DistilBERT.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SatyamRaj1/Bias-Detection/blob/main/DistilBERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "source": [
        "# Importing necessary libraries\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import torch"
      ],
      "metadata": {
        "id": "-v476Ipbjopj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "u-97x3C3kDDG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# os.chdir(\"/content/drive/MyDrive/Colab Notebooks/ToxicBias\")"
      ],
      "metadata": {
        "id": "EHMXig_zjXqB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Train = pd.read_csv(\"Train.csv\", usecols = ['text', 'bias', 'race', 'religion', 'lgbtq', 'gender', 'political'], index_col = False)\n",
        "Test = pd.read_csv(\"Test.csv\", usecols = ['text', 'bias', 'race', 'religion', 'lgbtq', 'gender', 'political'], index_col = False)\n",
        "Val = pd.read_csv(\"Val.csv\", usecols = ['text', 'bias', 'race', 'religion', 'lgbtq', 'gender', 'political'], index_col = False)\n"
      ],
      "metadata": {
        "id": "uPVets7skJV-"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "data = pd.concat([Train, Test, Val])"
      ],
      "metadata": {
        "id": "MDLJRymdl6ub"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "data.shape"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
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          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# def modifyDF(df):\n",
        "  # newCol = ['race', 'gender', 'religion', 'lgbtq', 'political']\n",
        "  # for col in newCol:\n",
        "  #   df[col] = df.iloc[:, 2].apply(lambda x: 1 if col in x else 0)\n",
        "data['bias'] = data['bias'].apply(lambda x: 0 if x==1 else 1)\n",
        "data.rename(columns = {'bias': 'non_bias', 'text':'comment_text'}, inplace = True)\n"
      ],
      "metadata": {
        "id": "wOZ4qg3qd1Ne"
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      "execution_count": null,
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      "source": [
        "# modifyDF(data)\n",
        "# modifyDF(Test)\n",
        "# modifyDF(Val)"
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        "id": "zuIqzm4Eecm1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data"
      ],
      "metadata": {
        "id": "aa9MKnTjdMUj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Total_of_Class = {}                   # Total labeled comments from each class\n",
        "for col in data.iloc[:,1:].columns:\n",
        "    Total_of_Class[col] = data[col].value_counts()[1]\n",
        "    print(f\"{col} : {Total_of_Class[col]}\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9SfCHLBgK_h",
        "outputId": "f90b3f41-9532-4ba8-817b-eca04eb188b0"
      },
      "execution_count": null,
      "outputs": [
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          "text": [
            "non_bias : 6087\n",
            "\n",
            "race : 2202\n",
            "\n",
            "religion : 1573\n",
            "\n",
            "gender : 358\n",
            "\n",
            "lgbtq : 576\n",
            "\n",
            "political : 218\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating new column named \"total_classes\" which will show total no. of classes comment belongs to\n",
        "\n",
        "data[\"total_classes\"] = data.iloc[:,1:6].apply(lambda x: sum(x), axis=1)    # if we take sum of all labels we can tell how many classes comment belong to\n",
        "data                                                                        # for ex. if comment belongs to \"race\" and \"gender\" then the sum will be 2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "1ryLNfvTgffP",
        "outputId": "63996e16-8986-412b-8bd9-d779077e0d01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                          comment_text  non_bias  race  \\\n",
              "0    We are still having their water put at risk in...         0     1   \n",
              "1    This whole tragedy is the face and body and ph...         0     0   \n",
              "2    Black on black killings.  Stop this madness ma...         0     1   \n",
              "3          The world would be at peace with out islam.         0     0   \n",
              "4    Another Muslim commits a terrorist act and dem...         0     0   \n",
              "..                                                 ...       ...   ...   \n",
              "827  According to evolutionary theory, all human be...         1     0   \n",
              "828  There are certainly some dangerous people with...         1     0   \n",
              "829  The Muslims have been part of this country sin...         1     0   \n",
              "830  Muslims live in many countries all over the wo...         1     0   \n",
              "831  British citizens have the right to be free in ...         1     0   \n",
              "\n",
              "     religion  gender  lgbtq  political  total_classes  \n",
              "0           0       1      0          0              2  \n",
              "1           1       0      0          0              1  \n",
              "2           0       0      0          0              1  \n",
