News-Classification-V2.ipynb

{
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   },
   "source": [
    "<div dir=\"auto\" align=\"center\">\n",
    "    <h3>\n",
    "        بسم الله الرحمن الرحیم\n",
    "    </h3>\n",
    "    <br>\n",
    "    <h1>\n",
    "        <strong>\n",
    "            بازیابی پیشرفته اطلاعات\n",
    "        </strong>\n",
    "    </h1>\n",
    "    <h2>\n",
    "        <strong>\n",
    "            تمرین چهارم (دسته‌بندی اخبار) - نسخه بهبودیافته\n",
    "        </strong>\n",
    "    </h2>\n",
    "    <br>\n",
    "    <h3>\n",
    "        محمد هجری - ٩٨١٠٦١٥٦\n",
    "        <br><br>\n",
    "        ارشان دلیلی - ٩٨١٠٥٧٥١\n",
    "        <br><br>\n",
    "        سروش جهان‌زاد - ٩٨١٠٠٣٨٩\n",
    "    </h3>\n",
    "    <br>\n",
    "</div>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "bDy59Vhk6ttY"
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        دسترسی به داده‌ها و مدل‌های ذخیره شده\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        به دلیل حجم بالای فایل‌های ذخیره شده، از قرار دادن آن‌ها در پوشه نوتبوک صرف نظر کرده و با اجرای قطعه کد زیر، از طریق گوگل درایو به آن‌ها دسترسی پیدا می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gXmeQEW66ttZ",
    "outputId": "416a2101-dceb-4693-c58d-b7a4828c77f6",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# import sys\n",
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "# address = \"University/Term 6/MIR/Homeworks/HW3\"\n",
    "# sys.path.append(f\"/content/drive/My Drive/{address}\")\n",
    "# %cd /content/drive/My\\ Drive/$address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "Uhyo5mtc6tta"
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        نصب و دسترسی به کتابخانه‌های مورد نیاز\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با اجرای دو قطعه کد زیر، کتابخانه‌هایی که از آن‌ها در این تمرین استفاده شده است، نصب و قابل استفاده می‌شوند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5DJqkFOY6tta",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "! pip install bs4\n",
    "! pip install tqdm\n",
    "! pip install pandas\n",
    "! pip install requests\n",
    "! pip install hazm\n",
    "! pip install unidecode\n",
    "! pip install datasets\n",
    "! pip install scikit-learn\n",
    "! pip install nltk\n",
    "! pip install torch\n",
    "! pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "bs5P-S4T6ttb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import csv\n",
    "import hazm\n",
    "import nltk\n",
    "import json\n",
    "import torch\n",
    "import pickle\n",
    "import zipfile\n",
    "import fasttext\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "from datasets import Dataset\n",
    "from bs4 import BeautifulSoup\n",
    "from string import punctuation\n",
    "from IPython.display import display\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import DataCollatorWithPadding\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score, confusion_matrix\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, TextClassificationPipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "HlgX1wDm6ttb"
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        ١. دریافت داده‌ها\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این تمرین، بیش از ٦٨ هزار خبر از\n",
    "        <a href=\"https://www.hamshahrionline.ir/\"> وب‌سایت همشهری‌آنلاین </a>\n",
    "        گردآوری شده که در ١٠ دسته‌ی سیاسی، جهانی، اقتصادی، اجتماعی، شهری، ورزشی، علمی، فرهنگی، فناوری اطلاعات و مهارت‌های زندگی طبقه‌بندی شده‌اند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "TFclx23L6ttc",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "CATEGORIES = {\n",
    "    'Politics': 'سیاسی',\n",
    "    'World': 'جهانی',\n",
    "    'Economy': 'اقتصادی',\n",
    "    'Society': 'اجتماعی',\n",
    "    'City': 'شهری',\n",
    "    'Sport': 'ورزشی',\n",
    "    'Science': 'علمی',\n",
    "    'Culture': 'فرهنگی',\n",
    "    'IT': 'فناوری اطلاعات',\n",
    "    'LifeSkills': 'مهارت‌های زندگی',\n",
    "}\n",
    "\n",
    "CATEGORIES_CLASSES = {\n",
    "    'Politics': 0,\n",
    "    'World': 1,\n",
    "    'Economy': 2,\n",
    "    'Society': 3,\n",
    "    'City': 4,\n",
    "    'Sport': 5,\n",
    "    'Science': 6,\n",
    "    'Culture': 7,\n",
    "    'IT': 8,\n",
    "    'LifeSkills': 9,\n",
    "}\n",
    "\n",
    "CLASSES_CATEGORIES = {v: k for k, v in CATEGORIES_CLASSES.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "DgNCrnH56ttd"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        برای دریافت داده‌ها یک ماژول Scraper ساخته‌ایم که اخبار مربوط به ١٠ دسته‌ی مذکور را در بازه‌ی زمانی تعیین شده، کراول کرده و در فایل dataset.zip ذخیره و فشرده سازی می‌کند. کد مربوط به این ماژول را در زیر مشاهده می‌کنید.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "fVKQF6R36ttd",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Scraper:\n",
    "\n",
    "    def __init__(self, current_year, current_month):\n",
    "        self.current_year = current_year\n",
    "        self.current_month = current_month\n",
    "\n",
    "    def get_URL_content(self, URL):\n",
    "        while True:\n",
    "            try:\n",
    "                return requests.get(URL, timeout=5).content\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "    def generate_page_URL(self, page_index, category, year, month):\n",
    "        tp = {'Politics': 6, 'World': 11, 'Economy': 10, 'Society': 5, 'City': 7,\n",
    "              'Sport': 9, 'Science': 20, 'Culture': 26, 'IT': 718, 'LifeSkills': 21}[category]\n",
    "        return f'https://www.hamshahrionline.ir/archive?pi={page_index}&tp={tp}&ty=1&ms=0&mn={month}&yr={year}'\n",
