Hate_Speech_Transformers.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U -q\n",
        "!pip install datasets -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpTHjZLKuX1a",
        "outputId": "f2523ee4-cfa8-4bb6-fc6d-c2992e1b8acd"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m55.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m27.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, accuracy_score, recall_score, f1_score, precision_score\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
      ],
      "metadata": {
        "id": "Ok2QfLyA-rmU"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nltk\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CakEeGod4ndI",
        "outputId": "14a30a88-026b-4d6f-ea4c-975cad333cf8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2024.5.15)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.4)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "metadata": {
        "id": "ATL4aOPQ4yAq"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert/distilbert-base-uncased\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"distilbert/distilbert-base-uncased\", num_labels = 3)"
      ],
      "metadata": {
        "id": "nIgrXejn-umJ",
        "colab": {
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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        {
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          "data": {
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              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_dataset(\"tdavidson/hate_speech_offensive\")\n",
        "data = data['train']\n",
        "data_df = data.to_pandas()"
      ],
      "metadata": {
        "id": "QiN1LjnilHfo"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the stop words\n",
        "stop_words = set(stopwords.words('english'))\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def preprocess_text(text):\n",
        "    # Lowercase the text\n",
        "    text = text.lower()\n",
        "    # Remove special characters and punctuation\n",
        "    text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
        "    # Tokenize the text\n",
        "    tokens = word_tokenize(text)\n",
        "    # Remove stop words and lemmatize the tokens\n",
        "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n",
        "    # Join the tokens back into a single string\n",
        "    text = ' '.join(tokens)\n",
        "    return text\n",
        "\n",
        "# Apply the preprocessing function to the 'text' column\n",
        "data_df['tweet'] = data_df['tweet'].apply(preprocess_text)"
      ],
      "metadata": {
        "id": "9htN1V863DtC"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "k1evyINj5BJl",
        "outputId": "4d54d00e-3a72-4b06-86e2-87b1fea0ea0b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count  hate_speech_count  offensive_language_count  neither_count  \\\n",
              "0          3                  0                         0              3   \n",
              "1          3                  0                         3              0   \n",
              "2          3                  0                         3              0   \n",
              "3          3                  0                         2              1   \n",
              "4          6                  0                         6              0   \n",
              "...      ...                ...                       ...            ...   \n",
              "24778      3                  0                         2              1   \n",
              "24779      3                  0                         1              2   \n",
              "24780      3                  0                         3              0   \n",
              "24781      6                  0                         6              0   \n",
              "24782      3                  0                         0              3   \n",
              "\n",
              "       class                                              tweet  \n",
              "0          2  rt mayasolovely woman shouldnt complain cleani...  \n",
              "1          1  rt mleew17 boy dat coldtyga dwn bad cuffin dat...  \n",
              "2          1  rt urkindofbrand dawg rt 80sbaby4life ever fuc...  \n",
              "3          1           rt cganderson vivabased look like tranny  \n",
              "4          1  rt shenikaroberts shit hear might true might f...  \n",
              "...      ...                                                ...  \n",
              "24778      1  yous muthafin lie 8220lifeasking 20pearls core...  \n",
              "24779      2  youve gone broke wrong heart baby drove rednec...  \n",
              "24780      1  young buck wan na eat dat nigguh like aint fuc...  \n",
              "24781      1                     youu got wild bitch tellin lie  \n",
              "24782      2  ruffled ntac eileen dahlia beautiful color com...  \n",
              "\n",
              "[24783 rows x 6 columns]"
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>hate_speech_count</th>\n",
              "      <th>offensive_language_count</th>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>24778</th>\n",
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              "      <td>1</td>\n",
              "      <td>yous muthafin lie 8220lifeasking 20pearls core...</td>\n",
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              "      <td>young buck wan na eat dat nigguh like aint fuc...</td>\n",
              "    </tr>\n",
