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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"import numpy as np \n",
"import nltk\n",
"import os\n",
"import sklearn\n",
"import parquet"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"data1 = pd.read_parquet(\"news-00000-of-00007-0ff1ec222cd690f2.parquet\")\n",
"data2 = pd.read_parquet(\"news-00001-of-00007-7c273f5de9017dc5.parquet\")\n",
"data3 = pd.read_parquet(\"telegram_blogs-00000-of-00001-80087cf60adbe6d4.parquet\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# Extract the 'text' column from each DataFrame\n",
"texts1 = data1['text']\n",
"texts2 = data2['text']\n",
"texts3 = data3['text']\n",
"\n",
"# Concatenate the 'text' columns from all three DataFrames\n",
"all_texts = pd.concat([texts1, texts2, texts3], ignore_index=True)\n",
"data = pd.DataFrame(all_texts, columns=['text'])\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 643523.000000\n",
"mean 1225.167071\n",
"std 2613.174490\n",
"min 0.000000\n",
"25% 271.000000\n",
"50% 689.000000\n",
"75% 1362.000000\n",
"max 299171.000000\n",
"Name: text, dtype: float64\n"
]
}
],
"source": [
"# Calculate the length of each text entry\n",
"text_lengths = data['text'].str.len()\n",
"\n",
"# Display the distribution of text lengths\n",
"length_distribution = text_lengths.describe()\n",
"\n",
"# Print the distribution\n",
"print(length_distribution)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import string\n",
"\n",
"def random_letter():\n",
" \"\"\"Returns a random alphanumeric character.\"\"\"\n",
" return random.choice(string.ascii_letters + string.digits)\n",
"\n",
"def replace_random_letters(word, pct=0.15):\n",
" \"\"\"Replaces random letters in a word with a given probability.\"\"\"\n",
" if random.random() < pct:\n",
" char_pos = random.choice(range(len(word)))\n",
" return word[:char_pos] + random_letter() + word[char_pos + 1:]\n",
" else:\n",
" return word\n",
"\n",
"def misspell_text(text, pct=0.15, last_letter_error_pct=0.20):\n",
" \"\"\"Generates a misspelled version of the input text.\"\"\"\n",
" words = text.split()\n",
" misspelled_words = [replace_random_letters(word, pct) for word in words]\n",
" \n",
" # Apply last letter error with a different probability\n",
" for i, word in enumerate(misspelled_words):\n",
" if random.random() < last_letter_error_pct:\n",
" if len(word) > 1: # Ensure word has more than 1 character\n",
" misspelled_words[i] = word[:-1] + random_letter()\n",
" \n",
" return ' '.join(misspelled_words)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"data.rename(columns={'text':'ground_truth'}, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Index' object has no attribute '_format_flat'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/IPython/core/formatters.py:343\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 341\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 343\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:1053\u001b[0m, in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshape\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mint\u001b[39m]:\n\u001b[1;32m 1038\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;124;03m Return a tuple representing the dimensionality of the DataFrame.\u001b[39;00m\n\u001b[1;32m 1040\u001b[0m \n\u001b[1;32m 1041\u001b[0m \u001b[38;5;124;03m See Also\u001b[39;00m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;124;03m --------\u001b[39;00m\n\u001b[1;32m 1043\u001b[0m \u001b[38;5;124;03m ndarray.shape : Tuple of array dimensions.\u001b[39;00m\n\u001b[1;32m 1044\u001b[0m \n\u001b[1;32m 1045\u001b[0m \u001b[38;5;124;03m Examples\u001b[39;00m\n\u001b[1;32m 1046\u001b[0m \u001b[38;5;124;03m --------\u001b[39;00m\n\u001b[1;32m 1047\u001b[0m \u001b[38;5;124;03m >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})\u001b[39;00m\n\u001b[1;32m 1048\u001b[0m \u001b[38;5;124;03m >>> df.shape\u001b[39;00m\n\u001b[1;32m 1049\u001b[0m \u001b[38;5;124;03m (2, 2)\u001b[39;00m\n\u001b[1;32m 1050\u001b[0m \n\u001b[1;32m 1051\u001b[0m \u001b[38;5;124;03m >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],\u001b[39;00m\n\u001b[1;32m 1052\u001b[0m \u001b[38;5;124;03m ... 'col3': [5, 6]})\u001b[39;00m\n\u001b[0;32m-> 1053\u001b[0m \u001b[38;5;124;03m >>> df.shape\u001b[39;00m\n\u001b[1;32m 1054\u001b[0m \u001b[38;5;124;03m (2, 3)\u001b[39;00m\n\u001b[1;32m 1055\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 1056\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex), \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns)\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/format.py:1102\u001b[0m, in \u001b[0;36mto_html\u001b[0;34m(self, buf, encoding, classes, notebook, border, table_id, render_links)\u001b[0m\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mformat_array\u001b[39m(\n\u001b[1;32m 1080\u001b[0m values: ArrayLike,\n\u001b[1;32m 1081\u001b[0m formatter: Callable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1090\u001b[0m fallback_formatter: Callable \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1091\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m 1092\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;124;03m Format an array for printing.