    "\n",
    "    def get_page_URLs_by_time(self, category, year, month):\n",
    "        URLs = []\n",
    "        page_index = 1\n",
    "        while True:\n",
    "            URL = self.generate_page_URL(page_index, category, year, month)\n",
    "            content = self.get_URL_content(URL)\n",
    "            if re.findall('pagination', str(content)):\n",
    "                URLs.append(URL)\n",
    "                page_index += 1\n",
    "            else:\n",
    "                break\n",
    "        return URLs\n",
    "\n",
    "    def get_page_URLs_since(self, category, year, month):\n",
    "        URLs = []\n",
    "        with tqdm() as pbar:\n",
    "            while True:\n",
    "                if month > 12:\n",
    "                    month = 1\n",
    "                    year += 1\n",
    "                pbar.set_description(f'[{category}] [Extracting page URLs] [Date: {year}/{month}]')\n",
    "                URLs_by_time = self.get_page_URLs_by_time(category, year, month)\n",
    "                if URLs_by_time:\n",
    "                    for URL in URLs_by_time:\n",
    "                        URLs.append(URL)\n",
    "                    month += 1\n",
    "                elif self.current_year > year or (self.current_year == year and self.current_month > month):\n",
    "                    month += 1\n",
    "                else:\n",
    "                    break\n",
    "        return URLs\n",
    "\n",
    "    def get_news_URLs_since(self, category, year, month):\n",
    "        news_URLs = []\n",
    "        page_URLs = self.get_page_URLs_since(category, year, month)\n",
    "        with tqdm(page_URLs) as pbar:\n",
    "            for page_URL in pbar:\n",
    "                content = self.get_URL_content(page_URL)\n",
    "                soup = BeautifulSoup(content, 'html5lib')\n",
    "                for item in soup.findAll('li', attrs={'class': 'news'}):\n",
    "                    URL = item.find('div', attrs={'class': 'desc'}).find('h3').find('a')['href']\n",
    "                    URL = 'https://www.hamshahrionline.ir' + URL\n",
    "                    news_URLs.append(URL)\n",
    "                pbar.set_description(f'[{category}] [Extracting news URLs] [{len(news_URLs)} news until now]')\n",
    "        return news_URLs\n",
    "\n",
    "    def parse_news(self, URL, category):\n",
    "        try:\n",
    "            content = self.get_URL_content(URL)\n",
    "            soup = BeautifulSoup(content, 'html.parser')\n",
    "            date = soup.find('div', {'class': 'col-6 col-sm-4 col-xl-4 item-date'}).span.text.strip()\n",
    "            title = soup.find('div', {'class': 'item-title'}).h1.text.strip()\n",
    "            intro = soup.find('p', {'class': 'introtext', 'itemprop': 'description'}).text.strip()\n",
    "            body = soup.find('div', {'class': 'item-text', 'itemprop': 'articleBody'}).text.strip()\n",
    "            return {\n",
    "                'date': date,\n",
    "                'title': title,\n",
    "                'intro': intro,\n",
    "                'body': body,\n",
    "                'category': category,\n",
    "            }\n",
    "        except:\n",
    "            return None\n",
    "\n",
    "    def scrape(self, from_year, from_month):\n",
    "        categories = ['Politics', 'World', 'Economy', 'Society', 'City',\n",
    "                      'Sport', 'Science', 'Culture', 'IT', 'LifeSkills']\n",
    "        news = []\n",
    "        for category in categories:\n",
    "            URLs = self.get_news_URLs_since(category, from_year, from_month)\n",
    "            with tqdm(URLs) as pbar:\n",
    "                pbar.set_description(f'[{category}] [Scraping news]')\n",
    "                for URL in pbar:\n",
    "                    news.append(self.parse_news(URL, category))\n",
    "        news = list(filter(None, news))\n",
    "        pd.DataFrame(news).to_csv(f'dataset.csv', encoding='utf-8')\n",
    "        with zipfile.ZipFile('dataset.zip', 'w', zipfile.ZIP_DEFLATED) as zip_file:\n",
    "            zip_file.write('dataset.csv')\n",
    "        os.remove('dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "Cx3Sl-iS6tte"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با اجرای قطعه کد زیر، یک instance از ماژول Scraper ایجاد شده و شروع به دریافت و ذخیره‌سازی داده‌ها می‌کند. خبرهای دریافت شده همگی مربوط به قرن جدید، از سال ١٤٠٠ به بعد هستند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "fc5LA2wh6ttf",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "scraper = Scraper(current_year=1401, current_month=3)\n",
    "scraper.scrape(from_year=1400, from_month=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "5kMts6qe6ttf"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        بعد از ذخیره شدن داده‌ها در فایل فشرده dataset.zip، آن‌ها را از این فایل استخراج کرده و وارد برنامه می‌کنیم. با اجرای قطعه کد زیر، تعداد خبرهای هر دسته و تعداد کل خبرها را می‌توان مشاهده کرد.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "HuoWmM7P6ttf",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def read_dataset_from_file():\n",
    "    dataset = []\n",
    "    with zipfile.ZipFile('dataset.zip', 'r') as zip_file:\n",
    "        zip_file.extractall()\n",
    "    with open('dataset.csv', encoding='utf-8') as file:\n",
    "        csv_reader = csv.reader(file)\n",
    "        header = next(csv_reader)\n",
    "        for row in csv_reader:\n",
    "            data = dict(zip(header[1:], row[1:]))\n",
    "            dataset.append(data)\n",
    "    os.remove('dataset.csv')\n",
