{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "albert_multiclass.ipynb             data.csv\n",
      "albert_sentiment_checkpoint_100.pt  datapreprocessing.ipynb\n",
      "albert_sentiment_checkpoint_96.pt   deberta.py\n",
      "albert_sentiment_checkpoint_97.pt   evaludate_roberta.py\n",
      "albert_sentiment_checkpoint_98.pt   \u001b[0m\u001b[01;34mlightning_logs\u001b[0m/\n",
      "albert_sentiment_checkpoint_99.pt   model.py\n",
      "\u001b[01;34mbug_priority_multiclass\u001b[0m/            newdata.csv\n",
      "bug_priority_multiclass.zip         preProcessed.csv\n",
      "\u001b[01;34mcheckpoints\u001b[0m/                        Pri_Android_A11y.xlsx\n",
      "data_cleaned2.csv                   \u001b[01;34m__pycache__\u001b[0m/\n",
      "data_cleaned.csv\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('./data_cleaned2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>labels</th>\n",
       "      <th>textlen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VoiceOver skips over text in paragraphs which ...</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AXEnhancedUserInterface breaks window managers...</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mac voiceover screen reader</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>when using firefox on mac with voiceover you c...</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Children of HTML label element are read 3 time...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  labels  textlen\n",
       "0  VoiceOver skips over text in paragraphs which ...       2       12\n",
       "1  AXEnhancedUserInterface breaks window managers...       2       14\n",
       "2                        mac voiceover screen reader       3        4\n",
       "3  when using firefox on mac with voiceover you c...       2       13\n",
       "4  Children of HTML label element are read 3 time...       2       11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'Kevin_Pri':'label','Summary':'text'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>labels</th>\n",
       "      <th>textlen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VoiceOver skips over text in paragraphs which ...</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AXEnhancedUserInterface breaks window managers...</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mac voiceover screen reader</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>when using firefox on mac with voiceover you c...</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Children of HTML label element are read 3 time...</td>\n",
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      "text/plain": [
       "                                                text  labels  textlen\n",
       "0  VoiceOver skips over text in paragraphs which ...       2       12\n",
