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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"..\")\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/timo/rep/TextClassifier/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "splits = {'train': 'train.jsonl', 'test': 'test.jsonl'}\n",
    "train = pd.read_json(\"hf://datasets/AlexSham/Toxic_Russian_Comments/\" + splits[\"train\"], lines=True)\n",
    "test = pd.read_json(\"hf://datasets/AlexSham/Toxic_Russian_Comments/\" + splits[\"test\"], lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>видимо в разных регионах называют по разному ,...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>понятно что это нарушение правил, писать капсл...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>какие классные, жизненные стихи....</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>а и правда-когда его запретили?...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>в соленой воде вирусы живут .ученые изучали со...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223456</th>\n",
       "      <td>вова - дима когда же вы подавитесь деньгами???...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223457</th>\n",
       "      <td>какая красота, просто нет слов выразить чувств...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223458</th>\n",
       "      <td>вы пост гаи выставити на перекрестке возле 21 ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223459</th>\n",
       "      <td>как -то на лебедей непохожи</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223460</th>\n",
       "      <td>интересно чей это самолет!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>223461 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     text  label\n",
       "0       видимо в разных регионах называют по разному ,...      0\n",
       "1       понятно что это нарушение правил, писать капсл...      1\n",
       "2                     какие классные, жизненные стихи....      0\n",
       "3                      а и правда-когда его запретили?...      0\n",
       "4       в соленой воде вирусы живут .ученые изучали со...      0\n",
       "...                                                   ...    ...\n",
       "223456  вова - дима когда же вы подавитесь деньгами???...      0\n",
       "223457  какая красота, просто нет слов выразить чувств...      0\n",
       "223458  вы пост гаи выставити на перекрестке возле 21 ...      0\n",
       "223459                        как -то на лебедей непохожи      0\n",
       "223460                         интересно чей это самолет!      0\n",
       "\n",
       "[223461 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>хорошо пошло!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>посмотрела, как будто дома побывала. как река ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>отдам котят 1,5 месяца в добрые руки.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0,5литровая баночка 200р стоит в таганроге. та...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>речь шла о радужных зонтиках над верандой.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24824</th>\n",
       "      <td>и ты будь здоров</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24825</th>\n",
       "      <td>не дорога а прям стекло но правда битое (h)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24826</th>\n",
       "      <td>спасибо большое. буду ждать хороших новостей. ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24827</th>\n",
       "      <td>активирую установку 🌈🌈🌈👍😎🔥🔥🔥</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24828</th>\n",
       "      <td>а вы курс российского рубля видели, кошмар!!!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24829 rows × 2 columns</p>\n",
       "</div>"
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       "                                                    text  label\n",
       "0                                          хорошо пошло!      0\n",
       "1      посмотрела, как будто дома побывала. как река ...      0\n",
       "2                  отдам котят 1,5 месяца в добрые руки.      0\n",
       "3      0,5литровая баночка 200р стоит в таганроге. та...      0\n",
       "4             речь шла о радужных зонтиках над верандой.      0\n",
       "...                                                  ...    ...\n",
       "24824                                   и ты будь здоров      0\n",
       "24825        не дорога а прям стекло но правда битое (h)      0\n",
       "24826  спасибо большое. буду ждать хороших новостей. ...      0\n",
       "24827                       активирую установку 🌈🌈🌈👍😎🔥🔥🔥      0\n",
       "24828      а вы курс российского рубля видели, кошмар!!!      0\n",
       "\n",
       "[24829 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['class'] = train['label'].map({0: 'non-toxic', 1: 'toxic'})\n",
    "test['class'] = test['label'].map({0: 'non-toxic', 1: 'toxic'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets = {\"Train Dataset\": train, \"Test Dataset\": test}\n",
    "\n",
    "for name, df in datasets.items():\n",
    "    df['class'].value_counts().plot.pie(\n",
    "        autopct='%1.1f%%',\n",
    "        ylabel='',\n",
    "        title=f\"Распределение классов в {name}\"\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Dataset\n",
      "class: non-toxic\n",
      "0     видимо в разных регионах называют по разному ,...\n",
      "2                   какие классные, жизненные стихи....\n",
      "3                    а и правда-когда его запретили?...\n",
      "4     в соленой воде вирусы живут .ученые изучали со...\n",
      "6     ни в одном из близлежащих строительных не нашл...\n",
      "7     какая дрянь сломала? виноваты родители, выраст...\n",
      "8     да, висели такие на стене, над кроватью. в люб...\n",
      "9                           потому что мы так воспитаны\n",
      "10                                    лапти и не только\n",
      "11                  привет обмен на дизель без вложений\n",
      "Name: text, dtype: object \n",
      "\n",
      "class: toxic\n",
      "1     понятно что это нарушение правил, писать капсл...\n",
      "5     правильно! это же тихановская 26 лет растила и...\n",
      "13                                     на хуй, безликая\n",
      "16                  дебилов хватает.надо было с головой\n",
      "30                    умник хуев. у каждого своё мнение\n",
      "38      на мыло его дегтярное пустить пидора путинского\n",
      "47    неправильно вы называете таких чиновников идио...\n",
      "52                             ретранслятор тебе в жопу\n",
      "67                                         пидор усатый\n",
      "71             а вы пидоры учились платно???,гандоны!!!\n",
      "Name: text, dtype: object \n",
      "\n",
      "Test Dataset\n",
      "class: non-toxic\n",
      "0     видимо в разных регионах называют по разному ,...\n",
      "2                   какие классные, жизненные стихи....\n",
      "3                    а и правда-когда его запретили?...\n",
      "4     в соленой воде вирусы живут .ученые изучали со...\n",
      "6     ни в одном из близлежащих строительных не нашл...\n",
      "7     какая дрянь сломала? виноваты родители, выраст...\n",
      "8     да, висели такие на стене, над кроватью. в люб...\n",
      "9                           потому что мы так воспитаны\n",
      "10                                    лапти и не только\n",
      "11                  привет обмен на дизель без вложений\n",
      "Name: text, dtype: object \n",
      "\n",
      "class: toxic\n",
      "1     понятно что это нарушение правил, писать капсл...\n",
      "5     правильно! это же тихановская 26 лет растила и...\n",
      "13                                     на хуй, безликая\n",
      "16                  дебилов хватает.надо было с головой\n",
      "30                    умник хуев. у каждого своё мнение\n",
      "38      на мыло его дегтярное пустить пидора путинского\n",
      "47    неправильно вы называете таких чиновников идио...\n",
      "52                             ретранслятор тебе в жопу\n",
      "67                                         пидор усатый\n",
      "71             а вы пидоры учились платно???,гандоны!!!\n",
      "Name: text, dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for name, df in datasets.items():\n",
    "    print(name)\n",
    "    for label in train['class'].unique():\n",
    "        print(f\"class: {label}\")\n",
    "        print(train[train['class'] == label]['text'].iloc[:10], \"\\n\")"
   ]
  }
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