              "3           1       0      0          0              1  \n",
              "4           1       0      0          0              1  \n",
              "..        ...     ...    ...        ...            ...  \n",
              "827         0       0      0          0              1  \n",
              "828         0       0      0          0              1  \n",
              "829         0       0      0          0              1  \n",
              "830         0       0      0          0              1  \n",
              "831         0       0      0          0              1  \n",
              "\n",
              "[10412 rows x 8 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>827</th>\n",
              "      <td>According to evolutionary theory, all human be...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>828</th>\n",
              "      <td>There are certainly some dangerous people with...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>The Muslims have been part of this country sin...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>830</th>\n",
              "      <td>Muslims live in many countries all over the wo...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>831</th>\n",
              "      <td>British citizens have the right to be free in ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10412 rows × 8 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-7d46cbd9-2651-4cde-87b2-b8c1a8966acd')\"\n",
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              "  <style>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\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",
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              "\n",
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              "        document.querySelector('#df-7d46cbd9-2651-4cde-87b2-b8c1a8966acd button.colab-df-convert');\n",
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              "        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",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "\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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      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",
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              "    (() => {\n",
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              "        document.querySelector('#df-d628941a-fc75-4406-b060-6f9cf39311ce button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_cd55aed2-a9d3-4698-9aff-afb3ee32f3fb\">\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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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\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')\"\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",
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              "    </button>\n",
              "    <script>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10412,\n  \"fields\": [\n    {\n      \"column\": \"comment_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10405,\n        \"samples\": [\n          \"Im not sure I see a pattern of racial discrimination in favor of black people in this country.  Do you have any studies that dont include cherrypicked items?\",\n          \"I've never heard of a woman being a 'bitch' for wanting a stable income. I think you are confusing the meaning of these words.\",\n          \"Because everywhere the Muslims go  the whining and complaining starts.  And the useful idiots keep on accommodating themlook at Sweden  All they need is to get one toe in the door.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non_bias\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"religion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lgbtq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"political\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_classes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "multi_label_total = {}\n",
        "for value in data[\"total_classes\"].unique():\n",
        "    multi_label_total[value] = data[\"total_classes\"].value_counts()[value]\n",
        "multi_label_total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fkjdUAKyh4eX",
        "outputId": "a2bce63a-1a86-448f-d0b3-162da5b38291"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{2: 434, 1: 9829, 0: 116, 3: 33}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# copying our dataset in new variable for further text preprocessing\n",
        "new_data = data.copy()"
      ],
      "metadata": {
        "id": "Tjylg0phiQvU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re"
      ],
      "metadata": {
        "id": "1BcD-4s4ibXM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating copy of data for data cleaning and preprocessing\n",
        "cleaned_data = new_data.copy()\n",
        "\n",
        "# Removing Hyperlinks from text\n",
        "\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"https?://\\S+|www\\.\\S+\",\"\",x) )\n",
        "\n",
        "print(data.loc[629,\"comment_text\"],\"\\n\\n\")\n",
        "print(\"++++ After Removing URLs ++++\\n\\n \")\n",