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              "      <td>youu got wild bitch tellin lie</td>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ed0f88b-a7c0-4f56-8927-0d842c8024c0')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-2ed0f88b-a7c0-4f56-8927-0d842c8024c0 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-2ed0f88b-a7c0-4f56-8927-0d842c8024c0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7290f520-0474-4db7-8062-4d6c8ce90b74\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7290f520-0474-4db7-8062-4d6c8ce90b74')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    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",
              "  }\n",
              "\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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    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",
              "</style>\n",
              "\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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7290f520-0474-4db7-8062-4d6c8ce90b74 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7aec535e-0078-4580-9ee9-34cce00db209\">\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('data_df')\"\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_7aec535e-0078-4580-9ee9-34cce00db209 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_df",
              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 24783,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6,\n          7,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hate_speech_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"offensive_language_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"neither_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8,\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tweet\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24685,\n        \"samples\": [\n          \"pulling hoe aint something proud hoe go anybody 9995\",\n          \"rt yunheaux ppl uk go dumb hard curse like shitting cunt like dam cunt cant shit\",\n          \"seriously care color somebody make huge faggot book\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = list(data_df['tweet'])\n",
        "y = list(data_df['class'])"
      ],
      "metadata": {
        "id": "JnWM63bilg1H"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_val, y_train, y_val = train_test_split(x,y, test_size = 0.3)"
      ],
      "metadata": {
        "id": "gPNX_g6Ulqy9"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_tokenized = tokenizer(x_train, padding = True, truncation = True, max_length = 512 )"
      ],
      "metadata": {
        "id": "HlF3fLSsmLxR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_val_tokenized = tokenizer(x_val, padding = True, truncation = True, max_length = 512 )"
      ],
      "metadata": {
        "id": "nJuWOs8MmhoI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create torch dataset\n",
        "class Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels=None):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        if self.labels:\n",
        "            item[\"labels\"] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.encodings[\"input_ids\"])\n"
      ],
      "metadata": {
        "id": "jDfTKrLWm_9B"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = Dataset(x_train_tokenized, y_train)\n",
        "val_dataset = Dataset(x_val_tokenized, y_val)"
      ],
      "metadata": {
        "id": "OZ65h5kjolQU"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(p):\n",
        "    print(type(p))\n",
        "    pred, labels = p\n",
        "    pred = np.argmax(pred, axis=1)\n",
        "\n",
        "    clf_rep = classification_report(y_true=labels, y_pred=pred)\n",
        "\n",
        "    return {\"classification_report\": clf_rep}"
      ],
      "metadata": {
        "id": "22EYK5lqogEG"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Trainer\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"output\",\n",
        "    num_train_epochs=2,\n",
        "    per_device_train_batch_size=32\n",
        "\n",
        ")\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "C1UrC17YoyrE"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "vL9qA-Djo1kJ",
        "outputId": "ad231a49-ae4c-405c-cf7f-fe8dc2d4068d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1086' max='1086' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1086/1086 08:00, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.313000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.214500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1086, training_loss=0.2614688592282009, metrics={'train_runtime': 482.1204, 'train_samples_per_second': 71.965, 'train_steps_per_second': 2.253, 'total_flos': 1445280280052112.0, 'train_loss': 0.2614688592282009, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "grI6TufewhN_",
        "outputId": "6710f129-82c6-413b-e08e-66bdd8223ae8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='930' max='930' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [930/930 00:34]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Trainer is attempting to log a value of \"              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.55      0.32      0.40       435\n",
            "           1       0.93      0.96      0.95      5769\n",
            "           2       0.88      0.90      0.89      1231\n",
            "\n",
            "    accuracy                           0.91      7435\n",
            "   macro avg       0.79      0.73      0.75      7435\n",
            "weighted avg       0.90      0.91      0.91      7435\n",
            "\" of type <class 'str'> for key \"eval/classification_report\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'transformers.trainer_utils.EvalPrediction'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.2521468698978424,\n",
