single sentence using the saved baseline model.\n",
        "    Args:\n",
        "        sentence (str): Input sentence to classify.\n",
        "    Returns:\n",
        "        dict: Predicted probabilities and the predicted class.\n",
        "    \"\"\"\n",
        "    # Load the saved model\n",
        "    with open('./baseline_model.pkl', 'rb') as f:\n",
        "        model = pickle.load(f)\n",
        "\n",
        "    # Predict probabilities\n",
        "    probabilities = model.predict_proba([sentence])[0]\n",
        "    predicted_class = probabilities.argmax()  # Get the class with the highest probability\n",
        "\n",
        "    # Map the class to labels\n",
        "    class_map = {0: \"hate\", 1: \"not-hate\"}\n",
        "    return {\"probabilities\": probabilities, \"predicted_class\": class_map[predicted_class]}\n",
        "\n",
        "\n",
        "# Test the prediction\n",
        "sample_sentence = \"I hate everyone.\"  # Replace with your input sentence\n",
        "prediction = predict_baseline(sample_sentence)\n",
        "print(f\"Prediction for '{sample_sentence}': {prediction}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gi-_BgYLz-Z2",
        "outputId": "e395cb3e-b31f-44e0-a337-73a33c86e9bf"
      },
      "id": "Gi-_BgYLz-Z2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Evaluation Results:\n",
            "Accuracy: 0.6729\n",
            "Precision: 0.6725\n",
            "Recall: 0.6729\n",
            "F1 Score: 0.6719\n",
            "Baseline model saved to './baseline_model.pkl'\n",
            "Prediction for 'I hate everyone.': {'probabilities': array([0.89925677, 0.10074323]), 'predicted_class': 'hate'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tranasform predict\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "import torch\n",
        "\n",
        "class TransformerPredict:\n",
        "    def __init__(self, model_path):\n",
        "        self.tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "        self.model = AutoModelForSequenceClassification.from_pretrained(model_path)\n",
        "\n",
        "    def predict(self, sentence):\n",
        "        inputs = self.tokenizer(sentence, return_tensors=\"pt\", truncation=True, padding=True, max_length=512)\n",
        "        with torch.no_grad():\n",
        "            logits = self.model(**inputs).logits\n",
        "            probabilities = torch.softmax(logits, dim=-1).numpy()[0]\n",
        "        predicted_class = probabilities.argmax()\n",
        "        return probabilities, predicted_class\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "XtJxTkFXz-R3"
      },
      "id": "XtJxTkFXz-R3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# baseline predict\n",
        "import pickle\n",
        "\n",
        "class BaselinePredict:\n",
        "    def __init__(self, model_path):\n",
        "        with open(model_path, \"rb\") as f:\n",
        "            self._model = pickle.load(f)\n",
        "\n",
        "    def predict(self, sentence):\n",
        "        probabilities = self._model.predict_proba([sentence])[0]\n",
        "        predicted_class = probabilities.argmax()\n",
        "        return probabilities, predicted_class\n"
      ],
      "metadata": {
        "id": "JcbXEjwb8bWx"
      },
      "id": "JcbXEjwb8bWx",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import pandas for test data\n",
        "import pandas as pd\n",
        "\n",
        "# Load test data\n",
        "df_test = pd.read_csv(\"test.csv\")\n",
        "\n",
        "# Paths to saved models\n",
        "BERT_MODEL_PATH = \"./bert-finetuned\"\n",
        "DISTILBERT_MODEL_PATH = \"./distilbert-finetuned\"\n",
        "BASELINE_MODEL_PATH = \"./baseline_model.pkl\"\n",
        "\n",
        "# Load models\n",
        "bert_predictor = TransformerPredict(BERT_MODEL_PATH)\n",
        "distilbert_predictor = TransformerPredict(DISTILBERT_MODEL_PATH)\n",
        "baseline_predictor = BaselinePredict(BASELINE_MODEL_PATH)\n",
        "\n",
        "# Example usage\n",
        "sentence = \"I hate everyone.\"\n",
        "bert_probs, bert_pred = bert_predictor.predict(sentence)\n",
        "distilbert_probs, distilbert_pred = distilbert_predictor.predict(sentence)\n",
        "baseline_probs, baseline_pred = baseline_predictor.predict(sentence)\n",
        "\n",
        "# Print results\n",
        "print(f\"BERT Prediction: {bert_probs}, Class: {bert_pred}\")\n",
        "print(f\"DistilBERT Prediction: {distilbert_probs}, Class: {distilbert_pred}\")\n",
        "print(f\"Baseline Prediction: {baseline_probs}, Class: {baseline_pred}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "40WnajUu5gNb",
        "outputId": "02a9ed26-3523-4074-957c-4392c70bd39a"
      },
      "id": "40WnajUu5gNb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "Incorrect path_or_model_id: './bert-finetuned'. Please provide either the path to a local folder or the repo_id of a model on the Hub.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    402\u001b[0m         \u001b[0;31m# Load from URL or cache if already cached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m         resolved_file = hf_hub_download(\n\u001b[0m\u001b[1;32m    404\u001b[0m             \u001b[0mpath_or_repo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marg_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"repo_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m                 \u001b[0mvalidate_repo_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mREPO_ID_REGEX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         raise HFValidationError(\n\u001b[0m\u001b[1;32m    161\u001b[0m             \u001b[0;34m\"Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHFValidationError\u001b[0m: Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: './bert-finetuned'.",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-a0890db45dc7>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Load models\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mbert_predictor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTransformerPredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBERT_MODEL_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mdistilbert_predictor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTransformerPredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDISTILBERT_MODEL_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mbaseline_predictor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBaselinePredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBASELINE_MODEL_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-11-a71d1d250326>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_path)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mTransformerPredict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModelForSequenceClassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m         \u001b[0;31m# Next, let's try to use the tokenizer_config file to get the tokenizer class.