        "print(cleaned_data.loc[629,\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYXmo2qKiTBX",
        "outputId": "1acea97a-c133-4df7-9f5b-93a34c8cc010"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "629    the real victims are the women. Yes because th...\n",
            "629    You leftist rag doesnt deserve this good of a ...\n",
            "629    There is no point in insulting someone for bei...\n",
            "Name: comment_text, dtype: object \n",
            "\n",
            "\n",
            "++++ After Removing URLs ++++\n",
            "\n",
            " \n",
            "629    the real victims are the women. Yes because th...\n",
            "629    You leftist rag doesnt deserve this good of a ...\n",
            "629    There is no point in insulting someone for bei...\n",
            "Name: comment_text, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rmoving emojis from text\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(\"[\"\n",
        "                                                                                   u\"\\U0001F600-\\U0001F64F\"\n",
        "                                                                                   u\"\\U0001F300-\\U0001F5FF\"\n",
        "                                                                                   u\"\\U0001F680-\\U0001F6FF\"\n",
        "                                                                                   u\"\\U0001F1E0-\\U0001F1FF\"\n",
        "                                                                                   u\"\\U00002702-\\U000027B0\"\n",
        "                                                                                   u\"\\U000024C2-\\U0001F251\"\n",
        "                                                                                   \"]+\",\"\", x, flags=re.UNICODE))\n",
        "\n",
        "print(data.loc[728,\"comment_text\"],\"\\n\\n\")\n",
        "print(\"++++ After Removing Emojis ++++\\n\\n \")\n",
        "print(cleaned_data.loc[728,\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_LR20Cbiivy",
        "outputId": "0fe1b5a3-106a-4d8b-9dea-ee5dc7caea8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "728    LMAO Im sure christian nutjobs praying in isla...\n",
            "728    Islam is a religion of equality and human righ...\n",
            "728    The fact that a person may be disabled does no...\n",
            "Name: comment_text, dtype: object \n",
            "\n",
            "\n",
            "++++ After Removing Emojis ++++\n",
            "\n",
            " \n",
            "728    LMAO Im sure christian nutjobs praying in isla...\n",
            "728    Islam is a religion of equality and human righ...\n",
            "728    The fact that a person may be disabled does no...\n",
            "Name: comment_text, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing IP addresses from text\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\",\"\",x))\n",
        "\n",
        "print(data.loc[744,\"comment_text\"],\"\\n\\n\")\n",
        "print(\"++++ After Removing IP addresses ++++\\n\\n \")\n",
        "print(cleaned_data.loc[744,\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziAOZ0fGiuPS",
        "outputId": "ea083bf1-7bd7-40d3-f65d-8ec8a5e95fd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "744    Cubans have less class than Mexicans which mea...\n",
            "744    What? Who said that? Don't tell me \"Someone sa...\n",
            "744    The majority of Gypsy workers are proud and ha...\n",
            "Name: comment_text, dtype: object \n",
            "\n",
            "\n",
            "++++ After Removing IP addresses ++++\n",
            "\n",
            " \n",
            "744    Cubans have less class than Mexicans which mea...\n",
            "744    What? Who said that? Don't tell me \"Someone sa...\n",
            "744    The majority of Gypsy workers are proud and ha...\n",
            "Name: comment_text, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # matches of css style,class elements and attributes\n",
        "# idxs_css = []\n",
        "# for i in  range(len(cleaned_data)):\n",
        "#     if re.findall(r\"[{][|].+\\n\",cleaned_data.loc[i,\"comment_text\"]):\n",
        "#         idxs_css.append(i)\n",
        "# print(f\"length of css style elements containing comments : {len(idxs_css)}\")\n",
        "\n",
        "# # Comments Containing Css style, class and attributes\n",
        "# ct=0\n",
        "# for i in idxs_css:\n",
        "#     ct += 1\n",
        "#     if ct>5:\n",
        "#         break\n",
        "#     st = cleaned_data.loc[i,\"comment_text\"]\n",
        "#     print(f\"Comment No.{i} :\\n\\n{st}\")\n",
        "#     print(\"---------------------------------------------------------------------------------------------------------------\\n\\n\")"
      ],
      "metadata": {
        "id": "AX7L8TFoixie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# cleaned_data.loc[idxs_css,\"total_classes\"].value_counts()    # total css style labeled comments"
      ],
      "metadata": {
        "id": "Z8XtK7icjBI5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing html tags from text\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"<.*?>\",\"\",x))"
