toxic_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "toxic classification.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM4xK7KYq7nVY0+sGWN1zmN",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "94a635b0446f45f8b719fbe64c96fdfe": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_34a3bba2df2a4ef7b5afcd7ee66fb22b",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_6bbf42f4a16845569a3d997e3b42218c",
              "IPY_MODEL_4b5f791fab4c48d1885fc0dbd110f698",
              "IPY_MODEL_8ae17de8a7d94abba3da61830a77347f"
            ]
          }
        },
        "34a3bba2df2a4ef7b5afcd7ee66fb22b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "6bbf42f4a16845569a3d997e3b42218c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_aeb2cc60342845869649a9968a5eee6c",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": "",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_c8e6f4413c1645a59569d4088150e909"
          }
        },
        "4b5f791fab4c48d1885fc0dbd110f698": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_6d1699e992cf496995b34c144f37a80b",
            "_dom_classes": [],
            "description": "",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 1,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 1,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_2d0f80a5991b4a7fa59efcdd1068566c"
          }
        },
        "8ae17de8a7d94abba3da61830a77347f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_fc406b6417e84d1eb8d913a7121d588c",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 1999996/? [02:17&lt;00:00, 14545.00it/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_92eaf8b0da7042a4b72eb85c43cc1053"
          }
        },
        "aeb2cc60342845869649a9968a5eee6c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "c8e6f4413c1645a59569d4088150e909": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "6d1699e992cf496995b34c144f37a80b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "2d0f80a5991b4a7fa59efcdd1068566c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": "20px",
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "fc406b6417e84d1eb8d913a7121d588c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "92eaf8b0da7042a4b72eb85c43cc1053": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/levi3001/cs470_term_project_toxic_classification/blob/main/toxic_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvSrOZ_hv_SS"
      },
      "source": [
        "try:\n",
        "    %tensorflow_version 2.x\n",
        "except Exception:\n",
        "    pass\n",
        "\n",
        "import os\n",
        "import random\n",
        "import re\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import re\n",
        "from tqdm.auto import tqdm"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kW7k_ppx2MN9",
        "outputId": "6648f1d2-2127-4f1c-eaaa-cedac25dadd7"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SmmsG-0ELQA",
        "outputId": "6fa4fe64-92ea-4090-a1cc-8e2cd7267dfc"
      },
      "source": [
        "!unzip  drive/MyDrive/jigsaw-toxic-comment-classification-challenge.zip -d data"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  drive/MyDrive/jigsaw-toxic-comment-classification-challenge.zip\n",
            "  inflating: data/sample_submission.csv.zip  \n",
            "  inflating: data/test.csv.zip       \n",
            "  inflating: data/test_labels.csv.zip  \n",
            "  inflating: data/train.csv.zip      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "S_adDNelIIjt",
        "outputId": "0413a91f-c80d-42d3-afc3-976920e40e45"
      },
      "source": [
        "train_data=pd.read_csv('data/train.csv.zip')\n",
        "train_data"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>comment_text</th>\n",
              "      <th>toxic</th>\n",
              "      <th>severe_toxic</th>\n",
              "      <th>obscene</th>\n",
              "      <th>threat</th>\n",
              "      <th>insult</th>\n",
              "      <th>identity_hate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0000997932d777bf</td>\n",
              "      <td>Explanation\\nWhy the edits made under my usern...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>000103f0d9cfb60f</td>\n",
              "      <td>D'aww! He matches this background colour I'm s...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>000113f07ec002fd</td>\n",
              "      <td>Hey man, I'm really not trying to edit war. It...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0001b41b1c6bb37e</td>\n",
              "      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0001d958c54c6e35</td>\n",