\u001b[39;00m\n\u001b[1;32m 1094\u001b[0m \n\u001b[1;32m 1095\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[1;32m 1096\u001b[0m \u001b[38;5;124;03m ----------\u001b[39;00m\n\u001b[1;32m 1097\u001b[0m \u001b[38;5;124;03m values : np.ndarray or ExtensionArray\u001b[39;00m\n\u001b[1;32m 1098\u001b[0m \u001b[38;5;124;03m formatter\u001b[39;00m\n\u001b[1;32m 1099\u001b[0m \u001b[38;5;124;03m float_format\u001b[39;00m\n\u001b[1;32m 1100\u001b[0m \u001b[38;5;124;03m na_rep\u001b[39;00m\n\u001b[1;32m 1101\u001b[0m \u001b[38;5;124;03m digits\u001b[39;00m\n\u001b[0;32m-> 1102\u001b[0m \u001b[38;5;124;03m space\u001b[39;00m\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;124;03m justify\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m \u001b[38;5;124;03m decimal\u001b[39;00m\n\u001b[1;32m 1105\u001b[0m \u001b[38;5;124;03m leading_space : bool, optional, default True\u001b[39;00m\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;124;03m Whether the array should be formatted with a leading space.\u001b[39;00m\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;124;03m When an array as a column of a Series or DataFrame, we do want\u001b[39;00m\n\u001b[1;32m 1108\u001b[0m \u001b[38;5;124;03m the leading space to pad between columns.\u001b[39;00m\n\u001b[1;32m 1109\u001b[0m \n\u001b[1;32m 1110\u001b[0m \u001b[38;5;124;03m When formatting an Index subclass\u001b[39;00m\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;124;03m (e.g. IntervalIndex._get_values_for_csv), we don't want the\u001b[39;00m\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;124;03m leading space since it should be left-aligned.\u001b[39;00m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;124;03m fallback_formatter\u001b[39;00m\n\u001b[1;32m 1114\u001b[0m \n\u001b[1;32m 1115\u001b[0m \u001b[38;5;124;03m Returns\u001b[39;00m\n\u001b[1;32m 1116\u001b[0m \u001b[38;5;124;03m -------\u001b[39;00m\n\u001b[1;32m 1117\u001b[0m \u001b[38;5;124;03m List[str]\u001b[39;00m\n\u001b[1;32m 1118\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 1119\u001b[0m fmt_klass: \u001b[38;5;28mtype\u001b[39m[_GenericArrayFormatter]\n\u001b[1;32m 1120\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(values\u001b[38;5;241m.\u001b[39mdtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/html.py:88\u001b[0m, in \u001b[0;36mHTMLFormatter.to_string\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto_string\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m---> 88\u001b[0m lines \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m lines):\n\u001b[1;32m 90\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mstr\u001b[39m(x) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m lines]\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/html.py:644\u001b[0m, in \u001b[0;36mNotebookFormatter.render\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m<div>\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 643\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite_style()\n\u001b[0;32m--> 644\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m</div>\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39melements\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/html.py:94\u001b[0m, in \u001b[0;36mHTMLFormatter.render\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrender\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[0;32m---> 94\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_write_table\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshould_show_dimensions:\n\u001b[1;32m 97\u001b[0m by \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mchr\u001b[39m(\u001b[38;5;241m215\u001b[39m) \u001b[38;5;66;03m# × # noqa: RUF003\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/html.py:267\u001b[0m, in \u001b[0;36mHTMLFormatter._write_table\u001b[0;34m(self, indent)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite(\n\u001b[1;32m 262\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m<table\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mborder_attr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m class=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(_classes)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mid_section\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m>\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 263\u001b[0m indent,\n\u001b[1;32m 264\u001b[0m )\n\u001b[1;32m 266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt\u001b[38;5;241m.\u001b[39mheader \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshow_row_idx_names:\n\u001b[0;32m--> 267\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_write_header\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindent_delta\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_write_body(indent \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindent_delta)\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m</table>\u001b[39m\u001b[38;5;124m\"\u001b[39m, indent)\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/html.py:403\u001b[0m, in \u001b[0;36mHTMLFormatter._write_header\u001b[0;34m(self, indent)\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m<thead>\u001b[39m\u001b[38;5;124m\"\u001b[39m, indent)\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt\u001b[38;5;241m.