      ],
      "metadata": {
        "id": "5NceQqcHjKAG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# There are some comments which contain double quoted words like --> \"\"words\"\"  we will convert these to --> \"words\"\n",
        "\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\\"\\\"\", \"\\\"\",x))   # replacing \"\" with \"\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"^\\\"\", \"\",x))      # removing quotation from start and the end of the string\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\\"$\", \"\",x))\n",
        "\n",
        "print(data.loc[15,\"comment_text\"],\"\\n\\n\")\n",
        "print(\"++++ After Removing double quotation ++++\\n\\n \")\n",
        "print(cleaned_data.loc[15,\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gg9ashbwjLFl",
        "outputId": "0262924d-8117-41a1-99e5-993a5017ed0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15    PROGRESSIVES are a CLEAR and PRESENT DANGER to...\n",
            "15    Actually its a genderneutral practice.   Same ...\n",
            "15    A recruit dies after falling off a building be...\n",
            "Name: comment_text, dtype: object \n",
            "\n",
            "\n",
            "++++ After Removing double quotation ++++\n",
            "\n",
            " \n",
            "15    PROGRESSIVES are a CLEAR and PRESENT DANGER to...\n",
            "15    Actually its a genderneutral practice.   Same ...\n",
            "15    A recruit dies after falling off a building be...\n",
            "Name: comment_text, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing Punctuation / Special characters (;:'\".?@!%&*+) which appears more than twice in the text\n",
        "\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"[^a-zA-Z0-9\\s][^a-zA-Z0-9\\s]+\", \" \",x))\n",
        "\n",
        "print(data.loc[1871,\"comment_text\"],\"\\n\\n\")\n",
        "print(\"++++ After Removing consequent special chars ++++\\n\\n \")\n",
        "print(cleaned_data.loc[1871,\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D308RiopjTdh",
        "outputId": "5255c44e-2f78-4368-f52e-bdc2a161b52f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Im not gay  or black or of a religious minority or any other identifiable target group. But I do think that the union is trying to stir up trouble. \n",
            "\n",
            "\n",
            "++++ After Removing consequent special chars ++++\n",
            "\n",
            " \n",
            "Im not gay  or black or of a religious minority or any other identifiable target group. But I do think that the union is trying to stir up trouble.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing Special characters\n",
        "\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"[^a-zA-Z0-9\\s\\\"\\',:;?!.()]\", \" \",x))\n",
        "\n",
        "print(data.loc[126,\"comment_text\"],\"\\n\\n\")\n",
        "print(\"++++ After Removing special chars ++++\\n\\n \")\n",
        "print(cleaned_data.loc[126,\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPiwgxFrju2S",
        "outputId": "ae92a1f9-a564-4dd9-9fe1-e13286dc6c4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "126         Ban Muslim refugees and  I will vote for you\n",
            "126    Do Black Lives Matter when the killer is black...\n",
            "126    How is the Proud Boys belief that western whit...\n",
            "Name: comment_text, dtype: object \n",
            "\n",
            "\n",
            "++++ After Removing special chars ++++\n",
            "\n",
            " \n",
            "126         Ban Muslim refugees and  I will vote for you\n",
            "126    Do Black Lives Matter when the killer is black...\n",
            "126    How is the Proud Boys belief that western whit...\n",
            "Name: comment_text, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing extra spaces in text\n",
        "\n",
        "cleaned_data[\"comment_text\"] = cleaned_data[\"comment_text\"].map(lambda x: re.sub(r\"\\s\\s+\", \" \",x))\n",
        "print(cleaned_data.loc[126,\"comment_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHIBaEAIj13u",
        "outputId": "d820c984-2bf3-432b-9d09-89bcf54e3c20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "126          Ban Muslim refugees and I will vote for you\n",
            "126    Do Black Lives Matter when the killer is black...\n",
            "126    How is the Proud Boys belief that western whit...\n",
            "Name: comment_text, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_data.reset_index(inplace=True)\n",
        "cleaned_data.drop(columns=[\"index\"], inplace=True)\n",
        "Final_data = cleaned_data.copy()"
      ],
      "metadata": {
        "id": "9SbBEB_gj4tL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertTokenizer\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "# Using Pretrained DistilBertTokenizer\n",
        "tokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "# Creating Dataset class for Toxic comments and Labels\n",
        "class Toxic_Dataset(Dataset):\n",
        "    def __init__(self, Comments_, Labels_):\n",
        "        self.comments = Comments_.copy()\n",
        "        self.labels = Labels_.copy()\n",