              "      <td>You, sir, are my hero. Any chance you remember...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>159566</th>\n",
              "      <td>ffe987279560d7ff</td>\n",
              "      <td>\":::::And for the second time of asking, when ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159567</th>\n",
              "      <td>ffea4adeee384e90</td>\n",
              "      <td>You should be ashamed of yourself \\n\\nThat is ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159568</th>\n",
              "      <td>ffee36eab5c267c9</td>\n",
              "      <td>Spitzer \\n\\nUmm, theres no actual article for ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159569</th>\n",
              "      <td>fff125370e4aaaf3</td>\n",
              "      <td>And it looks like it was actually you who put ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159570</th>\n",
              "      <td>fff46fc426af1f9a</td>\n",
              "      <td>\"\\nAnd ... I really don't think you understand...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>159571 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      id  ... identity_hate\n",
              "0       0000997932d777bf  ...             0\n",
              "1       000103f0d9cfb60f  ...             0\n",
              "2       000113f07ec002fd  ...             0\n",
              "3       0001b41b1c6bb37e  ...             0\n",
              "4       0001d958c54c6e35  ...             0\n",
              "...                  ...  ...           ...\n",
              "159566  ffe987279560d7ff  ...             0\n",
              "159567  ffea4adeee384e90  ...             0\n",
              "159568  ffee36eab5c267c9  ...             0\n",
              "159569  fff125370e4aaaf3  ...             0\n",
              "159570  fff46fc426af1f9a  ...             0\n",
              "\n",
              "[159571 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "bon3QefSJAKg",
        "outputId": "8fa323c8-e0ee-48f6-c77f-b7318ae76e47"
      },
      "source": [
        "test_data=pd.read_csv('data/test.csv.zip')\n",
        "test_data"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>comment_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00001cee341fdb12</td>\n",
              "      <td>Yo bitch Ja Rule is more succesful then you'll...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0000247867823ef7</td>\n",
              "      <td>== From RfC == \\n\\n The title is fine as it is...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00013b17ad220c46</td>\n",
              "      <td>\" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00017563c3f7919a</td>\n",
              "      <td>:If you have a look back at the source, the in...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00017695ad8997eb</td>\n",
              "      <td>I don't anonymously edit articles at all.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153159</th>\n",
              "      <td>fffcd0960ee309b5</td>\n",
              "      <td>. \\n i totally agree, this stuff is nothing bu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153160</th>\n",
              "      <td>fffd7a9a6eb32c16</td>\n",
              "      <td>== Throw from out field to home plate. == \\n\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153161</th>\n",
              "      <td>fffda9e8d6fafa9e</td>\n",
              "      <td>\" \\n\\n == Okinotorishima categories == \\n\\n I ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153162</th>\n",
              "      <td>fffe8f1340a79fc2</td>\n",
              "      <td>\" \\n\\n == \"\"One of the founding nations of the...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153163</th>\n",
              "      <td>ffffce3fb183ee80</td>\n",
              "      <td>\" \\n :::Stop already. Your bullshit is not wel...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>153164 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      id                                       comment_text\n",
              "0       00001cee341fdb12  Yo bitch Ja Rule is more succesful then you'll...\n",
              "1       0000247867823ef7  == From RfC == \\n\\n The title is fine as it is...\n",
              "2       00013b17ad220c46  \" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...\n",
              "3       00017563c3f7919a  :If you have a look back at the source, the in...\n",
              "4       00017695ad8997eb          I don't anonymously edit articles at all.\n",
              "...                  ...                                                ...\n",