       "1  AXEnhancedUserInterface breaks window managers...       2       14\n",
       "2                        mac voiceover screen reader       3        4\n",
       "3  when using firefox on mac with voiceover you c...       2       13\n",
       "4  Children of HTML label element are read 3 time...       2       11"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['textlen']= df['text'].apply(lambda x: len(x.split()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'label'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/albert/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'label'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/miniconda3/envs/albert/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/miniconda3/envs/albert/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'label'"
     ]
    }
   ],
   "source": [
    "df['label']= df['label']-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0adOQ7Q0NDUpLSwu7d4/HE3atSWoLR+c4Bw4cGLNzXa4t8wNh147WusI5Di4tVh9b4xGzih3xOKsLFy6EXRtxQLnhhht04sQJdXd365VXXlFFRYWampoiPUxEqqqqVFlZGbzd09Oj3NxclZSUyOFwDHt/v98vj8ejxYsXy2azXclWr4hZ1QdH5TinqkvH7FwjZU+0tGV+QBuOJ8oXSAjrPqO1rnCOg1Cx/tgaT5hV7IjnWQ0+AxKOiANKSkqKrrvuOklSQUGBjh07pmeffVb33HOP+vv71dXVFXIVpaOjQ9nZ2ZKk7OxsHT16NOR4g+/yGay5FLvdLrvdPmS7zWaLaHiR1pvCNxDeD+rhhLP20TrX5fIFEsLuZbTWFYvfG6aI1cfWeMSsYkc8ziqS9Vz270EJBALy+XwqKCiQzWZTY2NjcF9bW5va29vlcrkkSS6XSydPnlRnZ2ewxuPxyOFwKD8//3JbAQAAcSKiKyhVVVVasmSJ8vLydO7cOdXX1+vdd9/VwYMHlZGRoeXLl6uyslKZmZlyOBxatWqVXC6XioqKJEklJSXKz8/XsmXLVFtbK6/Xq/Xr18vtdl/yCgkAABifIgoonZ2deuCBB/TZZ58pIyNDc+bM0cGDB7V48WJJ0tNPP63ExESVl5fL5/OptLRU27ZtC94/KSlJ+/bt04oVK+RyuZSenq6Kigpt3rx5dFcFAABiWkQBZefOnd+4PzU1VXV1daqrq/vammnTpvFuCQAA8I34LB4AAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjBPRhwUC8Wz6uv3D1ny6tWwMOgEAcAUFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwTnK0GwBiyfR1+4et+XRr2Rh0AgDxjSsoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgRBZSamhrdcsstmjRpkrKysnTXXXepra0tpKavr09ut1tTpkzRxIkTVV5ero6OjpCa9vZ2lZWVKS0tTVlZWVq7dq0uXrx4+asBAABxIaKA0tTUJLfbrZaWFnk8Hvn9fpWUlKi3tzdYs2bNGr3++uvas2ePmpqadPbsWS1dujS4f2BgQGVlZerv79fhw4f14osvavfu3dq4cePorQoAAMS05EiK33zzzZDbu3fvVlZWllpbW/W9731P3d3d2rlzp+rr67Vw4UJJ0q5du3TjjTeqpaVFRUVFamho0AcffKC33npLTqdT8+bN05YtW/Too4+qurpaKSkpo7e6GDB93f5otwAAgHEiCihf1d3dLUnKzMyUJLW2tsrv96u4uDhYM3PmTOXl5am5uVlFRUVqbm7W7Nmz5XQ6gzWlpaVasWKFTp8+rZtuumnIeXw+n3w+X/B2T0+PJMnv98vv9w/b52BNOLVjzZ5kjdm5wln/WPZzyfMnWiF/hsO0dZn4fXalmPzYQihmFTvieVaRrCnBsqwR/c0dCAT0ox/9SF1dXXrvvfckSfX19XrwwQdDwoQkFRYW6vbbb9evfvUrPfzww/r73/+ugwcPBvdfuHBB6enpOnDggJYsWTLkXNXV1dq0adOQ7fX19UpLSxtJ+wAAYIxduHBBP/7xj9Xd3S2Hw/GNtSO+guJ2u3Xq1KlgOLmSqqqqVFlZGbzd09Oj3NxclZSUDLtA6cvE5vF4tHjxYtlstivZasRmVR8cvmiUnKouHbZmLPu5FHuipS3zA9pwPFG+QEJY9zFtXeH0Ey9MfmwhFLOKHfE8q8FnQMIxooCycuVK7du3T4cOHdLUqVOD27Ozs9Xf36+uri5Nnjw5uL2jo0PZ2dnBmqNHj4Ycb/BdPoM1X2W322W324dst9lsEQ0v0vqx4BsI74fwaAhn7WPZzzfxBRLC7sW0dZn2PTYWTHxs4dKYVeyIx1lFsp6I3sVjWZZWrlypvXv36u2339aMGTNC9hcUFMhms6mxsTG4ra2tTe3t7XK5XJIkl8ulkydPqrOzM1jj8XjkcDiUn58fSTsAACBORXQFxe12q76+Xq+99pomTZokr9crScrIyNCECROUkZGh5cuXq7KyUpmZmXI4HFq1apVcLpeKiookSSUlJcrPz9eyZctUW1srr9er9evXy+12X/IqCQAAGH8iCijbt2+XJN12220h23ft2qWf/OQnkqSnn35aiYmJKi8vl8/nU2lpqbZt2xasTUpK0r59+7RixQq5XC6lp6eroqJCmzdvvryVAACAuBFRQAnnDT+pqamqq6tTXV3d19ZMmzZNBw4ciOTUAABgHOGzeAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxhnxhwVieNPX7Y92CwAAxCSuoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4ydFuAIg309ftH7bm061lY9AJAMQurqAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMkxztBoDxaPq6/cPWfLq1bAw6AQAzcQUFAAAYh4ACAACMQ0ABAADG4TUoQAzjtSwA4lXEV1AOHTqkO+64Qzk5OUpISNCrr74ast+yLG3cuFHXXnutJkyYoOLiYn300UchNV988YXuv/9+ORwOTZ48WcuXL9f58+cvayEAACB+RBxQent7NXfuXNXV1V1yf21trZ577jnt2LFDR44cUXp6ukpLS9XX1xesuf/++3X69Gl5PB7t27dPhw4d0sMPPzzyVQAAgLgS8VM8S5Ys0ZIlSy65z7IsPfPMM1q/fr3uvPNOSdLvf/97OZ1Ovfrqq7r33nv14Ycf6s0339SxY8c0f/58SdLzzz+vH/7wh/q///s/5eTkXMZyAABAPBjV16B88skn8nq9Ki4uDm7LyMjQggUL1NzcrHvvvVfNzc2aPHlyMJxIUnFxsRITE3XkyBHdfffdQ47r8/nk8/mCt3t6eiRJfr9ffr9/2L4Ga8KpHU32JGtMzzeccNYf7Z7tiVbIn+GIhXWNxGit60p+30frsYXIMavYEc+zimRNoxpQvF6vJMnpdIZsdzqdwX1er1dZWVmhTSQnKzMzM1jzVTU1Ndq0adOQ7Q0NDUpLSwu7P4/HE3btaKgtHNPTDevAgQPD1pjS85b5gbBrY2ldkRitdYVznMs11o8tjByzih3xOKsLFy6EXRsT7+KpqqpSZWVl8HZPT49yc3NVUlIih8Mx7P39fr88Ho8WL14sm812JVsNMav64JidKxynqkuHrYl2z/ZES1vmB7TheKJ8gYSw7hML6xqJ0VpXOMcZqWg9thA5ZhU74nlWg8+AhGNUA0p2drYkqaOjQ9dee21we0dHh+bNmxes6ezsDLnfxYsX9cUXXwTv/1V2u112u33IdpvNFtHwIq2/XL6B8H7AjpVw1m5Kz75AQti9xNK6IjFa6xqL7/mxfmxh5JhV7IjHWUWynlH9RW0zZsxQdna2Ghsbg9t6enp05MgRuVwuSZLL5VJXV5daW1uDNW+//bYCgYAWLFgwmu0AAIAYFfEVlPPnz+vjjz8O3v7kk0904sQJZWZmKi8vT6tXr9bjjz+u66+/XjNmzNCGDRuUk5Oju+66S5J044036gc/+IEeeugh7dixQ36/XytXrtS9997LO3gAAICkEQSU48eP6/bbbw/eHnxtSEVFhXbv3q1f/OIX6u3t1cMPP6yuri7deuutevPNN5Wamhq8zx//+EetXLlSixYtUmJiosrLy/Xcc8+NwnIAAEA8iDig3HbbbbKsr39rY0JCgjZv3qzNmzd/bU1mZqbq6+sjPTUAABgn+LBAAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjJMc7QYARN/0dfuHrfl0a9kYdAIAX+IKCgAAMA4BBQAAGIeAAgAAjENAAQAAxuFFskCcC+cFsABgGq6gAAAA43AFZYT4VykAAFcOV1AAAIBxuIICICyXumpoT7JUWyjNqj4o30ACv8wNwKjhCgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwTnK0GwAQP6av2z9szadby8bsOABiF1dQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh3fxAIhbvBsIiF1cQQEAAMaJ6hWUuro6Pfnkk/J6vZo7d66ef/55FRYWRrMlAOMMV1kAM0UtoLz88suqrKzUjh07tGDBAj3zzDMqLS1VW1ubsrKyotWWpPD+wgIwfozW3wkEHSB8UQsoTz31lB566CE9+OCDkqQdO3Zo//79+t3vfqd169ZFqy0AMWI8/0NiJGu3J1mqLZRmVR+UbyBBEoEJZotKQOnv71dra6uqqqqC2xITE1VcXKzm5uYh9T6fTz6fL3i7u7tbkvTFF1/I7/cPez6/368LFy7o888/l81mG7Y++WJvOMuIOZ9//vmwNdFee3LA0oULASX7EzUQSAjrPrGwrpGIhXUxr8iEs/YFNY3D1ozkL+5LzSqcfjD2Iv2ZFUvOnTsnSbIsa/hiKwr++c9/WpKsw4cPh2xfu3atVVhYOKT+sccesyTxxRdffPHFF19x8HXmzJlhs0JMvM24qqpKlZWVwduBQEBffPGFpkyZooSE4f/V1tPTo9zcXJ05c0YOh+NKtorLxKxiC/OKHcwqdsTzrCzL0rlz55STkzNsbVQCytVXX62kpCR1dHSEbO/o6FB2dvaQervdLrvdHrJt8uTJEZ/X4XDE3bDjFbOKLcwrdjCr2BGvs8rIyAirLiq/ByUlJUUFBQVqbPzvc62BQECNjY1yuVzRaAkAABgkak/xVFZWqqKiQvPnz1dhYaGeeeYZ9fb2Bt/VAwAAxq+oBZR77rlH//rXv7Rx40Z5vV7NmzdPb775ppxO56ify26367HHHhvyNBHMw6xiC/OKHcwqdjCrLyVYVjjv9QEAABg7fBYPAAAwDgEFAAAYh4ACAACMQ0ABAADGifuAUldXp+nTpys1NVULFizQ0aNHo90SJB06dEh33HGHcnJylJCQoFdffTVkv2VZ2rhxo6699lpNmDBBxcXF+uijj6LT7DhXU1OjW265RZMmTVJWVpbuuusutbW1hdT09fXJ7XZrypQpmjhxosrLy4f8IkZcedu3b9ecOXOCv+DL5XLpjTfeCO5nTubaunWrEhIStHr16uC28T6vuA4oL7/8siorK/XYY4/p/fff19y5c1VaWqrOzs5otzbu9fb2au7cuaqrq7vk/traWj333HPasWOHjhw5ovT0dJWWlqqvr2+MO0VTU5PcbrdaWlrk8Xjk9/tVUlKi3t7/fqDfmjVr9Prrr2vPnj1qamrS2bNntXTp0ih2PT5NnTpVW7duVWtrq44fP66FCxfqzjvv1OnTpyUxJ1MdO3ZML7zwgubMmROyfdzPa1Q+/c9QhYWFltvtDt4eGBiwcnJyrJqamih2ha+SZO3duzd4OxAIWNnZ2daTTz4Z3NbV1WXZ7XbrT3/6UxQ6xP/q7Oy0JFlNTU2WZX05G5vNZu3ZsydY8+GHH1qSrObm5mi1if/vqquusn77298yJ0OdO3fOuv766y2Px2N9//vftx555BHLsnhcWZZlxe0VlP7+frW2tqq4uDi4LTExUcXFxWpubo5iZxjOJ598Iq/XGzK7jIwMLViwgNkZoLu7W5KUmZkpSWptbZXf7w+Z18yZM5WXl8e8omhgYEAvvfSSent75XK5mJOh3G63ysrKQuYi8biSovibZK+0f//73xoYGBjym2mdTqf++te/RqkrhMPr9UrSJWc3uA/REQgEtHr1an3nO9/RrFmzJH05r5SUlCEf4Mm8ouPkyZNyuVzq6+vTxIkTtXfvXuXn5+vEiRPMyTAvvfSS3n//fR07dmzIPh5XcRxQAIw+t9utU6dO6b333ot2K/gaN9xwg06cOKHu7m698sorqqioUFNTU7TbwlecOXNGjzzyiDwej1JTU6PdjpHi9imeq6++WklJSUNe8dzR0aHs7OwodYVwDM6H2Zll5cqV2rdvn9555x1NnTo1uD07O1v9/f3q6uoKqWde0ZGSkqLrrrtOBQUFqqmp0dy5c/Xss88yJ8O0traqs7NTN998s5KTk5WcnKympiY999xzSk5OltPpHPfzituAkpKSooKCAjU2Nga3BQIBNTY2yuVyRbEzDGfGjBnKzs4OmV1PT4+OHDnC7KLAsiytXLlSe/fu1dtvv60ZM2aE7C8oKJDNZguZV1tbm9rb25mXAQKBgHw+H3MyzKJFi3Ty5EmdOHEi+DV//nzdf//9wf8e7/OK66d4KisrVVFRofnz56uwsFDPPPOMent79eCDD0a7tXHv/Pnz+vjjj4O3P/nkE504cUKZmZnKy8vT6tWr9fjjj+v666/XjBkztGHDBuXk5Oiuu+6KXtPjlNvtVn19vV577TVNmjQp+Px3RkaGJkyYoIyMDC1fvlyVlZXKzMyUw+HQqlWr5HK5VFRUFOXux5eqqiotWbJEeXl5OnfunOrr6/Xuu+/q4MGDzMkwkyZNCr6Oa1B6erqmTJkS3D7u5xXttxFdac8//7yVl5dnpaSkWIWFhVZLS0u0W4JlWe+8844lachXRUWFZVlfvtV4w4YNltPptOx2u7Vo0SKrra0tuk2PU5eakyRr165dwZr//Oc/1s9+9jPrqquustLS0qy7777b+uyzz6LX9Dj105/+1Jo2bZqVkpJiXXPNNdaiRYushoaG4H7mZLb/fZuxZTGvBMuyrChlIwAAgEuK29egAACA2EVAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBx/h8LbckIPdLO0wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['textlen'].hist(bins=50)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5060.00000\n",
       "mean        9.63083\n",
       "std         4.25744\n",
       "min         1.00000\n",
       "25%         7.00000\n",
       "50%         9.00000\n",
       "75%        12.00000\n",
       "max        43.00000\n",
       "Name: textlen, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['textlen'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "labels\n",
       "1    2035\n",
       "2    1465\n",
       "0     804\n",
       "3     756\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['labels'].value_counts().plot(kind='bar')\n",
    "plt.show()\n",