        "\n",
        "        self.comments[\"comment_text\"] = self.comments[\"comment_text\"].map(lambda x: tokenizer(x, padding=\"max_length\", truncation=True, return_tensors=\"pt\"))\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        comment = self.comments.loc[idx,\"comment_text\"]\n",
        "        label = np.array(self.labels.loc[idx,:])\n",
        "\n",
        "        return comment, label"
      ],
      "metadata": {
        "id": "k-sl3lAhkY_1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "StexQnpsnfl9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_rest, X_test, Y_rest, Y_test = train_test_split(Final_data.iloc[:,0],Final_data.iloc[:,1:], test_size=0.1, stratify=Final_data.iloc[:,7])\n",
        "X_train, X_val, Y_train, Y_val = train_test_split(X_rest,Y_rest, test_size=0.1, stratify=Y_rest.iloc[:,-1])\n",
        "\n",
        "Y_test.drop(columns=[\"non_bias\",\"total_classes\"], inplace=True)\n",
        "Y_train.drop(columns=[\"non_bias\",\"total_classes\"], inplace=True)\n",
        "Y_val.drop(columns=[\"non_bias\",\"total_classes\"], inplace=True)"
      ],
      "metadata": {
        "id": "byKbe5ZenOGs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.reset_index()\n",
        "X_train.drop(columns=[\"index\"], inplace=True)\n",
        "Y_train = Y_train.reset_index()\n",
        "Y_train.drop(columns=[\"index\"], inplace=True)\n",
        "X_test = X_test.reset_index()\n",
        "X_test.drop(columns=[\"index\"], inplace=True)\n",
        "Y_test = Y_test.reset_index()\n",
        "Y_test.drop(columns=[\"index\"], inplace=True)\n",
        "X_val = X_val.reset_index()\n",
        "X_val.drop(columns=[\"index\"], inplace=True)\n",
        "Y_val = Y_val.reset_index()\n",
        "Y_val.drop(columns=[\"index\"], inplace=True)"
      ],
      "metadata": {
        "id": "THRPHO-qnaX2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, Y_train.shape, X_test.shape, Y_test.shape, X_val.shape, Y_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgSehl8Ft6rF",
        "outputId": "9f98789a-6267-43b5-ecf9-968be53c95f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8433, 1), (8433, 5), (1042, 1), (1042, 5), (937, 1), (937, 5))"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making Training, Testing and Validation of data using Dataset class\n",
        "Train_data = Toxic_Dataset(X_train, Y_train)\n",
        "Test_data = Toxic_Dataset(X_test, Y_test)\n",
        "Val_data = Toxic_Dataset(X_val, Y_val)\n",
        "\n",
        "# Making datasets into batches\n",
        "Train_Loader = DataLoader(Train_data, batch_size=32, shuffle=True)\n",
        "Test_Loader = DataLoader(Test_data, shuffle=True)        #batch_size=16,\n",
        "Val_Loader = DataLoader(Val_data, shuffle=True)          #batch_size=16,"
      ],
      "metadata": {
        "id": "WW3L69AYkgqf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# DistilBERT\n",
        "\n",
        "from transformers import DistilBertForSequenceClassification\n",
        "\n",
        "Distil_bert = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n",
        "\n",
        "Distil_bert.classifier = nn.Sequential(\n",
        "                    nn.Linear(768,5),\n",
        "                    nn.Sigmoid()\n",
        "                  )\n",
        "# print(Distil_bert)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106,
          "referenced_widgets": [
            "f22d6049d2b44ec5aba26be0cf6bf264",
            "ee23eb68e326463cba2353e85bc6f889",
            "27c1ed0c746448b1b7594634ac4206ac",
            "457d0b2b7b404335b901f09c1d34953c",
            "47768ed8faaf478e8b81ed5ace159f06",
            "e197f9866c8648e4b12b53467705772b",
            "24d234c4ec21428c87aabade9461f00c",
            "cea1ced317e6411e979f9cdc9cf7b053",
            "b95ff658ecb64788b373ba63f015b54f",
            "e713686c292843749be81a850a55c3bf",
            "afed72627e7145bf89ab882595d13d5b"
          ]
        },
        "id": "1rVq5B80kwnT",
        "outputId": "d3814288-60eb-4fca-bef0-a06283d487d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f22d6049d2b44ec5aba26be0cf6bf264"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.optim import Adam\n",
        "from tqdm import tqdm\n",
        "from torch.nn import BCELoss\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "\n",
        "def train_Model(model,Train_DL, Val_DL, learning_rate, epochs):\n",
        "\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "    Loss = BCELoss()\n",
        "    Optimizer = Adam(params=model.parameters(), lr=learning_rate)\n",
        "    scheduler = StepLR(Optimizer, step_size=212, gamma=0.1)\n",
        "\n",
        "    model.to(device)\n",
        "    model.train()\n",
        "\n",
        "    train_acc_epochs = []\n",
        "    train_loss_epochs = []\n",
        "    val_acc_epochs = []\n",
        "    val_loss_epochs = []\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        training_loss = {}\n",
        "        training_accuracy = {}\n",
        "        validation_loss = {}\n",
        "        validation_accuracy = {}\n",
        "        batch = 0\n",
        "\n",
        "        for comments, labels in tqdm(Train_DL):\n",
        "\n",