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 857\u001b[0;31m         \u001b[0mtokenizer_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_tokenizer_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    858\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"_commit_hash\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtokenizer_config\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    859\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_commit_hash\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_commit_hash\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py\u001b[0m in \u001b[0;36mget_tokenizer_config\u001b[0;34m(pretrained_model_name_or_path, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, **kwargs)\u001b[0m\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m     \u001b[0mcommit_hash\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"_commit_hash\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 689\u001b[0;31m     resolved_config_file = cached_file(\n\u001b[0m\u001b[1;32m    690\u001b[0m         \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m         \u001b[0mTOKENIZER_CONFIG_FILE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    467\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"There was a specific connection error when trying to load {path_or_repo_id}:\\n{err}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHFValidationError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m         raise EnvironmentError(\n\u001b[0m\u001b[1;32m    470\u001b[0m             \u001b[0;34mf\"Incorrect path_or_model_id: '{path_or_repo_id}'. Please provide either the path to a local folder or the repo_id of a model on the Hub.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    471\u001b[0m         ) from e\n",
            "\u001b[0;31mOSError\u001b[0m: Incorrect path_or_model_id: './bert-finetuned'. Please provide either the path to a local folder or the repo_id of a model on the Hub."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming test_data is a list or DataFrame-like object\n",
        "data = []  # List to store results\n",
        "\n",
        "# Check the type of test_data\n",
        "print(type(test_data))  # Use this to debug if needed\n",
        "\n",
        "# Adjust iteration based on test_data type\n",
        "if isinstance(test_data, pd.DataFrame):\n",
        "    # Use .iterrows() for DataFrame\n",
        "    for _, row in test_data[:50].iterrows():\n",
        "        text = row[\"text\"]\n",
        "        label = row[\"label\"]\n",
        "\n",
        "        # Get predictions for each model\n",
        "        baseline_probs, baseline_pred = baseline_predictor.predict(text)\n",
        "        bert_probs, bert_pred = bert_predictor.predict(text)\n",
        "        distilbert_probs, distilbert_pred = distilbert_predictor.predict(text)\n",
        "\n",
        "        # Append results for each model\n",
        "        data.append({\"text\": text, \"label\": label, \"model\": \"baseline\", \"score_0\": baseline_probs})\n",
        "        data.append({\"text\": text, \"label\": label, \"model\": \"bert\", \"score_0\": bert_probs})\n",
        "        data.append({\"text\": text, \"label\": label, \"model\": \"distilbert\", \"score_0\": distilbert_probs})\n",
        "else:\n",
        "    # Assume test_data is a list of dictionaries\n",
        "    for row in test_data[:500]:\n",
        "        text = row[\"text\"]\n",
        "        label = row[\"label\"]\n",
        "\n",
        "        # Get predictions for each model\n",
        "        baseline_probs, baseline_pred = baseline_predictor.predict(text)\n",
        "        bert_probs, bert_pred = bert_predictor.predict(text)\n",
        "        distilbert_probs, distilbert_pred = distilbert_predictor.predict(text)\n",
        "\n",
        "        # Append results for each model\n",
        "        data.append({\"text\": text, \"label\": label, \"model\": \"baseline\", \"score_0\": baseline_probs})\n",
        "        data.append({\"text\": text, \"label\": label, \"model\": \"bert\", \"score_0\": bert_probs})\n",
        "        data.append({\"text\": text, \"label\": label, \"model\": \"distilbert\", \"score_0\": distilbert_probs})\n",
        "\n",
        "# Convert list to DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Display the first few rows\n",
        "print(df.head(10))\n"
      ],
      "metadata": {
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        "id": "fcwpWX1j5gFb",
        "outputId": "e4e78fd0-4a8a-4744-a542-07049074edc9"
      },
      "id": "fcwpWX1j5gFb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'test_data' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-5c85b24ab26d>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Check the type of test_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Use this to debug if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Adjust iteration based on test_data type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'test_data' is not defined"
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      "metadata": {
        "id": "ays_HVxN5f55"
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      "id": "ays_HVxN5f55",
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      "id": "6WKm317Q5H80",
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        "\n"
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