    "df['labels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['textlen'] >= 10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JevXVVxUdHa28vDz5fD5lZ2frzTffDDw/JiZG27dvV35+vjwej/r376/58+dr9erVXbsqAADQo4UUUDZu3Hjb63379lVpaalKS0tv2SYtLa1XfboFAAB0Pb6LBwAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwTkgBZe3atXr44Yc1cOBAJSYmas6cOaqpqbG1mTZtmqKiomyPp59+2tbmzJkzys3NVVxcnBITE7Vs2TLduHGj86sBAAC9Qp9QGldUVKigoEAPP/ywbty4oRdeeEFZWVk6efKk+vfvH2i3cOFCrV69OnAcFxcX+HNzc7Nyc3Pldru1f/9+nT9/Xk8++aQcDodefvnlLlgSAADo6UIKKDt37rQdb968WYmJiaqqqtLUqVMD5+Pi4uR2u9vsY9euXTp58qR2796tpKQkjRs3TmvWrNHy5ctVXFys2NjYO1hGz3X/85+12+a7dbl3YSYAAJgjpIBys4aGBklSQkKC7fz777+v9957T263W7Nnz9bKlSsDd1EqKys1ZswYJSUlBdpnZ2crPz9fJ06c0Pjx44PG8fl88vl8gePGxkZJkt/vl9/v78wSgrT254y2uqyv23HGtD9OV68xFK1jh3MOJqEewaiJHfWwox7BIrkmoaw5yrKsO/qXuKWlRb/4xS906dIlffnll4Hz77zzjtLS0pSSkqKjR49q+fLlmjRpkj7++GNJ0qJFi/SnP/1J5eXlgec0NTWpf//+2rFjh3JycoLGKi4uVklJSdD5srIy28tHAADAXE1NTXr88cfV0NAgl8t127Z3fAeloKBAx48ft4UT6S8BpNWYMWOUnJysjIwMnT59WsOHD7+jsYqKilRYWBg4bmxsVGpqqrKystpdYKj8fr+8Xq9WHomWryWqU30dL85ut83o4vJ223Skn+7SWo8ZM2bI4XCEbR6moB7BqIkd9bCjHsEiuSatr4B0xB0FlMWLF2v79u3at2+fhgwZctu2kydPliSdOnVKw4cPl9vt1qFDh2xt6urqJOmW71txOp1yOp1B5x0OR7f9cH0tUfI1dy6gdGRuHRnDhA3cnbXuiahHMGpiRz3sqEewSKxJKOsN6WPGlmVp8eLF2rp1q7744gsNGzas3edUV1dLkpKTkyVJHo9Hx44dU319faCN1+uVy+VSenp6KNMBAAC9VEh3UAoKClRWVqZPP/1UAwcOVG1trSQpPj5e/fr10+nTp1VWVqZZs2Zp8ODBOnr0qJYuXaqpU6dq7NixkqSsrCylp6friSee0Pr161VbW6sVK1aooKCgzbskAAAg8oR0B2XDhg1qaGjQtGnTlJycHHj84Q9/kCTFxsZq9+7dysrK0qhRo/R3f/d3ysvL07Zt2wJ9xMTEaPv27YqJiZHH49Ff//Vf68knn7T93hQAABDZQrqD0t4HflJTU1VRUdFuP2lpadqxY0coQwMAgAjCd/EAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDghfRcPwuP+5z9rt81363LvwkwAALg7uIMCAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOCEFlLVr1+rhhx/WwIEDlZiYqDlz5qimpsbW5tq1ayooKNDgwYM1YMAA5eXlqa6uztbmzJkzys3NVVxcnBITE7Vs2TLduHGj86sBAAC9QkgBpaKiQgUFBTpw4IC8Xq/8fr+ysrJ09erVQJulS5dq27Zt2rJliyoqKnTu3DnNnTs3cL25uVm5ubm6fv269u/fr3fffVebN2/WqlWrum5VAACgR+sTSuOdO3fajjdv3qzExERVVVVp6tSpamho0MaNG1VWVqbp06dLkjZt2qQHH3xQBw4c0JQpU7Rr1y6dPHlSu3fvVlJSksaNG6c1a9Zo+fLlKi4uVmxsbNetDgAA9EghBZSbNTQ0SJISEhIkSVVVVfL7/crMzAy0GTVqlIYOHarKykpNmTJFlZWVGjNmjJKSkgJtsrOzlZ+frxMnTmj8+PFB4/h8Pvl8vsBxY2OjJMnv98vv93dmCUFa+3NGW13W1+04Yzo/TkfH6ky/3dV/T0M9glETO+phRz2CRXJNQlnzHQeUlpYWLVmyRD/72c80evRoSVJtba1iY2M1aNAgW9ukpCTV1tYG2vwwnLReb73WlrVr16qkpCTo/K5duxQXF3enS7itNRNbOt3Hjh072m2zflKnh+nwWJ3h9Xq7tf+ehnoEoyZ21MOOegSLxJo0NTV1uO0dB5SCggIdP35cX3755Z120WFFRUUqLCwMHDc2Nio1NVVZWVlyuVxdOpbf75fX69XKI9HytUR1qq/jxdntthldXN6pMUIZ60601mPGjBlyOBzdMkZPQj2CURM76mFHPYJFck1aXwHpiDsKKIsXL9b27du1b98+DRkyJHDe7Xbr+vXrunTpku0uSl1dndxud6DNoUOHbP21fsqntc3NnE6nnE5n0HmHw9FtP1xfS5R8zZ0LKB2ZW2fHCGWszvYfaf8h3Q71CEZN7KiHHfUIFok1CWW9IX2Kx7IsLV68WFu3btUXX3yhYcOG2a5PmDBBDodDe/bsCZyrqanRmTNn5PF4JEkej0fHjh1TfX19oI3X65XL5VJ6enoo0wEAAL1USHdQCgoKVFZWpk8//VQDBw4MvGckPj5e/fr1U3x8vBYsWKDCwkIlJCTI5XLpmWeekcfj0ZQpUyRJWVlZSk9P1xNPPKH169ertrZWK1asUEFBQZt3SQAAQOQJKaBs2LBBkjRt2jTb+U2bNumpp56SJL366quKjo5WXl6efD6fsrOz9eabbwbaxsTEaPv27crPz5fH41H//v01f/58rV69unMrAQAAvUZIAcWy2v9IbN++fVVaWqrS0tJbtklLS+v2T50AAICeq1O/BwU9y/3Pf9Zum+/W5d6FmQAAcHt8WSAAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDghB5R9+/Zp9uzZSklJUVRUlD755BPb9aeeekpRUVG2x8yZM21tLl68qHnz5snlcmnQoEFasGCBrly50qmFAACA3iPkgHL16lU99NBDKi0tvWWbmTNn6vz584HHBx98YLs+b948nThxQl6vV9u3b9e+ffu0aNGi0GcPAAB6pT6hPiEnJ0c5OTm3beN0OuV2u9u89vXXX2vnzp06fPiwJk6cKEn6/e9/r1mzZul3v/udUlJSQp0SAADoZbrlPSh79+5VYmKiRo4cqfz8fF24cCFwrbKyUoMGDQqEE0nKzMxUdHS0Dh482B3TAQAAPUzId1DaM3PmTM2dO1fDhg3T6dOn9cILLygnJ0eVlZWKiYlRbW2tEhMT7ZPo00cJCQmqra1ts0+fzyefzxc4bmxslCT5/X75/f4unX9rf85oq8v6uh1nTOfH6cqxbu6n9bir69xTUY9g1MSOethRj2CRXJNQ1hxlWdYd/wsZFRWlrVu3as6cObds85//+Z8aPny4du/erYyMDL388st69913VVNTY2uXmJiokpIS5efnB/VRXFyskpKSoPNlZWWKi4u70+kDAIC7qKmpSY8//rgaGhrkcrlu27bL76Dc7Ec/+pHuvfdenTp1ShkZGXK73aqvr7e1uXHjhi5evHjL960UFRWpsLAwcNzY2KjU1FRlZWW1u8BQ+f1+eb1erTwSLV9LVKf6Ol6c3W6b0cXlnRqjq8e6uZ/WesyYMUMOh+OO59dbUI9g1MSOethRj2CRXJPWV0A6otsDyvfff68LFy4oOTlZkuTxeHTp0iVVVVVpwoQJkqQvvvhCLS0tmjx5cpt9OJ1OOZ3OoPMOh6Pbfri+lij5mjsXUDoyt86O0dVj3aqf7qx1T0Q9glETO+phRz2CRWJNQllvyAHlypUrOnXqVOD422+/VXV1tRISEpSQkKCSkhLl5eXJ7Xbr9OnTeu655zRixAhlZ//l/5k/+OCDmjlzphYuXKi33npLfr9fixcv1mOPPcYneAAAgKQ7+BTPkSNHNH78eI0fP16SVFhYqPHjx2vVqlWKiYnR0aNH9Ytf/EI//vGPtWDBAk2YMEH//u//brsD8v7772vUqFHKyMjQrFmz9Mgjj+idd97pulUBAIAeLeQ7KNOmTdPt3ldbXt7++xwSEhJUVlYW6tAAACBC8F08AADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGKdPuCcAs9z//Ge2Y2eMpfWTpNHF5fI1R0mSvluXG46pAQAiCHdQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADBOyAFl3759mj17tlJSUhQVFaVPPvnEdt2yLK1atUrJycnq16+fMjMz9c0339jaXLx4UfPmzZPL5dKgQYO0YMECXblypVMLAQAAvUfIAeXq1at66KGHVFpa2ub19evX64033tBbb72lgwcPqn///srOzta1a9cCbebNm6cTJ07I6/Vq+/bt2rdvnxYtWnTnqwAAAL1Kn1CfkJOTo5ycnDavWZal1157TStWrNCjjz4qSfq3f/s3JSUl6ZNPPtFjjz2mr7/+Wjt37tThw4c1ceJESdLvf/97zZo1S7/73e+UkpLSieUAAIDeIOSAcjvffvutamtrlZmZGTgXHx+vyZMnq7KyUo899pgqKys1aNCgQDiRpMzMTEVHR+vgwYP65S9/GdSvz+eTz+cLHDc2NkqS/H6//H5/Vy4h0J8z2uqyvm7HGdP5cbpzrNY6/LAeXV3znqR17ZFcg5tREzvqYUc9gkVyTUJZc5cGlNraWklSUlKS7XxSUlLgWm1trRITE+2T6NNHCQkJgTY3W7t2rUpKSoLO79q1S3FxcV0x9SBrJrZ0uo8dO3a022b9pE4Pc1fG+mE9OjJWb+f1esM9BeNQEzvqYUc9gkViTZqamjrctksDSncpKipSYWFh4LixsVGpqanKysqSy+Xq0rH8fr+8Xq9WHomWryWqU30dL85ut83o4vJOjdHdYzmjLa2Z2GKrR0fG6q1a98eMGTPkcDjCPR0jUBM76mFHPYJFck1aXwHpiC4NKG63W5JUV1en5OTkwPm6ujqNGzcu0Ka+vt72vBs3bujixYuB59/M6XTK6XQGnXc4HN32w/W1RMnX3LmA0pG5dXaMuzXWD+sRaf9BtaU7915PRU3sqIcd9QgWiTUJZb1d+ntQhg0bJrfbrT179gTONTY26uDBg/J4PJIkj8ejS5cuqaqqKtDmiy++UEtLiyZPntyV0wEAAD1UyHdQrly5olOnTgWOv/32W1VXVyshIUFDhw7VkiVL9NJLL+mBBx7QsGHDtHLlSqWkpGjOnDmSpAcffFAzZ87UwoUL9dZbb8nv92vx4sV67LHH+ARPhLn/+c/abfPduty7MBMAgGlCDihHjhzRz3/+88Bx63tD5s+fr82bN+u5557T1atXtWjRIl26dEmPPPKIdu7cqb59+wae8/7772vx4sXKyMhQdHS08vLy9MYbb3TBcmCKjoQPAABuJeSAMm3aNFnWrT+uGhUVpdWrV2v16tW3bJOQkKCysrJQhwYAABGC7+IBAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMbp8oBSXFysqKgo22PUqFGB69euXVNBQYEGDx6sAQMGKC8vT3V1dV09DQAA0IN1yx2Un/zkJzp//nzg8eWXXwauLV26VNu2bdOWLVtUUVGhc+fOae7cud0xDQAA0EP16ZZO+/SR2+0OOt/