\u001b[39mheader:\n\u001b[0;32m--> 403\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_write_col_header\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindent\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindent_delta\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshow_row_idx_names:\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_write_row_header(indent \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindent_delta)\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/html.py:383\u001b[0m, in \u001b[0;36mHTMLFormatter._write_col_header\u001b[0;34m(self, indent)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 382\u001b[0m row\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 383\u001b[0m row\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_columns_formatted_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 384\u001b[0m align \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfmt\u001b[38;5;241m.\u001b[39mjustify\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_truncated_horizontally:\n",
"File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/io/formats/html.py:611\u001b[0m, in \u001b[0;36mNotebookFormatter._get_columns_formatted_values\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 609\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_columns_formatted_values\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m 610\u001b[0m \u001b[38;5;66;03m# only reached with non-Multi Index\u001b[39;00m\n\u001b[0;32m--> 611\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[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_format_flat\u001b[49m(include_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Index' object has no attribute '_format_flat'"
]
},
{
"data": {
"text/plain": [
" ground_truth\n",
"0 «Toshshahartransxizmat» AJ Axborot xizmati jam...\n",
"1 Oʻzbekiston Respublikasi Prezidenti Shavkat Mi...\n",
"2 Oʻzbekistonning AQSHdagi elchisi Javlon Vahobo...\n",
"3 Oliy Majlisning Inson huquqlari boʻyicha vakil...\n",
"4 Bu haqda Agentlik axborot xizmati xabar berdi....\n",
"... ...\n",
"643518 Марказий Осиё давлатлари бўйлаб эркин ҳаракатл...\n",
"643519 Олий таълим муассасаларида \\nКоррупцияга қар...\n",
"643520 Қирғизистонда вазир лавозимига тайинланганид...\n",
"643521 Исроил ва Фаластин ўқ отишни тўхтатишди. \"Масж...\n",
"643522 АҚШ 20 та давлатга ҳарбий иншоотлар учун 240...\n",
"\n",
"[643523 rows x 1 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Apply the function to create misspelled texts\n",
"data['sample_misspelled'] = data['ground_truth'].apply(lambda x: misspell_text(x))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ground_truth \\\n",
"0 «Toshshahartransxizmat» AJ Axborot xizmati jam... \n",
"1 Oʻzbekiston Respublikasi Prezidenti Shavkat Mi... \n",
"2 Oʻzbekistonning AQSHdagi elchisi Javlon Vahobo... \n",
"3 Oliy Majlisning Inson huquqlari boʻyicha vakil... \n",
"4 Bu haqda Agentlik axborot xizmati xabar berdi.... \n",
"... ... \n",
"643518 Марказий Осиё давлатлари бўйлаб эркин ҳаракатл... \n",
"643519 Олий таълим муассасаларида \\nКоррупцияга қар... \n",
"643520 Қирғизистонда вазир лавозимига тайинланганид... \n",
"643521 Исроил ва Фаластин ўқ отишни тўхтатишди. \"Масж... \n",
"643522 АҚШ 20 та давлатга ҳарбий иншоотлар учун 240... \n",
"\n",
" sample_misspelled \n",
"0 «Toshshahartransxizmat» A6 Axborot xizmatG jam... \n",
"1 Oʻzbekiston Respublikasi Prezidenti Shavkat Mi... \n",
"2 Oʻzbekistonnin6 AQSHdagi elchisi Javlog Vahobo... \n",
"3 Oliy Majlisning Inson huquqlar2 boʻyicha Makil... \n",
"4 Bu haqda AgentliU axlorot xizmal9 xabar berdi.... \n",
"... ... \n",
"643518 Марказиc ОсGё давлатлаIи бўйлаб эркин ҳаракатл... \n",
"643519 yОлиe таъcиQ муассасаларида КоррупциягX қарш4... \n",
"643520 Қирғизистонда вазиP лавозимига тайинланганид... \n",
"643521 Исроил tf Фаластиt ўқ отишни тўхтатиPдиA \"Масж... \n",
"643522 АҚШ 20 тB давлатга ҳарбиn иншоотлар учун 240... \n",
"\n",
"[643523 rows x 2 columns]\n"
]
}
],
"source": [
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"expanded_data = pd.concat([data['ground_truth'], data['ground_truth']], ignore_index=True).to_frame(name='ground_truth')\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"\n",
"def creative_misspell(text):\n",
" \"\"\"Apply creative misspelling strategies to the text.\"\"\"\n",
" words = text.split()\n",
" misspelled_words = []\n",
" for word in words:\n",
" # Example of a simple typographical error\n",
" if random.random() < 0.05: # Apply with 5% probability\n",
" word = word.replace('the', 'teh')\n",
" \n",
" # Example of omitting letters\n",
" if random.random() < 0.05 and len(word) > 3:\n",
" omit_index = random.randint(1, len(word) - 2) # Avoid first and last character\n",
" word = word[:omit_index] + word[omit_index + 1:]\n",
" \n",
" # Add more rules as needed\n",
" \n",
" misspelled_words.append(word)\n",
" \n",
" return ' '.join(misspelled_words)\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"expanded_data['sample_misspelled'] = expanded_data['ground_truth'].apply(creative_misspell)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|