              "153159  fffcd0960ee309b5  . \\n i totally agree, this stuff is nothing bu...\n",
              "153160  fffd7a9a6eb32c16  == Throw from out field to home plate. == \\n\\n...\n",
              "153161  fffda9e8d6fafa9e  \" \\n\\n == Okinotorishima categories == \\n\\n I ...\n",
              "153162  fffe8f1340a79fc2  \" \\n\\n == \"\"One of the founding nations of the...\n",
              "153163  ffffce3fb183ee80  \" \\n :::Stop already. Your bullshit is not wel...\n",
              "\n",
              "[153164 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "v1YrXfT7Jj51",
        "outputId": "086d5f7d-55b7-487b-dc5c-26ef5b14e588"
      },
      "source": [
        "test_label=pd.read_csv('data/test_labels.csv.zip')\n",
        "test_label"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>toxic</th>\n",
              "      <th>severe_toxic</th>\n",
              "      <th>obscene</th>\n",
              "      <th>threat</th>\n",
              "      <th>insult</th>\n",
              "      <th>identity_hate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00001cee341fdb12</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0000247867823ef7</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00013b17ad220c46</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00017563c3f7919a</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00017695ad8997eb</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153159</th>\n",
              "      <td>fffcd0960ee309b5</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153160</th>\n",
              "      <td>fffd7a9a6eb32c16</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153161</th>\n",
              "      <td>fffda9e8d6fafa9e</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153162</th>\n",
              "      <td>fffe8f1340a79fc2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153163</th>\n",
              "      <td>ffffce3fb183ee80</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>153164 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      id  toxic  severe_toxic  ...  threat  insult  identity_hate\n",
              "0       00001cee341fdb12     -1            -1  ...      -1      -1             -1\n",
              "1       0000247867823ef7     -1            -1  ...      -1      -1             -1\n",
              "2       00013b17ad220c46     -1            -1  ...      -1      -1             -1\n",
              "3       00017563c3f7919a     -1            -1  ...      -1      -1             -1\n",
              "4       00017695ad8997eb     -1            -1  ...      -1      -1             -1\n",
              "...                  ...    ...           ...  ...     ...     ...            ...\n",
              "153159  fffcd0960ee309b5     -1            -1  ...      -1      -1             -1\n",
              "153160  fffd7a9a6eb32c16     -1            -1  ...      -1      -1             -1\n",
              "153161  fffda9e8d6fafa9e     -1            -1  ...      -1      -1             -1\n",
              "153162  fffe8f1340a79fc2     -1            -1  ...      -1      -1             -1\n",
              "153163  ffffce3fb183ee80     -1            -1  ...      -1      -1             -1\n",
              "\n",
              "[153164 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyW58BcSz0C3"
      },
      "source": [
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub(r\"what's\", \"what is \", text)\n",
        "    text = re.sub(r\"\\'s\", \" \", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
        "    text = re.sub(r\"can't\", \"cannot \", text)\n",
        "    text = re.sub(r\"n't\", \" not \", text)\n",
        "    text = re.sub(r\"i'm\", \"i am \", text)\n",
        "    text = re.sub(r\"\\'re\", \" are \", text)\n",
        "    text = re.sub(r\"\\'d\", \" would \", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
        "    text = re.sub(r\"\\'scuse\", \" excuse \", text)\n",
        "    text = re.sub('\\W', ' ', text)\n",
        "    text = re.sub('\\s+', ' ', text)\n",
        "    text = text.strip(' ')\n",
        "    return text\n",
        "#https://www.kaggle.com/gtskyler/toxic-comments-bert"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdAh3l9vMl84"
      },
      "source": [
        "train_data['comment_texxt']=train_data['comment_text'].map(lambda text: clean_text(text))\n",
        "test_data['comment_texxt']=test_data['comment_text'].map(lambda text: clean_text(text))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEmUrpIxJn03"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.utils.np_utils import to_categorical\n",