Q0KCNGzeqrKxM06dPlyRt2rRJDz74oA4cOKApU6Z0x3QAAEAP0y0B5ZtvvlFKSor69u0rj8ejtWvXaujQoaqqqpLf71dmZmag7ahRozR06FBVVlbeMqD4fD75fL7AcWNjoyTJ7/fL7/d36dxb+3NGW13W1+04Yzo/TneO1VqHH9bDtHXdTa3zMW1e4URN7KiHHfUIFsk1CWXNUZZldc2/JP/r888/15UrVzRy5EidP39eJSUl+vOf/6zjx49r27Zt+s1vfmMLG5I0adIk/fznP9c//MM/tNlncXGxSkpKgs6XlZUpLi6uK6cPAAC6SVNTkx5//HE1NDTI5XLdtm2XB5SbXbp0SWlpaXrllVfUr1+/Owoobd1BSU1N1X//93+3u8BQ+f1+eb1erTwSLV9LVKf6Ol6c3W6b0cXlnRqju8dyRltaM7HFVg/T1nU3te6PGTNmyOFwhHs6RqAmdtTDjnoEi+SaNDY26t577+1QQOmWl3h+aNCgQfrxj3+sU6dOacaMGbp+/bouXbqkQYMGBdrU1dW1+Z6VVk6nU06nM+i8w+Hoth+uryVKvubOBZSOzK2zY9ytsX5YD9PWFQ7dufd6KmpiRz3sqEewSKxJKOvt9t+DcuXKFZ0+fVrJycmaMGGCHA6H9uzZE7heU1OjM2fOyOPxdPdUAABAD9Hld1D+/u//XrNnz1ZaWprOnTunF198UTExMfr1r3+t+Ph4LViwQIWFhUpISJDL5dIzzzwjj8fDJ3jQre5//rN223y3LvcuzAQA0BFdHlC+//57/frXv9aFCxd033336ZFHHtGBAwd03333SZJeffVVRUdHKy8vTz6fT9nZ2XrzzTe7ehoAAKAH6/KA8uGHH972et++fVVaWqrS0tKuHhoAAPQSfBcPAAAwDgEFAAAYh4ACAACMQ0ABAADG6fZf1AZ0t458hBgA0LNwBwUAABiHgAIAAIxDQAEAAMbhPShACEYXl2v9pL/8762+EJFfmQ8AnccdFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADj9An3BAC07f7nP2u3zXfrcu/CTADg7uMOCgAAMA53UIAuxp0PAOg87qAAAADjEFAAAIBxCCgAAMA4BBQAAGAc3iQLgDf2AjAOAQVAh9wqxDhjLK2fJI0uLlfNb//PXZ4VgN4qrC/xlJaW6v7771ffvn01efJkHTp0KJzTAQAAhghbQPnDH/6gwsJCvfjii/rqq6/00EMPKTs7W/X19eGaEgAAMETYXuJ55ZVXtHDhQv3mN7+RJL311lv67LPP9K//+q96/vnnwzUtoEfpie8d6cicO8K0dQHoWmEJKNevX1dVVZWKiooC56Kjo5WZmanKysqg9j6fTz6fL3Dc0NAgSbp48aL8fn+Xzs3v96upqUl9/NFqbonqVF8XLlxot02fG1c7NUZ3j9WnxVJTU4utHr1hXXc8lv9qUD26baweUsMf7hHT1jV57Z4uGetgUUaHx3JGW1oxvkXj/u/H8t20RzrST2/T+nfqhQsX5HA4Ot1fR36mpte5q2tyJ8JVx8uXL0uSLMtqv7EVBn/+858tSdb+/ftt55ctW2ZNmjQpqP2LL75oSeLBgwcPHjx49ILH2bNn280KPeJTPEVFRSosLAwct7S06OLFixo8eLCiojp3l+NmjY2NSk1N1dmzZ+Vyubq0756IethRj2DUxI562FGPYJFcE8uydPnyZaWkpLTbNiwB5d5771VMTIzq6ups5+vq6uR2u4PaO51OOZ1O27lBgwZ15xTlcrkibuPcDvWwox7BqIkd9bCjHsEitSbx8fEdaheWT/HExsZqwoQJ2rPn/78G1tLSoj179sjj8YRjSgAAwCBhe4mnsLBQ8+fP18SJEzVp0iS99tprunr1auBTPQAAIHKFLaD86le/0n/9139p1apVqq2t1bhx47Rz504lJSWFa0qS/vJy0osvvhj0klKkoh521CMYNbGjHnbUIxg16Zgoy+rIZ30AAADuHr7NGAAAGIeAAgAAjENAAQAAxiGgAAAA40RkQNm3b59mz56tlJQURUVF6ZNPPrFdtyxLq1atUnJysvr166fMzEx988034ZnsXdJeTZ566ilFRUXZHjNnzgzPZO+CtWvX6uGHH9bAgQOVmJioOXPmqKamxtbm2rVrKigo0ODBgzVgwADl5eUF/fLB3qIj9Zg2bVrQHnn66afDNOPutWHDBo0dOzbwi7Y8Ho8+//zzwPVI2hut2qtJJO2Pm61bt05RUVFasmRJ4Fwk7pFQRWRAuXr1qh566CGVlpa2eX39+vV644039NZbb+ngwYPq37+/srOzde3atbs807unvZpI0syZM3X+/PnA44MPPriLM7y7KioqVFBQoAMHDsjr9crv9ysrK0tXr/7/L7pbunSptm3bpi1btqiiokLnzp3T3Llzwzjr7tORekjSwoULbXtk/fr1YZpx9xoyZIjWrVunqqoqHTlyRNOnT9ejjz6qEydOSIqsvdGqvZpIkbM/fujw4cN6++23NXbsWNv5SNwjIeuSb//rwSRZW7duDRy3tLRYbrfb+sd//MfAuUuXLllOp9P64IMPwjDDu+/mmliWZc2fP9969NFHwzIfE9TX11uSrIqKCsuy/rInHA6HtWXLlkCbr7/+2pJkVVZWhmuad83N9bAsy/qrv/or69lnnw3fpMLsnnvusf7lX/4l4vfGD7XWxLIic39cvnzZeuCBByyv12tbP3ukYyLyDsrtfPvtt6qtrVVmZmbgXHx8vCZPnqzKysowziz89u7dq8TERI0cOVL5+fkd+rr73qKhoUGSlJCQIEmqqqqS3++37ZNRo0Zp6NChEbFPbq5Hq/fff1/33nuvRo8eraKiIjU1NYVjendVc