    "    return dataset\n",
    "\n",
    "\n",
    "dataset = pd.DataFrame(read_dataset_from_file())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "SQOAtgLY6ttf"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "    با اجرای قطعه کد زیر، تعداد خبرهای هر دسته و تعداد کل خبرها را می‌توان مشاهده کرد.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "HIkDaiJC6ttg",
    "outputId": "ccaea68d-a10a-4e57-ef6c-141aca057421",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>دسته</th>\n",
       "      <th>تعداد</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>کل خبرها</td>\n",
       "      <td>68362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>سیاسی</td>\n",
       "      <td>15798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>جهانی</td>\n",
       "      <td>2895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>اقتصادی</td>\n",
       "      <td>8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اجتماعی</td>\n",
       "      <td>13585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>شهری</td>\n",
       "      <td>3853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ورزشی</td>\n",
       "      <td>8348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>علمی</td>\n",
       "      <td>3190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>فرهنگی</td>\n",
       "      <td>6512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>فناوری اطلاعات</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>مهارت‌های زندگی</td>\n",
       "      <td>4844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               دسته  تعداد\n",
       "0          کل خبرها  68362\n",
       "1             سیاسی  15798\n",
       "2             جهانی   2895\n",
       "3           اقتصادی   8900\n",
       "4           اجتماعی  13585\n",
       "5              شهری   3853\n",
       "6             ورزشی   8348\n",
       "7              علمی   3190\n",
       "8            فرهنگی   6512\n",
       "9    فناوری اطلاعات    437\n",
       "10  مهارت‌های زندگی   4844"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_dataset_info():\n",
    "    global CATEGORIES, dataset\n",
    "\n",
    "    length_dict = {key: 0 for key in CATEGORIES.keys()}\n",
    "    for _, data in dataset.iterrows():\n",
    "        length_dict[data['category']] += 1\n",
    "\n",
    "    df_dict = {\n",
    "        'دسته': CATEGORIES.values(),\n",
    "        'تعداد': length_dict.values(),\n",
    "    }\n",
    "\n",
    "    df = pd.DataFrame(df_dict)\n",
    "    df.index += 1\n",
    "    df.loc[0] = ['کل خبرها', len(dataset)]\n",
    "    df = df.sort_index()\n",
    "    display(df)\n",
    "\n",
    "\n",
    "display_dataset_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "YuwgMYpB6ttg"
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        ٢. پیش پردازش اولیه‌ی متن\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        ابزار مورد استفاده برای پیش‌پردازش متن ورودی به صورت ماژولار طراحی شده است؛ به طوری که با صدا زدن تابع preprocess از آن، متن داده شده با عبور از یک خط لوله به صورت مرحله به مرحله تغییر می‌کند تا به یک ساختار استاندارد برسد. این مراحل عبارتند از:\n",
    "    </p>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        نرمال سازی داده‌ها (normalize)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف لینک‌ها (remove_links)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف نشانه‌های نگارشی (remove_punctuations)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        واحد سازی داده‌ها (word_tokenize)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف کلمات نامعتبر (remove_invalid_words)\n",
    "    </li>\n",
    "    <li style='direction:rtl;text-align:justify;'>\n",
    "        حذف ایست‌واژه‌ها (remove_stopwords)\n",
    "    </li>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "phcZ2s706ttg",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Preprocessor:\n",
    "\n",
    "    def __init__(self, stopwords_path):\n",
    "        self.stopwords = []\n",
    "        with open(stopwords_path, encoding='utf-8') as file:\n",
    "            self.stopwords = file.read().split()\n",
    "\n",
    "    def preprocess(self, text):\n",
    "        text = self.normalize(text)\n",
    "        text = self.remove_links(text)\n",
    "        text = self.remove_punctuations(text)\n",
    "        words = self.word_tokenize(text)\n",
    "        words = self.remove_invalid_words(words)\n",
    "        words = self.remove_stopwords(words)\n",
    "        return words\n",
    "\n",
    "    def normalize(self, text):\n",
    "        return hazm.Normalizer().normalize(text)\n",
    "\n",
    "    def remove_links(self, text):\n",
    "        patterns = ['\\S*http\\S*', '\\S*www\\S*', '\\S+\\.ir\\S*', '\\S+\\.com\\S*', '\\S+\\.org\\S*', '\\S*@\\S*']\n",
    "        for pattern in patterns:\n",
    "            text = re.sub(pattern, ' ', text)\n",
    "        return text\n",
    "\n",
    "    def remove_punctuations(self, text):\n",
    "        return re.sub(f'[{punctuation}؟،٪×÷»«]+', '', text)\n",
    "\n",
    "    def word_tokenize(self, text):\n",
    "        return hazm.word_tokenize(text)\n",
    "\n",
    "    def remove_invalid_words(self, words):\n",
    "        return [word for word in words if len(word) > 3 or re.match('^[\\u0600-\\u06FF]{2,3}$', word)]\n",
    "\n",
    "    def remove_stopwords(self, words):\n",
    "        return [word for word in words if word not in self.stopwords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "2tqXc9WR6tth",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def save_preprocessed_texts(texts, path=\"Preprocessed_texts_title.pickle\"):\n",