        "            labels = labels.to(device)\n",
        "            labels = labels.float()\n",
        "            masks = comments[\"attention_mask\"].squeeze(1).to(device)\n",
        "            input_ids = comments[\"input_ids\"].squeeze(1).to(device)\n",
        "\n",
        "            output = model(input_ids, masks)\n",
        "            loss = Loss(output.logits, labels)\n",
        "\n",
        "            Optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            Optimizer.step()\n",
        "            scheduler.step()\n",
        "\n",
        "\n",
        "            batch += 1\n",
        "            if batch%53 == 0:\n",
        "                with torch.no_grad():\n",
        "                    acc = []\n",
        "                    op = output.logits\n",
        "                    for lb in range(len(labels)):\n",
        "                        correct = 0\n",
        "                        for i in range(len(labels[lb])):\n",
        "                            res = 1 if op[lb,i]>0.5 else 0\n",
        "                            if res == labels[lb,i]:\n",
        "                                correct += 1\n",
        "                        acc.append(correct/len(labels[lb]))\n",
        "\n",
        "                    training_loss[batch] = loss.item()\n",
        "                    training_accuracy[batch] = sum(acc)/len(acc)\n",
        "                    print(f\"Epoch:{epoch+1} | batch no:{batch}/{len(Train_DL)} | Loss:{loss.item():.4f} | Accuracy:{sum(acc)/len(acc):.4f}\")\n",
        "\n",
        "                    # Testing model on validation Data\n",
        "                    accVal = []\n",
        "                    val_loss = 0\n",
        "                    for comments, labels in Val_DL:\n",
        "                        labels = labels.to(device)\n",
        "                        labels = labels.float()\n",
        "                        masks = comments[\"attention_mask\"].squeeze(1).to(device)\n",
        "                        input_ids = comments[\"input_ids\"].squeeze(1).to(device)\n",
        "\n",
        "                        output = model(input_ids, masks)\n",
        "                        loss = Loss(output.logits, labels)\n",
        "                        val_loss += loss.item()\n",
        "\n",
        "                        op = output.logits\n",
        "                        correct_val = 0\n",
        "                        for i in range(5):\n",
        "                            res = 1 if op[0,i]>0.5 else 0\n",
        "                            if res == labels[0,i]:\n",
        "                                correct_val += 1\n",
        "                        accVal.append(correct_val/5)\n",
        "\n",
        "                    validation_loss[batch] = val_loss/len(Val_DL)\n",
        "                    validation_accuracy[batch] = sum(accVal)/len(accVal)\n",
        "                    print(f\" Validation Loss:{val_loss/len(Val_DL):.4f} | Validation Accuracy:{sum(accVal)/len(accVal):.4f}\")\n",
        "\n",
        "        train_acc_epochs.append(training_accuracy)\n",
        "        train_loss_epochs.append(training_loss)\n",
        "        val_acc_epochs.append(validation_accuracy)\n",
        "        val_loss_epochs.append(validation_loss)\n",
        "\n",
        "    return train_acc_epochs, train_loss_epochs, val_acc_epochs, val_loss_epochs"
      ],
      "metadata": {
        "id": "7nTtC05Nk07G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training Model\n",
        "TA, TL, VA, VL = train_Model(Distil_bert, Train_Loader, Val_Loader, learning_rate=0.0003, epochs=2)"
      ],
      "metadata": {
        "id": "vu_zzgpMk2jy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c682ddf-a280-45de-b716-6a25553c175f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 20%|█▉        | 52/264 [01:08<04:49,  1.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:1 | batch no:53/264 | Loss:0.1740 | Accuracy:0.9500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 20%|██        | 53/264 [01:27<24:15,  6.90s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.2078 | Validation Accuracy:0.9279\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 40%|███▉      | 105/264 [02:36<03:35,  1.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:1 | batch no:106/264 | Loss:0.1919 | Accuracy:0.9438\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 40%|████      | 106/264 [02:56<17:56,  6.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.1636 | Validation Accuracy:0.9426\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 60%|█████▉    | 158/264 [04:05<02:23,  1.35s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:1 | batch no:159/264 | Loss:0.1619 | Accuracy:0.9563\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 159/264 [04:24<11:53,  6.79s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.1612 | Validation Accuracy:0.9298\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 80%|███████▉  | 211/264 [05:33<01:11,  1.36s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:1 | batch no:212/264 | Loss:0.1468 | Accuracy:0.9375\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 80%|████████  | 212/264 [05:53<05:52,  6.79s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.1461 | Validation Accuracy:0.9462\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "100%|██████████| 264/264 [07:02<00:00,  1.60s/it]\n",