        "import tensorflow as tf\n",
        "import re\n",
        "\n",
        "vocab_size = 6000\n",
        "tokenizer = Tokenizer(num_words=vocab_size, split=' ', oov_token='<OOV>')\n",
        "tokenizer.fit_on_texts(train_data['comment_text'].values)\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWz5R58kMev0"
      },
      "source": [
        ""
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTT6g-dYKHjC"
      },
      "source": [
        "train_val_x = train_data['comment_text'].values\n",
        "train_val_y = train_data[[\"toxic\",\"severe_toxic\",\"obscene\",\"threat\",\"insult\",\"identity_hate\"]].values\n",
        "train_x, val_x, train_y, val_y = train_test_split(train_val_x,train_val_y, test_size=0.2, random_state=30)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bf6ka8N59WDW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fefe74c6-71f3-4f81-b04f-b8ba21fcea22"
      },
      "source": [
        "print(len(train_x))\n",
        "print(train_y.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "127656\n",
            "(127656, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ivn6Er4y9Z8q"
      },
      "source": [
        "tokenizer = Tokenizer(num_words = 100000, oov_token='<oov>')\n",
        "tokenizer.fit_on_texts(train_data['comment_text'])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNXxgHGX9lHl",
        "outputId": "5e25f21e-1bbb-4940-d473-8f67a8e8e821"
      },
      "source": [
        "train_seq = tokenizer.texts_to_sequences(train_x)\n",
        "maxlen = max([len(x) for x in train_seq])\n",
        "train_pad = pad_sequences(train_seq, maxlen = maxlen,\n",
        "                                padding = 'pre',\n",
        "                                truncating='pre')\n",
        "print(maxlen)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1403\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XU7z7hjk9omY"
      },
      "source": [
        "val_seq = tokenizer.texts_to_sequences(val_x)\n",
        "val_pad = pad_sequences(val_seq, maxlen = maxlen,\n",
        "                                padding = 'pre',\n",
        "                                truncating='pre')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGlEnXDJ9nIn",
        "outputId": "2be535c5-4282-4d72-9c1b-c605fa04cd69"
      },
      "source": [
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "vocab_size"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "210339"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMLRTu3-ED3m"
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCJqoqGADe1U",
        "outputId": "c9d04596-1e19-4105-e2ac-4beeadff8557"
      },
      "source": [
        "!wget dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-20 17:24:27--  http://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 104.22.74.142, 172.67.9.4, 104.22.75.142, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|104.22.74.142|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1523785255 (1.4G) [application/zip]\n",
            "Saving to: ‘crawl-300d-2M.vec.zip’\n",
            "\n",
            "crawl-300d-2M.vec.z 100%[===================>]   1.42G  52.8MB/s    in 27s     \n",
            "\n",
            "2021-11-20 17:24:54 (54.6 MB/s) - ‘crawl-300d-2M.vec.zip’ saved [1523785255/1523785255]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zd6Y_KhEYlZ",
        "outputId": "faead147-aaea-4f67-d26c-3e9171d11638"
      },
      "source": [
        "!unzip crawl-300d-2M.vec.zip "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  crawl-300d-2M.vec.zip\n",
            "  inflating: crawl-300d-2M.vec       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lWfEbHQ0bD8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "94a635b0446f45f8b719fbe64c96fdfe",
            "34a3bba2df2a4ef7b5afcd7ee66fb22b",
            "6bbf42f4a16845569a3d997e3b42218c",
            "4b5f791fab4c48d1885fc0dbd110f698",
            "8ae17de8a7d94abba3da61830a77347f",
            "aeb2cc60342845869649a9968a5eee6c",
            "c8e6f4413c1645a59569d4088150e909",
            "6d1699e992cf496995b34c144f37a80b",
            "2d0f80a5991b4a7fa59efcdd1068566c",
            "fc406b6417e84d1eb8d913a7121d588c",
            "92eaf8b0da7042a4b72eb85c43cc1053"
          ]
        },
        "outputId": "674ae2ae-4300-4c80-96db-59892dfab499"
      },
      "source": [
        "import io\n",
        "print('loading word embeddings...')\n",
        "embeddings_index = {}\n",
        "f = io.open('crawl-300d-2M.vec', encoding='utf-8')\n",
        "for line in tqdm(f):\n",