    "    with open(path, \"wb\") as file:\n",
    "        pickle.dump(texts, file)\n",
    "\n",
    "\n",
    "def load_preprocessed_texts(path=\"Preprocessed_texts_title.pickle\"):\n",
    "    with open(path, \"rb\") as file:\n",
    "        return pickle.load(file)\n",
    "\n",
    "\n",
    "def data_to_text(data):\n",
    "    return ' '.join([data['title'], data['intro'], data['body']]).lower()\n",
    "\n",
    "\n",
    "def get_preprocessed_texts(dataset, preprocessor, mode, save=False):\n",
    "    preprocessed_texts = []\n",
    "    if mode == 'process':\n",
    "        texts = [data_to_text(data) for _, data in dataset.iterrows()]\n",
    "        preprocessed_texts = [preprocessor.preprocess(text) for text in tqdm(texts)]\n",
    "    if mode == 'load':\n",
    "        preprocessed_texts = load_preprocessed_texts()\n",
    "    if save:\n",
    "        save_preprocessed_texts(preprocessed_texts)\n",
    "    return preprocessed_texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "E3M4tMlL6tth"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با اجرای قطعه کد زیر، یک instance از ماژول Preprocessor ایجاد کرده و شروع به پیش پردازش داده‌ها می‌کنیم، یا داده‌های پیش‌پردازش‌شده‌ی ذخیره‌شده را از فایل مربوطه بازیابی می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "Izni3XhQ6tth",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "preprocessor = Preprocessor(stopwords_path='stopwords.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "3MYNCBkS8CTe"
   },
   "outputs": [],
   "source": [
    "preprocessed_texts = get_preprocessed_texts(dataset, preprocessor, mode='load', save=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "Cmjunsz86tti"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        با توجه به این که حجم مجموعه داده‌ها باعث ایجاد محدودیت حافظه و زمان برای مدل tf-idf می‌شود، ناچار هستیم کسری از مجموعه‌ی داده‌ها را به عنوان ورودی برای یادگیری به آن بدهیم. با اجرای قطعه کد زیر، یک مجموعه داده‌ی کوچک‌تر از روی مجموعه داده‌ی اصلی ایجاد می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NpxmLgeU6tti",
    "outputId": "f0f02553-2d1d-4cf3-fc01-828736afabe3",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4000/4000 [00:16<00:00, 238.78it/s]\n"
     ]
    }
   ],
   "source": [
    "def get_mini_dataset(len_each_category=400):\n",
    "    global CATEGORIES, dataset\n",
    "\n",
    "    mini_dataset = []\n",
    "    for category in CATEGORIES.keys():\n",
    "        dataset_by_category = dataset.loc[dataset['category'] == category]\n",
    "        length = min(len_each_category, dataset_by_category.shape[0])\n",
    "        mini_dataset.append(dataset_by_category.sample(length, random_state=1))\n",
    "\n",
    "    mini_dataset = pd.concat(mini_dataset).reset_index(drop=True)\n",
    "    texts = [data_to_text(data) for _, data in mini_dataset.iterrows()]\n",
    "    mini_preprocessed_texts = [preprocessor.preprocess(text) for text in tqdm(texts)]\n",
    "    return mini_dataset, mini_preprocessed_texts\n",
    "\n",
    "\n",
    "# Almost 4K News\n",
    "mini_dataset, mini_preprocessed_texts = get_mini_dataset()\n",
    "mini_dataset['category_code'] = mini_dataset['category'].apply(lambda x: CATEGORIES_CLASSES[x])\n",
    "labels = mini_dataset['category_code'].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "7GgSPBt36tti"
   },
   "source": [
    "<div>\n",
    "    <h3 style='direction:rtl;text-align:justify;'>\n",
    "        ٣. دسته‌بندی اخبار بر اساس برچسب خبر\n",
    "    </h3>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این مرحله با استفاده از دو مدل متفاوت زبانی، یکی مبتنی بر روش سنتی رگرسیون لجستیک و دیگری بر پایه ترنسفرمر، شروع به یادگیری بر روی خبرهای استخراج شده می‌کنیم و در انتها، نتیجه اجرای ده کوئری را بر روی هر یک مشاهده می‌کنیم. در نهایت، ارزیابی‌های مورد بحث در داک تمرین را بر روی داده‌های تست انجام می‌دهیم.\n",
    "    </p>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        کوئری‌های مورد استفاده در این بخش در زیر آمده‌اند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "qASkPvTs6tti",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "QUERIES = [\n",
    "    \"نتیجه توافق برجام\",\n",
    "    \"حمله ارتش روسیه به اوکراین\",\n",
    "    \"افزایش نرخ تورم در کشور\",\n",
    "    \"آمار فوتی‌های کرونا\",\n",
    "    \"اقدامات شهرداری تهران\",\n",
    "    \"صعود ایران به جام جهانی\",\n",
    "    \"بیماری آبله میمونی\",\n",
    "    \"جشنواره فیلم فجر\",\n",
    "    \"رونمایی از گوشی جدید اپل\",\n",
    "    \"روش پخت غذا\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "ABtH0P3n6tti"
   },
   "source": [
    "<div>\n",
    "    <h4 style='direction:rtl;text-align:justify;'>\n",
    "        ١. مدل مبتنی بر روش Logistic Regression\n",
    "    </h4>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این بخش از کلاس TfidfVectorizer از کتاب‌خانه‌ی sklearn استفاده کرده و مجموعه کلمات یکتای موجود در عناوین خبرها را به عنوان ورودی به آن می‌دهیم. این کلمات یکتا را از روی فایل ذخیره شده‌ی vocabulary.pickle می‌خوانیم. در ادامه، یک مدل ایجاد کرده و بردار embedding عنوان هر خبر را به روش tf-idf به دست می‌آوریم. سپس، داده‌هایمان را به دو مجموعه train و test افراز می‌کنیم. حال، با استفاده از کلاس LogisticRegression کتاب‌خانه‌ی sklearn شروع به یادگیری داده‌های train می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "pPQFUkyu6ttj",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class TF_IDF:\n",
    "\n",
    "    def __init__(self, vocabulary):\n",
    "        self.vectorizer = TfidfVectorizer(max_features=2000)\n",
    "\n",
    "    def fit_transform_vectorizer(self, dataset):\n",
    "        vectors = self.vectorizer.fit_transform(list(map(lambda doc: ' '.join(doc), dataset)))\n",