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:2 | batch no:53/264 | Loss:0.1139 | Accuracy:0.9625\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "\r 20%|██        | 53/264 [01:29<23:50,  6.78s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.1318 | Validation Accuracy:0.9509\n"
          ]
        },
        {
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            " 40%|███▉      | 105/264 [02:38<03:35,  1.36s/it]"
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        {
          "output_type": "stream",
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            "Epoch:2 | batch no:106/264 | Loss:0.1328 | Accuracy:0.9563\n"
          ]
        },
        {
          "output_type": "stream",
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            "\r 40%|████      | 106/264 [02:57<17:57,  6.82s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.1309 | Validation Accuracy:0.9522\n"
          ]
        },
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          "output_type": "stream",
          "name": "stderr",
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            " 60%|█████▉    | 158/264 [04:06<02:23,  1.36s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:2 | batch no:159/264 | Loss:0.0760 | Accuracy:0.9688\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r 60%|██████    | 159/264 [04:26<11:56,  6.82s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.1287 | Validation Accuracy:0.9537\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            " 80%|███████▉  | 211/264 [05:35<01:11,  1.35s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:2 | batch no:212/264 | Loss:0.1235 | Accuracy:0.9563\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "\r 80%|████████  | 212/264 [05:55<05:53,  6.79s/it]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Validation Loss:0.1265 | Validation Accuracy:0.9526\n"
          ]
        },
        {
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            "100%|██████████| 264/264 [07:04<00:00,  1.61s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating model on Test data\n",
        "TrueP = [0]*6\n",
        "FalseP = [0]*6\n",
        "TrueN = [0]*6\n",
        "FalseN = [0]*6\n",
        "def Evaluate_Model(model,Test_DL):\n",
        "\n",
        "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "    Loss = BCELoss()\n",
        "    model.to(device)\n",
        "    model.eval()\n",
        "\n",
        "    accTest = []\n",
        "    Test_loss = 0\n",
        "\n",
        "    for comments, labels in Test_DL:\n",
        "        labels = labels.to(device)\n",
        "        labels = labels.float()\n",
        "        masks = comments[\"attention_mask\"].squeeze(1).to(device)\n",
        "        input_ids = comments[\"input_ids\"].squeeze(1).to(device)\n",
        "\n",
        "        output = model(input_ids, masks)\n",
        "        loss = Loss(output.logits, labels)\n",
        "        Test_loss += loss.item()\n",
        "\n",
        "        op = output.logits\n",
        "        correct_val = 0\n",
        "        pred_bias = orig_bias = 0\n",
        "        for i in range(5):\n",
        "            res = 1 if op[0,i]>0.5 else 0\n",
        "            if(res == 1):\n",
        "              pred_bias = 1\n",
        "            if(labels[0, i] == 1):\n",
        "              orig_bias = 1\n",
        "            if res == labels[0,i]:\n",
        "                correct_val += 1\n",
        "            if res == 1 and labels[0,i] == 1:\n",
        "              TrueP[i]+=1\n",
        "            elif res == 1 and labels[0,i] == 0:\n",
        "              FalseP[i]+=1\n",
        "            elif res == 0 and labels[0,i] == 1:\n",
        "              FalseN[i]+=1\n",
        "            else:\n",
        "              TrueN[i]+=1\n",
        "        accTest.append(correct_val/5)\n",
        "        if (pred_bias == 1) and (orig_bias == 1):\n",
        "          TrueP[5]+=1\n",
        "        elif (pred_bias == 1) and (orig_bias == 0):\n",
        "          FalseP[5]+=1\n",
        "        elif pred_bias == 0 and orig_bias == 1:\n",
        "          FalseN[5]+=1\n",
        "        else:\n",
        "          TrueN[5]+=1\n",
        "\n",
        "    print(\"Testing Dataset:\\n\")\n",
        "    print(f\" Test Loss:{Test_loss/len(Test_DL):.4f} | Test Accuracy:{sum(accTest)/len(accTest):.4f}\")\n",
        "\n",
        "Evaluate_Model(Distil_bert, Test_Loader)"
      ],
      "metadata": {
        "id": "rE8eAIwrk9aO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2957b9b0-1aca-4f0b-f3ac-8bbacdea895a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Dataset:\n",
            "\n",
            " Test Loss:0.1206 | Test Accuracy:0.9572\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generateReport(index):\n",
        "  print(f'True Positive: {TrueP[index]} | True Negative: {TrueN[index]} | False Positive: {FalseP[index]} | False Negative: {FalseN[index]}')\n",