        "    values = line.rstrip().rsplit(' ')\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()\n",
        "print('found %s word vectors' % len(embeddings_index))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading word embeddings...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "94a635b0446f45f8b719fbe64c96fdfe",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "found 1999996 word vectors\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XItSiAW0SFY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94d14352-0796-4c4b-e546-53373a0739c6"
      },
      "source": [
        "#embedding matrix\n",
        "print('preparing embedding matrix...')\n",
        "words_not_found = []\n",
        "nb_words = vocab_size\n",
        "embedding_matrix = np.zeros((nb_words, 300))\n",
        "for word, i in tokenizer.word_index.items():\n",
        "    if i >= nb_words:\n",
        "        continue\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if (embedding_vector is not None) and len(embedding_vector) > 0:\n",
        "        # words not found in embedding index will be all-zeros.\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "    else:\n",
        "        words_not_found.append(word)\n",
        "print('number of null word embeddings: %d' % np.sum(np.sum(embedding_matrix, axis=1) == 0))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "preparing embedding matrix...\n",
            "number of null word embeddings: 108220\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9tvMcaj9sy0"
      },
      "source": [
        "model = tf.keras.Sequential(\n",
        "    [tf.keras.layers.Embedding(input_dim = vocab_size,output_dim = 300,input_length = maxlen,weights=[embedding_matrix]),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(6)\n",
        "    ])"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAE3h8lY97FH",
        "outputId": "7da02ada-cf2d-4322-a303-7351eec55d29"
      },
      "source": [
        "model.compile(loss = tf.keras.losses.BinaryCrossentropy(from_logits=True), optimizer = 'adam', metrics = ['accuracy'])\n",
        "model.fit(train_pad,train_y, epochs = 2, validation_data=(val_pad, val_y), batch_size = 32)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "3990/3990 [==============================] - 650s 161ms/step - loss: 0.0546 - accuracy: 0.9789 - val_loss: 0.0447 - val_accuracy: 0.9938\n",
            "Epoch 2/2\n",
            "3990/3990 [==============================] - 644s 161ms/step - loss: 0.0373 - accuracy: 0.9880 - val_loss: 0.0424 - val_accuracy: 0.9923\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3eaa2e9b50>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmds7XPG-N0O",
        "outputId": "6df2b311-6c6d-4fce-83d9-8c66fa4c52d8"
      },
      "source": [
        "test_seq = tokenizer.texts_to_sequences(test_data.comment_text)\n",
        "test_pad = pad_sequences(test_seq, maxlen = maxlen,\n",
        "                                padding = 'pre',\n",
        "                                truncating='pre')\n",
        "predicted = tf.math.sigmoid(model.predict(test_pad, batch_size = 200))\n",
        "predict = np.hstack((test_data.id[:, np.newaxis], predicted))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awmWtWKEHnby"
      },
      "source": [
        "subm = pd.DataFrame(predict, columns = ['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'])\n",
        "subm.to_csv('subm.csv', index = False)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "QpYvoSrLIIU3",
        "outputId": "ff7f293a-c108-42f7-a889-930540b6ea3f"
      },
      "source": [
        "subm"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>toxic</th>\n",
              "      <th>severe_toxic</th>\n",
              "      <th>obscene</th>\n",
              "      <th>threat</th>\n",
              "      <th>insult</th>\n",
              "      <th>identity_hate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00001cee341fdb12</td>\n",
              "      <td>0.988414</td>\n",
              "      <td>0.189765</td>\n",
              "      <td>0.955813</td>\n",
              "      <td>0.029313</td>\n",
              "      <td>0.847096</td>\n",
              "      <td>0.354981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0000247867823ef7</td>\n",
              "      <td>0.000178022</td>\n",
              "      <td>4.17261e-07</td>\n",
              "      <td>1.46116e-05</td>\n",
              "      <td>2.40856e-06</td>\n",
              "      <td>7.23422e-06</td>\n",
              "      <td>2.39614e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00013b17ad220c46</td>\n",