3OzPvzwQ129elUejyfi94YUXJNWkbY/CgoKlJuba9sLEn9/dFSP+Dbju6m2tlaSgn6jbVJSUuBaJJo5c6bmzp2rYcOG6fTp03rhhReUk5OjyspKxcTEhHt63aqlpUVLlizRz372M40ePVrSX/ZJbGxs0JdWRsI+aasekvT4448rLS1NKSkpOnr0qJYvX66amhp9/PHHYZxt9zl27Jg8Ho+uXbumAQMGaOvWrUpPT1d1dXXE7o1b1USKvP3x4Ycf6quvvtLhw4eDrkXy3x+hIKCgQx577LHAn8eMGaOxY8dq+PDh2rt3rzIyMsI4s+5XUFCg48eP68svvwz3VIxwq3osWrQo8OcxY8YoOTlZGRkZOn36tIYPH363p9ntRo4cqerqajU0NOijjz7S/PnzVVFREe5phdWtapKenh5R++Ps2bN69tln5fV61bdv33BPp8fiJZ6buN1uSQp6N3VdXV3gGqQf/ehHuvfee3Xq1KlwT6VbLV68WNu3b9cf//hHDRkyJHDe7Xbr+vXrunTpkq19b98nt6pHWyZPnixJvXaPxMbGasSIEZowYYLWrl2rhx56SK+//nrE7g3p1jVpS2/eH1VVVaqvr9dPf/pT9enTR3369FFFRYXeeOMN9enTR0lJSRG7R0JBQLnJsGHD5Ha7tWfPnsC5xsZGHTx40PZaaqT7/vvvdeHCBSUnJ4d7Kt3CsiwtXrxYW7du1RdffKFhw4bZrk+YMEEOh8O2T2pqanTmzJleuU/aq0dbqqurJanX7pGbtbS0yOfzRdzeuJ3WmrSlN++PjIwMHTt2TNXV1YHHxIkTNW/evMCf2SPti8iXeK5cuWJL7d9++62qq6uVkJCgoUOHasmSJXrppZf0wAMPaNiwYVq5cqVSUlI0Z86c8E26m92uJgkJCSopKVFeXp7cbrdOnz6t5557TiNGjFB2dnYYZ919CgoKVFZWpk8//VQDBw4MvC4cHx+vfv36KT4+XgsWLFBhYaESEhLkcrn0zDPPyOPxaMqUKWGefddrrx6nT59WWVmZZs2apcGDB+vo0aNaunSppk6dGvTxyt6gqKhIOTk5Gjp0qC5fvqyysjLt3btX5eXlEbc3Wt2uJpG2PwYOHGh7f5Yk9e/fX4MHDw6cj8Q9ErJwf4woHP74xz9akoIe8+fPtyzrLx81XrlypZWUlGQ5nU4rIyPDqqmpCe+ku9ntatLU1GRlZWVZ9913n+VwOKy0tDRr4cKFVm1tbbin3W3aqoUka9OmTYE2//M//2P97d/+rXXPPfdYcXFx1i9/+Uvr/Pnz4Zt0N2qvHmfOnLGmTp1qJSQkWE6n0xoxYoS1bNkyq6GhIbwT7yZ/8zd/Y6WlpVmxsbHWfffdZ2VkZFi7du0KXI+kvdHqdjWJtP3Rlps/Zh2JeyRUUZZlWXczEAEAALSH96AAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYJz/B5alD5uGm/GoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['textlen'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data_cleaned.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>label</th>\n",
       "      <th>text</th>\n",
       "      <th>textlen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>focus should go to the web content after acti...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>Aria-owns crash if the ID of an owned element ...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Regression in presubmit.py checking of accessi...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>Label names when they embed a control are not ...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>TalkBack doesn't read the text of the popup di...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label                                               text  textlen\n",
       "1       2   focus should go to the web content after acti...       11\n",
       "8       0  Aria-owns crash if the ID of an owned element ...       10\n",
       "9       1  Regression in presubmit.py checking of accessi...       11\n",
       "14      2  Label names when they embed a control are not ...       10\n",
       "15      2  TalkBack doesn't read the text of the popup di...       12"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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