    "        dense_vectors = vectors.todense().tolist()\n",
    "        return np.array(dense_vectors)\n",
    "\n",
    "    def save_TF_IDF_model(self, path=\"TF_IDF_model.pickle\"):\n",
    "        with open(path, \"wb\") as file:\n",
    "            pickle.dump(self.vectors.todense().tolist(), file)\n",
    "\n",
    "    def load_TF_IDF_model(self, path=\"TF_IDF_model.pickle\"):\n",
    "        with open(path, \"rb\") as file:\n",
    "            return pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "l8bXllAK6ttj",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "vocabulary = {}\n",
    "with open('vocabulary.pickle', \"rb\") as file:\n",
    "    vocabulary = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "aYf1ZnBD6ttj",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "TF_IDF_model = TF_IDF(vocabulary)\n",
    "vectors = TF_IDF_model.fit_transform_vectorizer(mini_preprocessed_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "fBhxqbi76ttj",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_vectors, test_vectors, train_labels, test_labels = train_test_split(vectors, labels, test_size=0.15, shuffle=True, random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "yUElni9L6ttj",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(max_iter=1000, random_state=0)\n",
    "classifier.fit(train_vectors, train_labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "X4KAwZxJ6ttj"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این قسمت، دسته‌ی هر یک از کوئری‌های مشخص شده را با استفاده از مدل یادگیری پیش‌بینی می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "vSAXoRKW6ttk",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Query</th>\n",
       "      <th>Predicted Category</th>\n",
       "      <th>Probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>نتیجه توافق برجام</td>\n",
       "      <td>Politics</td>\n",
       "      <td>0.430454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>حمله ارتش روسیه به اوکراین</td>\n",
       "      <td>World</td>\n",
       "      <td>0.799289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>افزایش نرخ تورم در کشور</td>\n",
       "      <td>Economy</td>\n",
       "      <td>0.385289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>آمار فوتی‌های کرونا</td>\n",
       "      <td>Society</td>\n",
       "      <td>0.480850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اقدامات شهرداری تهران</td>\n",
       "      <td>City</td>\n",
       "      <td>0.958167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>صعود ایران به جام جهانی</td>\n",
       "      <td>Sport</td>\n",
       "      <td>0.483883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>بیماری آبله میمونی</td>\n",
       "      <td>Science</td>\n",
       "      <td>0.364224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>جشنواره فیلم فجر</td>\n",
       "      <td>Culture</td>\n",
       "      <td>0.837791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>رونمایی از گوشی جدید اپل</td>\n",
       "      <td>IT</td>\n",
       "      <td>0.764616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>روش پخت غذا</td>\n",
       "      <td>LifeSkills</td>\n",
       "      <td>0.498233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Query Predicted Category  Probability\n",
       "0           نتیجه توافق برجام           Politics     0.430454\n",
       "1  حمله ارتش روسیه به اوکراین              World     0.799289\n",
       "2     افزایش نرخ تورم در کشور            Economy     0.385289\n",
       "3         آمار فوتی‌های کرونا            Society     0.480850\n",
       "4       اقدامات شهرداری تهران               City     0.958167\n",
       "5     صعود ایران به جام جهانی              Sport     0.483883\n",
       "6          بیماری آبله میمونی            Science     0.364224\n",
       "7            جشنواره فیلم فجر            Culture     0.837791\n",
       "8    رونمایی از گوشی جدید اپل                 IT     0.764616\n",
       "9                 روش پخت غذا         LifeSkills     0.498233"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "queries_class_prediction = []\n",
    "\n",
    "for query in QUERIES:\n",
    "    preprocessed_query = ' '.join(preprocessor.preprocess(query))\n",
    "    query_embed = TF_IDF_model.vectorizer.transform([preprocessed_query])\n",
    "    predicted_class_code = classifier.predict(query_embed)[0]\n",
    "    probability = classifier.predict_proba(query_embed)[0][predicted_class_code]\n",
    "    queries_class_prediction.append({\n",
    "        'Query': query,\n",
    "        'Predicted Category': CLASSES_CATEGORIES[predicted_class_code],\n",
    "        'Probability': probability\n",
    "    })\n",
    "\n",
    "display(pd.DataFrame(queries_class_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "5AjPHN6Q6ttk"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این قسمت، بررسی می‌کنیم که مدل یادگیری‌مان بر روی داده‌های test چه عملکردی دارد. با اجرای قطعه کد زیر، معیار ارزیابی F1-Macro و نیز accuracy و ماتریس درهم‌ریختگی محاسبه شده و نمایش داده می‌شوند. (در این ماتریس، ستون‌ها ground truth و سطرها پیش‌بینی ما هستند.)\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "_ultlXMM6ttk",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accurracy: 0.86\n",
      "\n",
      "F1-Macro: 0.857\n",
      "\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Politics</th>\n",
       "      <th>World</th>\n",
       "      <th>Economy</th>\n",
       "      <th>Society</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Science</th>\n",
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       "      <th>LifeSkills</th>\n",