        "  if(TrueP[index]+FalseP[index] == 0):\n",
        "    precision = 1\n",
        "  else:\n",
        "    precision = TrueP[index]/(TrueP[index]+FalseP[index])\n",
        "  if(TrueP[index]+FalseN[index] == 0):\n",
        "    recall = 1\n",
        "  else:\n",
        "    recall = TrueP[index]/(TrueP[index]+FalseN[index])\n",
        "  f1 = (2*precision*recall)/(precision+recall)\n",
        "  accuracy = (TrueP[index]+TrueN[index])/(TrueP[index]+TrueN[index]+FalseP[index]+FalseN[index])\n",
        "  print(f'Precision: {precision:.4f} | recall: {recall:.4f} | f1: {f1:.4f} | accuracy: {accuracy:.4f}')"
      ],
      "metadata": {
        "id": "wOYjWJpOtMq2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Bias\")\n",
        "generateReport(5)\n",
        "\n",
        "print(\"\\n\\nRace\")\n",
        "generateReport(0)\n",
        "\n",
        "print(\"\\n\\nGender\")\n",
        "generateReport(1)\n",
        "\n",
        "print(\"\\n\\nReligion\")\n",
        "generateReport(2)\n",
        "\n",
        "print(\"\\n\\nLGBTQ\")\n",
        "generateReport(3)\n",
        "\n",
        "print(\"\\n\\nPolitical\")\n",
        "generateReport(4)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_5PLT55sgpd",
        "outputId": "35de0b73-f897-4ea4-80e6-a3ec2abdba95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bias\n",
            "True Positive: 402 | True Negative: 514 | False Positive: 90 | False Negative: 36\n",
            "Precision: 0.8171 | recall: 0.9178 | f1: 0.8645 | accuracy: 0.8791\n",
            "\n",
            "\n",
            "Race\n",
            "True Positive: 210 | True Negative: 756 | False Positive: 58 | False Negative: 18\n",
            "Precision: 0.7836 | recall: 0.9211 | f1: 0.8468 | accuracy: 0.9271\n",
            "\n",
            "\n",
            "Gender\n",
            "True Positive: 126 | True Negative: 858 | False Positive: 34 | False Negative: 24\n",
            "Precision: 0.7875 | recall: 0.8400 | f1: 0.8129 | accuracy: 0.9443\n",
            "\n",
            "\n",
            "Religion\n",
            "True Positive: 0 | True Negative: 1004 | False Positive: 0 | False Negative: 38\n",
            "Precision: 1.0000 | recall: 0.0000 | f1: 0.0000 | accuracy: 0.9635\n",
            "\n",
            "\n",
            "LGBTQ\n",
            "True Positive: 52 | True Negative: 962 | False Positive: 21 | False Negative: 7\n",
            "Precision: 0.7123 | recall: 0.8814 | f1: 0.7879 | accuracy: 0.9731\n",
            "\n",
            "\n",
            "Political\n",
            "True Positive: 0 | True Negative: 1019 | False Positive: 0 | False Negative: 23\n",
            "Precision: 1.0000 | recall: 0.0000 | f1: 0.0000 | accuracy: 0.9779\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "UzwduiJu4BvE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import matlplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "OwfkmhVfwDoc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Acc_Loss_Plot(TA1, TA2, TL1, TL2, VA1, VA2, VL1, VL2):\n",
        "    plt.figure(figsize=(20,14))\n",
        "    Ac = plt.subplot(2,2,1)\n",
        "    plt.plot(TA1.keys(), TA1.values())\n",
        "    plt.plot(TA2.keys(), TA2.values())\n",
        "    Ac.set_xlabel(\"Step Number\", fontsize=15)\n",
        "    Ac.set_ylabel(\"Accuracy\", fontsize=15)\n",
        "    Ac.set_title(\"Training Accuracy\", fontsize=20)\n",
        "    Ac.legend([\"Epoch 1\",\"Epoch 2\"])\n",
        "\n",
        "    Ls = plt.subplot(2,2,2)\n",
        "    plt.plot(TL1.keys(), TL1.values())\n",
        "    plt.plot(TL2.keys(), TL2.values())\n",
        "    Ls.set_xlabel(\"Step Number\", fontsize=15)\n",
        "    Ls.set_ylabel(\"Loss\", fontsize=15)\n",
        "    Ls.set_title(\"Training Loss\", fontsize=20)\n",
        "    Ls.legend([\"Epoch 1\",\"Epoch 2\"])\n",
        "\n",
        "    VAc = plt.subplot(2,2,3)\n",
        "    plt.plot(VA1.keys(), VA1.values())\n",
        "    plt.plot(VA2.keys(), VA2.values())\n",
        "    VAc.set_xlabel(\"Step Number\", fontsize=15)\n",
        "    VAc.set_ylabel(\"Accuracy\", fontsize=15)\n",
        "    VAc.set_title(\"Validation Accuracy\", fontsize=20)\n",
        "    VAc.legend([\"Epoch 1\",\"Epoch 2\"])\n",
        "\n",
        "    VLs = plt.subplot(2,2,4)\n",
        "    plt.plot(VL1.keys(), VL1.values())\n",
        "    plt.plot(VL2.keys(), VL2.values())\n",
        "    VLs.set_xlabel(\"Step Number\", fontsize=15)\n",
        "    VLs.set_ylabel(\"Loss\", fontsize=15)\n",
        "    VLs.set_title(\"Validation Loss\", fontsize=20)\n",
        "    VLs.legend([\"Epoch 1\",\"Epoch 2\"])\n",
        "\n",
        "Acc_Loss_Plot(TA[0], TA[1], TL[0], TL[1], VA[0], VA[1], VL[0], VL[1])"
      ],
      "metadata": {
        "id": "WmB_58i8lFkJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7eef8b1e-457a-4d1d-c1f8-58a60a3399fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1400 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving model\n",
        "\n",
        "torch.save(Distil_bert,\"/content/dsbert_toxic_aug.pt\")"
      ],
      "metadata": {
        "id": "I7dqKWxhlJhB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vj46BDc8ycCL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}