              "      <td>0.000578599</td>\n",
              "      <td>3.38327e-06</td>\n",
              "      <td>0.000114595</td>\n",
              "      <td>8.44434e-06</td>\n",
              "      <td>2.99208e-05</td>\n",
              "      <td>9.12727e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00017563c3f7919a</td>\n",
              "      <td>0.000522951</td>\n",
              "      <td>6.48672e-06</td>\n",
              "      <td>3.54218e-05</td>\n",
              "      <td>4.31723e-05</td>\n",
              "      <td>5.97763e-05</td>\n",
              "      <td>1.88689e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00017695ad8997eb</td>\n",
              "      <td>0.00100809</td>\n",
              "      <td>1.27787e-05</td>\n",
              "      <td>0.000109722</td>\n",
              "      <td>3.85049e-05</td>\n",
              "      <td>7.19184e-05</td>\n",
              "      <td>3.16169e-05</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153159</th>\n",
              "      <td>fffcd0960ee309b5</td>\n",
              "      <td>0.566831</td>\n",
              "      <td>0.0011156</td>\n",
              "      <td>0.122031</td>\n",
              "      <td>0.000778948</td>\n",
              "      <td>0.0264501</td>\n",
              "      <td>0.0013428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153160</th>\n",
              "      <td>fffd7a9a6eb32c16</td>\n",
              "      <td>0.00271604</td>\n",
              "      <td>1.64712e-05</td>\n",
              "      <td>0.000191134</td>\n",
              "      <td>5.38324e-05</td>\n",
              "      <td>0.000260561</td>\n",
              "      <td>0.000131835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153161</th>\n",
              "      <td>fffda9e8d6fafa9e</td>\n",
              "      <td>0.000780383</td>\n",
              "      <td>8.97054e-06</td>\n",
              "      <td>0.000173499</td>\n",
              "      <td>1.71007e-05</td>\n",
              "      <td>6.49465e-05</td>\n",
              "      <td>1.64345e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153162</th>\n",
              "      <td>fffe8f1340a79fc2</td>\n",
              "      <td>0.00114952</td>\n",
              "      <td>2.1168e-06</td>\n",
              "      <td>7.53439e-05</td>\n",
              "      <td>9.61183e-06</td>\n",
              "      <td>4.24199e-05</td>\n",
              "      <td>2.51856e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153163</th>\n",
              "      <td>ffffce3fb183ee80</td>\n",
              "      <td>0.978304</td>\n",
              "      <td>0.0701576</td>\n",
              "      <td>0.911673</td>\n",
              "      <td>0.00588393</td>\n",
              "      <td>0.817598</td>\n",
              "      <td>0.0116858</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>153164 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      id        toxic  ...       insult identity_hate\n",
              "0       00001cee341fdb12     0.988414  ...     0.847096      0.354981\n",
              "1       0000247867823ef7  0.000178022  ...  7.23422e-06   2.39614e-06\n",
              "2       00013b17ad220c46  0.000578599  ...  2.99208e-05   9.12727e-06\n",
              "3       00017563c3f7919a  0.000522951  ...  5.97763e-05   1.88689e-05\n",
              "4       00017695ad8997eb   0.00100809  ...  7.19184e-05   3.16169e-05\n",
              "...                  ...          ...  ...          ...           ...\n",
              "153159  fffcd0960ee309b5     0.566831  ...    0.0264501     0.0013428\n",
              "153160  fffd7a9a6eb32c16   0.00271604  ...  0.000260561   0.000131835\n",
              "153161  fffda9e8d6fafa9e  0.000780383  ...  6.49465e-05   1.64345e-05\n",
              "153162  fffe8f1340a79fc2   0.00114952  ...  4.24199e-05   2.51856e-05\n",
              "153163  ffffce3fb183ee80     0.978304  ...     0.817598     0.0116858\n",
              "\n",
              "[153164 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YprL4uu8IKRH"
      },
      "source": [
        ""
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "hUZnM-lJI1JY",
        "outputId": "766735e0-8529-48f6-9651-52a6a004e14b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "name=  [ 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
        "values= [sum(train_data[name[i]]>0) for i in range(len(name))]\n",
        "plt.bar(name, values)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ptqlgzpekjv1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}