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       "      <td>3</td>\n",
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       "            Politics  World  Economy  Society  City  Sport  Science  Culture  \\\n",
       "Politics          52      6        2        4     0      0        1        2   \n",
       "World              3     40        2        2     0      1        4        1   \n",
       "Economy            0      0       52        2     1      0        0        0   \n",
       "Society            4      0        0       56     4      0        0        2   \n",
       "City               0      0        0        4    54      0        0        0   \n",
       "Sport              0      0        0        0     0     62        0        0   \n",
       "Science            0      2        0        0     0      0       42        1   \n",
       "Culture            0      1        0        0     0      0        0       62   \n",
       "IT                 0      1        3        1     0      0        1        0   \n",
       "LifeSkills         1      3        0        1     0      0        3        1   \n",
       "\n",
       "            IT  LifeSkills  \n",
       "Politics     0           0  \n",
       "World        2           0  \n",
       "Economy      2           0  \n",
       "Society      1           2  \n",
       "City         0           0  \n",
       "Sport        0           0  \n",
       "Science      6           3  \n",
       "Culture      2           1  \n",
       "IT          61           0  \n",
       "LifeSkills   1          35  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_acc = classifier.score(test_vectors, test_labels)\n",
    "print(f\"Mean Accurracy: {round(mean_acc, 3)}\\n\")\n",
    "\n",
    "predicted_labels = classifier.predict(test_vectors)\n",
    "f1_macro = f1_score(test_labels, predicted_labels, labels=np.arange(10), average='macro')\n",
    "print(f\"F1-Macro: {round(f1_macro, 3)}\\n\")\n",
    "\n",
    "confusion_mat = confusion_matrix(test_labels, predicted_labels, labels=np.arange(10))\n",
    "confusion_mat_df = pd.DataFrame(confusion_mat).rename(columns=CLASSES_CATEGORIES, index=CLASSES_CATEGORIES)\n",
    "display(confusion_mat_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "s5VKEYiI6ttk",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "<div>\n",
    "    <h4 style='direction:rtl;text-align:justify;'>\n",
    "        ٢. مدل مبتنی بر پایه Transformer\n",
    "    </h4>\n",
    "</div>\n",
    "\n",
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این بخش، با استفاده از کلاس AutoModelForSequenceClassification از کتاب‌خانه‌ی transformers نوشته‌های موجود را به دسته‌ی متناظرشان مرتبط می‌کنیم. برای این کار، از مدل ParsBERT که از پیش تمرین داده شده است به عنوان زیربنا استفاده می‌کنیم و سپس با افراز داده‌های موجود به سه بخش train و validation و test، با استفاده از دو بخش train و validation آن را بیش‌تر در راستای هدفمان که تشخیص دسته‌ی خبرهاست تمرین می‌دهیم.\n",
    "        همچنین در طول تمرین دادن از یک AutoTokenizer مبتنی بر ParsBERT استفاده می‌کنیم.\n",
    "    </p>\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        برای تمرین دادن این مدل از کلاس Trainer از همان کتاب‌خانه‌ی transformers استفاده می‌کنیم و پارامترهای تمرین دادن را هم مطابق آنچه در ادامه مشهود است با استفاده از متغیر training_args به آن می‌دهیم. در نهایت پس از پایان فرایند تمرین دادن، مدل را ذخیره می‌کنیم تا در دفعات بعد بتوانیم به صورت مستقیم آن را از فایل بازیابی کنیم و از آن استفاده کنیم.\n",
    "    </p>\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        پس از به دست آوردن مدل، با استفاده از کلاس TextClassificationPipeline از همان کتاب‌خانه‌ی transformers پایپ‌لاین تشخیص دسته را بر اساس مدلی که به دست آورده‌ایم تعریف می‌کنیم. بعد از این کار از این پایپ‌لاین استفاده می‌کنیم تا خروجی کار را بر روی درخواست‌هایی که نوشته بودیم ببینیم و همچنین با به دست آوردن نظر مدل درباره‌ی داده‌های test و محاسبه‌ی دقت میانگین و معیار F1-Macro و مشاهده‌ی ماتریس درهم‌ریختگی، کارایی مدل را بررسی کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "pBQAo29Y6ttk",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dataset['category_code'] = dataset['category'].apply(lambda x: CATEGORIES_CLASSES[x])\n",
    "labels = dataset['category_code'].to_numpy()\n",
    "new_dataset = pd.DataFrame()\n",
    "new_dataset['text'] = [' '.join(preprocessor.preprocess(x)) for x in dataset['title']]\n",
    "new_dataset['label'] = dataset['category_code']\n",
    "\n",
    "# Train 70%, Validation 15%, Test 15%\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(new_dataset, labels,  test_size=0.3, shuffle=True)\n",
    "val_data, test_data, val_labels, test_labels = train_test_split(test_data, test_labels,  test_size=0.5, shuffle=True)"
   ]
  },
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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     "text": [
      "Parameter 'function'=<function <lambda> at 0x7fa6a7807b90> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f99e2494dbab426dbf2fea59e53f7ca7",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9851a58505a1421dbb64840a1da06de5",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a3073d08c0e4c558f2c4bed9c63e0dd",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"HooshvareLab/bert-fa-zwnj-base\")\n",
    "\n",
    "tokenized_train_data = Dataset.from_pandas(train_data).map(lambda x: tokenizer(x['text'], truncation=True), batched=True)\n",
    "tokenized_val_data = Dataset.from_pandas(val_data).map(lambda x: tokenizer(x['text'], truncation=True), batched=True)\n",
    "tokenized_test_data = Dataset.from_pandas(test_data).map(lambda x: tokenizer(x['text'], truncation=True), batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 158,