              " 'eval_classification_report': '              precision    recall  f1-score   support\\n\\n           0       0.55      0.32      0.40       435\\n           1       0.93      0.96      0.95      5769\\n           2       0.88      0.90      0.89      1231\\n\\n    accuracy                           0.91      7435\\n   macro avg       0.79      0.73      0.75      7435\\nweighted avg       0.90      0.91      0.91      7435\\n',\n",
              " 'eval_runtime': 35.0835,\n",
              " 'eval_samples_per_second': 211.923,\n",
              " 'eval_steps_per_second': 26.508,\n",
              " 'epoch': 2.0}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Metrics for Bert**\n",
        "Trainer is attempting to log a value of \"             \n",
        "                precision   recall  f1-score   support\n",
        "\n",
        "           0       0.47      0.20      0.29       420\n",
        "           1       0.93      0.97      0.95      5732\n",
        "           2       0.88      0.90      0.89      1283\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   macro avg       0.76      0.69      0.71      7435\n",
        "weighted avg       0.90      0.91      0.90      7435"
      ],
      "metadata": {
        "id": "khaYrSXNZMPf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Metrics for roBERTa**\n",
        "\n",
        "rainer is attempting to log a value of \"              \n",
        "    precision    recall  f1-score   support\n",
        "\n",
        "           0       0.45      0.33      0.38       403\n",
        "           1       0.94      0.95      0.95      5776\n",
        "           2       0.87      0.89      0.88      1256\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   \n",
        "   macro avg       0.75      0.72      0.74      7435\n",
        "\n",
        "weighted avg       0.90      0.91      0.90      7435"
      ],
      "metadata": {
        "id": "92xMstslUczW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Metrics for Albert**\n",
        "\n",
        "                precision    recall  f1-score   support\n",
        "\n",
        "           0       0.48      0.12      0.19       427\n",
        "           1       0.93      0.97      0.95      5714\n",
        "           2       0.86      0.90      0.88      1294\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   macro avg       0.76      0.66      0.67      7435\n",
        "weighted avg       0.89      0.91      0.89      7435"
      ],
      "metadata": {
        "id": "FKHRR4hRa-7Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Metrics for distill-BERT**\n",
        "precision    recall  f1-score   support\n",
        "\n",
        "           0       0.55      0.32      0.40       435\n",
        "           1       0.93      0.96      0.95      5769\n",
        "           2       0.88      0.90      0.89      1231\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   macro avg       0.79      0.73      0.75      7435\n",
        "weighted avg       0.90      0.91      0.91      7435\n"
      ],
      "metadata": {
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      "cell_type": "code",
      "source": [
        "#google/bert-uncased\n",
        "'''\n",
        "Trainer is attempting to log a value of \"\n",
        "                 precision  recall   f1-score   support\n",
        "\n",
        "           0       0.47      0.20      0.29       420\n",
        "           1       0.93      0.97      0.95      5732\n",
        "           2       0.88      0.90      0.89      1283\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   macro avg       0.76      0.69      0.71      7435\n",
        "weighted avg       0.90      0.91      0.90      7435\n",
        "\n",
        "train_loss: 0.2644719357429069\n",
        "eval_loss: 0.24802963435649872\n",
        "'''\n",
        "\n",
        "#facebookAI/roBERTa\n",
        "'''\n",
        "Trainer is attempting to log a value of \"\n",
        "                precision  recall    f1-score   support\n",
        "\n",
        "           0       0.45      0.33      0.38       403\n",
        "           1       0.94      0.95      0.95      5776\n",
        "           2       0.87      0.89      0.88      1256\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   macro avg       0.75      0.72      0.74      7435\n",
        "weighted avg       0.90      0.91      0.90      7435\n",
        "train_loss: 0.2864733181386143\n",
        "eval_loss: 0.2618129253387451\n",
        "'''\n",
        "\n",
        "#albert/albert-base-v2\n",
        "'''\n",
        "                 precision  recall   f1-score   support\n",
        "\n",
        "           0       0.48      0.12      0.19       427\n",
        "           1       0.93      0.97      0.95      5714\n",
        "           2       0.86      0.90      0.88      1294\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   macro avg       0.76      0.66      0.67      7435\n",
        "weighted avg       0.89      0.91      0.89      7435\n",
        "\n",
        "\n",
        "\n",
        "train_loss: 0.31809741664767044\n",
        "eval_loss: 0.2619057595729828\n",
        "'''\n",
        "\n",
        "#distilbert/distilbert-base-uncased\n",
        "'''\n",
        "                precision   recall   f1-score   support\n",
        "\n",
        "           0       0.55      0.32      0.40       435\n",
        "           1       0.93      0.96      0.95      5769\n",
        "           2       0.88      0.90      0.89      1231\n",
        "\n",
        "    accuracy                           0.91      7435\n",
        "   macro avg       0.79      0.73      0.75      7435\n",
        "weighted avg       0.90      0.91      0.91      7435\n",
        "\n",
        "train_loss: 0.2614688592282009\n",
        "eval_loss:  0.2521468698978424\n",
        "'''\n"
      ],
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        "id": "aPTfC8jdJLKO"
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