inspect_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "from utils import  *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rste = load_dataset(\"razhan/rste\", split=\"train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rste = rste.to_pandas()\n",
    "df = df_rste\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clean_text'] = df['text'].apply(process_text)\n",
    "\n",
    "# df['contains_non_kurdish'] = df[\"text\"].apply(contains_non_kurdish_characters)\n",
    "# print(df['contains_non_kurdish'].sum())\n",
    "# df[df['contains_non_kurdish'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clean_text'] = df['clean_text'].apply(keep_kurdish_characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['contains_non_kurdish'] == False]['clean_text'].to_csv(\"data/data.ckb.txt\", index=False, header=False)\n",
    "df['clean_text'].to_csv(\"data/data.ckb.txt\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['text'].str.contains('ھ')]\n",
    "indices_with_substring = df[df['text'].str.contains('ھ')].index\n",
    "# print(indices_with_substring)\n",
    "df.loc[indices_with_substring]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_text = ''.join(df[\"text\"])\n",
    "\n",
    "unique_characters = set(all_text)\n",
    "\n",
    "print(\"Unique characters:\", unique_characters)\n",
    "print(\"Number of unique characters:\", len(unique_characters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_text = ''.join(df[\"clean_text\"])\n",
    "# all_text = ''.join(df[df['contains_non_kurdish'] == False]['clean_text'])\n",
    "unique_characters = set(all_text)\n",
    "unique_punctuations = extract_punctuation(all_text)\n",
    "print(\"Unique characters:\", unique_characters)\n",
    "print(\"Number of unique characters:\", len(unique_characters))\n",
    "print(\"Unique punctuations:\", unique_punctuations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['contains_non_kurdish'] = df[\"text\"].apply(contains_non_kurdish_characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(unique_punctuations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(\"asosoft_test_punc.csv\")\n",
    "# df['summary'] = df['summary'].apply(process_text)\n",
    "# df.to_csv(\"asosoft_test_clean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oscar_dataset = load_dataset(\"oscar-corpus/OSCAR-2301\", language=\"ckb\", split='train', token=True)\n",
    "wiki_dataset = load_dataset(\"wikipedia\", language=\"ckb\", date=\"20231120\", split='train', beam_runner='DirectRunner')\n",
    "\n",
    "df_oscar = oscar_dataset.to_pandas()\n",
    "df_wiki = wiki_dataset.to_pandas()\n",
    "df = pd.concat([df_oscar, df_wiki], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"text\"] = df[\"text\"].apply(process_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# text = df[\"text\"].str.cat(sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clean_text'] = df['text'].apply(keep_kurdish_characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"clean_text\"] = df[\"clean_text\"].apply(process_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['contains_non_kurdish'] = df[\"clean_text\"].apply(contains_non_kurdish_characters)\n",
    "print(df['contains_non_kurdish'].sum())\n",
    "# df[df['contains_non_kurdish'] == True].iloc[0]['clean_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['repeated_ngram'] = df['clean_text'].apply(lambda x: contains_repeated_ngram(x, 10))\n",
    "print(df['repeated_ngram'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows where repeated_ngram are True\n",
    "df = df[df['repeated_ngram'] == False]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['repeated_ngram'] == True].iloc[0]['clean_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_text = \"\".join(df[\"clean_text\"])\n",
    "# all_text = ''.join(df[df['contains_non_kurdish'] == False]['clean_text'])\n",
    "unique_characters = set(all_text)\n",
    "unique_punctuations = extract_punctuation(all_text)\n",
    "print(\"Unique characters:\", unique_characters)\n",
    "print(\"Number of unique characters:\", len(unique_characters))\n",
    "print(\"Unique punctuations:\", unique_punctuations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import sent_tokenize\n",
    "data = []\n",
    "for i, row in df.iterrows():\n",
    "    sentences = tokenizer_ckb.sent_tokenize(row['clean_text'])\n",
    "    # sentences = row['clean_text'].split('\\n')\n",
    "    # sentences = sent_tokenize(row['clean_text'])\n",
    "    sentences = [sent_tokenize(s) for s in sentences]\n",
    "    # flatten list of lists\n",
    "    sentences = [item for sublist in sentences for item in sublist]\n",
    "    # split on period and keep the period\n",
    "    sentences = [s.split('.') for s in sentences]\n",
    "    sentences = [item for sublist in sentences for item in sublist]\n",
    "\n",
    "    sentences = [s + '.' for s in sentences]\n",
    "    data.extend(sentences)\n",
    "    # if i == 5:\n",
    "    #     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# longest line in data\n",
    "max_line = max(data, key=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(max_line.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calulate the length of each line in the data and take the average\n",
    "lengths = [len(line.split()) for line in data]\n",
    "avg_length = sum(lengths) / len(lengths)\n",
    "print(avg_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# give me all the lines above 20 words\n",
    "long_lines = [line for line in data if len(line.split()) > 25]\n",
    "print(len(long_lines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write sentences to file\n",
    "with open(\"data/oscar_wiki.ckb.txt\", \"w\") as f:\n",
    "    for sentence in data:\n",
    "        f.write(sentence + \"\\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": "3.11.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}