     "referenced_widgets": [
      "4e6ee148818d4db895dbe9aab4768493",
      "f0ad55b6958447b98f0dc90ddd376f3f",
      "a98f62a3ceaa4be98aecdb6a469ecc0f",
      "9b0eb70307384a93810c6a347727d833",
      "81a94d36714f491db689cd9a19aef03d",
      "0d027469e6f944ad80471234db5ccfaa",
      "24771d2561fd41f8ba9b54677d715745",
      "76e5193b3bd44a8584c2b858441c7435",
      "b5826453722b451f9e18fe0ad9f2845b",
      "e14b8037f6134a88ad117a3fd427fbf1",
      "fed517506bd342b3b35981c55292adf4"
     ]
    },
    "id": "gTTYxVRE6ttl",
    "outputId": "647ef62b-5f87-406d-fbed-a7546e07ebbb",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e6ee148818d4db895dbe9aab4768493",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/452M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at HooshvareLab/bert-fa-zwnj-base were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.bias']\n",
      "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at HooshvareLab/bert-fa-zwnj-base and are newly initialized: ['bert.pooler.dense.bias', 'classifier.bias', 'bert.pooler.dense.weight', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"./results\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=128,\n",
    "    per_device_eval_batch_size=128,\n",
    "    num_train_epochs=5,\n",
    "    weight_decay=0.01,\n",
    ")\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"HooshvareLab/bert-fa-zwnj-base\", num_labels=10, id2label=CLASSES_CATEGORIES)\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_train_data,\n",
    "    eval_dataset=tokenized_val_data,\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4VJtEBTb6ttl",
    "outputId": "336aabeb-b6a9-4eca-da79-1203c231b71b",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file ./new_titles_transformer_model/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"./new_titles_transformer_model\",\n",
      "  \"architectures\": [\n",
      "    \"BertForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"Politics\",\n",
      "    \"1\": \"World\",\n",
      "    \"2\": \"Economy\",\n",
      "    \"3\": \"Society\",\n",
      "    \"4\": \"City\",\n",
      "    \"5\": \"Sport\",\n",
      "    \"6\": \"Science\",\n",
      "    \"7\": \"Culture\",\n",
      "    \"8\": \"IT\",\n",
      "    \"9\": \"LifeSkills\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"LABEL_0\": 0,\n",
      "    \"LABEL_1\": 1,\n",
      "    \"LABEL_2\": 2,\n",
      "    \"LABEL_3\": 3,\n",
      "    \"LABEL_4\": 4,\n",
      "    \"LABEL_5\": 5,\n",
      "    \"LABEL_6\": 6,\n",
      "    \"LABEL_7\": 7,\n",
      "    \"LABEL_8\": 8,\n",
      "    \"LABEL_9\": 9\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"problem_type\": \"single_label_classification\",\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.19.4\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 42000\n",
      "}\n",
      "\n",
      "loading weights file ./new_titles_transformer_model/pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing BertForSequenceClassification.\n",
      "\n",
      "All the weights of BertForSequenceClassification were initialized from the model checkpoint at ./new_titles_transformer_model.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(42000, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def train_and_save(trainer, model):\n",
    "    trainer.train()\n",
    "    model.save_pretrained(\"./new_titles_transformer_model\")\n",
    "\n",
    "# train_and_save(trainer, model)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"./new_titles_transformer_model\")\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "pLhuC5Ij6ttl",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "device = torch.cuda.current_device() if torch.cuda.is_available() else -1\n",
    "pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=False, device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "E_aia_eq6ttl"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این قسمت، دسته‌ی هر یک از کوئری‌های مشخص شده را با استفاده از مدل یادگیری پیش‌بینی می‌کنیم.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "-jxegNVH6ttl",
    "outputId": "09b75355-ad1a-4307-89be-9bbf3ad25e20",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Query</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>نتیجه توافق برجام</td>\n",
       "      <td>Politics</td>\n",
       "      <td>0.994476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>0.957387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>جشنواره فیلم فجر</td>\n",
       "      <td>Culture</td>\n",
       "      <td>0.989262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>رونمایی از گوشی جدید اپل</td>\n",
       "      <td>IT</td>\n",
       "      <td>0.832700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>روش پخت غذا</td>\n",
       "      <td>LifeSkills</td>\n",
       "      <td>0.987767</td>\n",
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      ],
      "text/plain": [
       "                        Query Predicted Category  Probability\n",
       "0           نتیجه توافق برجام           Politics     0.994476\n",
       "1  حمله ارتش روسیه به اوکراین              World     0.985831\n",
       "2     افزایش نرخ تورم در کشور            Economy     0.852276\n",
       "3         آمار فوتی‌های کرونا            Society     0.993497\n",
       "4       اقدامات شهرداری تهران               City     0.988388\n",
       "5     صعود ایران به جام جهانی              Sport     0.987941\n",
       "6          بیماری آبله میمونی            Science     0.957387\n",
       "7            جشنواره فیلم فجر            Culture     0.989262\n",
       "8    رونمایی از گوشی جدید اپل                 IT     0.832700\n",
       "9                 روش پخت غذا         LifeSkills     0.987767"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "queries_class_prediction = []\n",
    "\n",
    "for query in QUERIES:\n",
    "    preprocessed_query = ' '.join(preprocessor.preprocess(query))\n",
    "    prediction = pipe(preprocessed_query)[0]\n",
    "    predicted_class_code = CATEGORIES_CLASSES[prediction['label']]\n",
    "    probability = prediction['score']\n",
    "    queries_class_prediction.append({\n",
    "        'Query': query,\n",
    "        'Predicted Category': CLASSES_CATEGORIES[predicted_class_code],\n",
    "        'Probability': probability\n",
    "    })\n",
    "\n",
    "display(pd.DataFrame(queries_class_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "4b-doPSG6ttm"
   },
   "source": [
    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        در این قسمت، بررسی می‌کنیم که مدل یادگیری‌مان بر روی داده‌های test چه عملکردی دارد. با اجرای قطعه کد زیر، معیار ارزیابی F1-Macro و نیز accuracy و ماتریس درهم‌ریختگی محاسبه شده و نمایش داده می‌شوند. (در این ماتریس، ستون‌ها ground truth و سطرها پیش‌بینی ما هستند.)\n",
    "    </p>\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "         مشاهده می‌کنیم که دسته‌هایی که مدل برای درخواست‌هایمان در بخش قبلی پیشنهاد داده همگی منطقی هستند و معیارهای F1-Macro و دقت میانگین و ماتریس درهم‌ریختگی نیز نشان‌دهنده‌ی کارکرد خوب این مدل هستند.\n",
    "    </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 508
    },
    "id": "PAFV9DVj6ttm",
    "outputId": "15a31ab2-c55c-4abb-c4c6-10ba657ce18c",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/transformers/pipelines/base.py:998: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  UserWarning,\n",
      "Disabling tokenizer parallelism, we're using DataLoader multithreading already\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accurracy: 0.871\n",
      "\n",
      "F1-Macro: 0.832\n",
      "\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "      <th>Politics</th>\n",
       "      <td>2026</td>\n",
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       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <th>World</th>\n",
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       "      <td>16</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy</th>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>1249</td>\n",
       "      <td>64</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <th>Society</th>\n",
       "      <td>69</td>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "      <td>1784</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>481</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sport</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1225</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>383</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Culture</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "            Politics  World  Economy  Society  City  Sport  Science  Culture  \\\n",
       "Politics        2026     82       85      100    25      7        5       33   \n",
       "World             88    288        8        6     3      3       10       16   \n",
       "Economy           42      6     1249       64    10      4        4        1   \n",
       "Society           69      8       40     1784    51      4       29       29   \n",
       "City              20      2        7       53   481      0        2        5   \n",
       "Sport              6      1        1        5     2   1225        0       14   \n",
       "Science            4     18        5       32     3      0      383        4   \n",
       "Culture           18      6        0       34    12      4        6      875   \n",
       "IT                 1      2        5        0     0      0       14        2   \n",
       "LifeSkills         6     11       14       38     3      7       21       18   \n",
       "\n",
       "            IT  LifeSkills  \n",
       "Politics     2           5  \n",
       "World        1           4  \n",
       "Economy      2           2  \n",
       "Society      1          26  \n",
       "City         0           2  \n",
       "Sport        0           1  \n",
       "Science      8          30  \n",
       "Culture      0           0  \n",
       "IT          37           0  \n",
       "LifeSkills   0         585  "
      ]
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   "source": [
    "golden_labels = test_data['label'].to_numpy()\n",
    "model_test_predictions = pd.DataFrame(pipe(test_data['text'].to_list())).label.apply(lambda x: CATEGORIES_CLASSES[x]).to_numpy()\n",
    "\n",
    "mean_acc = np.average(model_test_predictions == golden_labels)\n",
    "print(f\"Mean Accurracy: {round(mean_acc, 3)}\\n\")\n",
    "\n",
    "f1_macro = f1_score(golden_labels, model_test_predictions, labels=np.arange(10), average='macro')\n",
    "print(f\"F1-Macro: {round(f1_macro, 3)}\\n\")\n",
    "\n",
    "confusion_mat = confusion_matrix(golden_labels, model_test_predictions, labels=np.arange(10))\n",
    "confusion_mat_df = pd.DataFrame(confusion_mat).rename(columns=CLASSES_CATEGORIES, index=CLASSES_CATEGORIES)\n",
    "display(confusion_mat_df)"
   ]
  },
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    "<div dir=\"auto\" align=\"justify\">\n",
    "    <p style='direction:rtl;text-align:justify;'>\n",
    "        از این که وقت ارزشمند خود را برای بررسی این نوتبوک صرف کرده‌اید، صمیمانه سپاسگزاریم. (:\n",
    "    </p>\n",
    "</div>"
   ]
  }
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