News-Classification-V1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "l9k4nvPt6ttU"
   },
   "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)\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": 4,
   "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 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": 5,
   "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": 6,
   "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": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "metadata": {
    "id": "2tqXc9WR6tth",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def save_preprocessed_texts(texts, path=\"Preprocessed_texts.pickle\"):\n",
    "    with open(path, \"wb\") as file:\n",
    "        pickle.dump(texts, file)\n",
    "\n",
    "\n",
    "def load_preprocessed_texts(path=\"Preprocessed_texts.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": 12,
   "metadata": {
    "id": "Izni3XhQ6tth",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "preprocessor = Preprocessor(stopwords_path='stopwords.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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%|██████████| 9437/9437 [00:45<00:00, 205.24it/s]\n"
     ]
    }
   ],
   "source": [
    "def get_mini_dataset(len_each_category=1000):\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 10K 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": 15,
   "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": 16,
   "metadata": {
    "id": "pPQFUkyu6ttj",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class TF_IDF:\n",
    "\n",
    "    def __init__(self, vocabulary):\n",
    "        self.vectorizer = TfidfVectorizer(vocabulary=vocabulary)\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": 17,
   "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": 18,
   "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": 19,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "yUElni9L6ttj",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(max_iter=1000, random_state=0)\n",
    "classifier.fit(train_vectors, train_labels)"
   ]
  },
  {
   "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": 21,
   "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>0.366094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>حمله ارتش روسیه به اوکراین</td>\n",
       "      <td>World</td>\n",
       "      <td>0.926617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>افزایش نرخ تورم در کشور</td>\n",
       "      <td>Economy</td>\n",
       "      <td>0.489613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>آمار فوتی‌های کرونا</td>\n",
       "      <td>Society</td>\n",
       "      <td>0.417413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اقدامات شهرداری تهران</td>\n",
       "      <td>City</td>\n",
       "      <td>0.995061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>صعود ایران به جام جهانی</td>\n",
       "      <td>Sport</td>\n",
       "      <td>0.611417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>بیماری آبله میمونی</td>\n",
       "      <td>Science</td>\n",
       "      <td>0.495273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>جشنواره فیلم فجر</td>\n",
       "      <td>Culture</td>\n",
       "      <td>0.972643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>رونمایی از گوشی جدید اپل</td>\n",
       "      <td>IT</td>\n",
       "      <td>0.817819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>روش پخت غذا</td>\n",
       "      <td>LifeSkills</td>\n",
       "      <td>0.605012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Query Predicted Category  Probability\n",
       "0           نتیجه توافق برجام           Politics     0.366094\n",
       "1  حمله ارتش روسیه به اوکراین              World     0.926617\n",
       "2     افزایش نرخ تورم در کشور            Economy     0.489613\n",
       "3         آمار فوتی‌های کرونا            Society     0.417413\n",
       "4       اقدامات شهرداری تهران               City     0.995061\n",
       "5     صعود ایران به جام جهانی              Sport     0.611417\n",
       "6          بیماری آبله میمونی            Science     0.495273\n",
       "7            جشنواره فیلم فجر            Culture     0.972643\n",
       "8    رونمایی از گوشی جدید اپل                 IT     0.817819\n",
       "9                 روش پخت غذا         LifeSkills     0.605012"
      ]
     },
     "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": 22,
   "metadata": {
    "id": "_ultlXMM6ttk",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accurracy: 0.858\n",
      "\n",
      "F1-Macro: 0.852\n",
      "\n"
     ]
    },
    {
     "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>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",
       "      <th>Culture</th>\n",
       "      <th>IT</th>\n",
       "      <th>LifeSkills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Politics</th>\n",
       "      <td>110</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Society</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>103</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sport</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Culture</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LifeSkills</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Politics  World  Economy  Society  City  Sport  Science  Culture  \\\n",
       "Politics         110     14        6        3     4      1        2        1   \n",
       "World              3    129        1        3     1      2        6        1   \n",
       "Economy            5      1      129        5     0      0        1        0   \n",
       "Society            3      0        7      103    12      0        3        5   \n",
       "City               4      0        3        7   149      0        0        1   \n",
       "Sport              0      1        2        0     2    134        0        0   \n",
       "Science            1      3        1        1     0      0      106        1   \n",
       "Culture            1      1        2        3     2      0        0      165   \n",
       "IT                 1      9        3        1     0      0        5        0   \n",
       "LifeSkills         2      4        2        3     1      2        6        6   \n",
       "\n",
       "            IT  LifeSkills  \n",
       "Politics     1           1  \n",
       "World        2           2  \n",
       "Economy      2           1  \n",
       "Society      0           3  \n",
       "City         0           0  \n",
       "Sport        0           0  \n",
       "Science      8           7  \n",
       "Culture      0           2  \n",
       "IT          64           0  \n",
       "LifeSkills   1         126  "
      ]
     },
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311,
     "referenced_widgets": [
      "0a4250dbb41549cb8f18585bec3b9c5c",
      "fce2351d56994af3a5c0315d8869d305",
      "29496b4c5e0740eb877fbf2d23bd899e",
      "6cd049c9ea9d4a5983624285d32ca36e",
      "67f88c33b18b4b75a70205f706f97f09",
      "b925b7826a22488eaf7cfda65402d35f",
      "a35b627b057b44168844bc5c253903f1",
      "85b5f1fbba34444886d91c821ef7ed1b",
      "254b0fa4c48845b88a731471e7d1782c",
      "1b3f13f817eb412f85d64de718a0ddf6",
      "e88fb1dd95c6474d82cab28636752fdb",
      "15d908a381f0426fbd56b94430de55b1",
      "69ea65d3288e4fcea3f01b37f8218d11",
      "9f2fe5b01c704480aab6be4478938491",
      "c634f5e4536844c19df9f412b51bbce6",
      "61fccaea3c6b479f9dec4e33f22e685c",
      "1cd22488e9b64330b9a16cfbbc8e03a4",
      "dcffc897809f4db0bb083268e81f2b79",
      "7ed3a0ad9741490191eeda10f5301611",
      "42e548498ed6450d9e9fe392024f0e1f",
      "6aa88d25b7fe444483ca90d783ba2789",
      "de8c250ca5824385bf09eeac4fa0f9cc",
      "d63161325566473587e0a9c50c093210",
      "dcc994a80fc745fe9d4f6b7291c85365",
      "dcb24a531a934bc09695c07802e81e0f",
      "3590a192b5ff424eba3ef7e7685e9576",
      "305da9204d634e08b74cb9cb9471a783",
      "2723fa305a1f41cbb1ec6ee7227a7164",
      "39ea7e424f6e405ab55014ff6bcf57e9",
      "6e1fa17badaa4e559c4a437ee3c18ad6",
      "b25f8bee169b4c39a0b3e930b2805c6c",
      "3a3de175272a45c587f93a529b8bda0c",
      "b706541bc94a4ffbba05aae29f5db7ec",
      "e3d987c625c04404ba4aa0a44b99aa3d",
      "cfa13ea00bbc48d388838be61588b2b4",
      "4eccd5eed8044c38ab684ca45eba2fac",
      "26e576ffba414bad8f699009b5817ad4",
      "e4c7d57280f044fda3b9c33fa77767fb",
      "80eb807346ee47beb13e507981dff31d",
      "d49cc3dc643244bbaefceebf8aa329e4",
      "f94110bd59494e82a54f2b09b0f5aa36",
      "6903fc948e97465c86d95c5637fe8e0d",
      "e7a8fa985fef4035a381fb05638c102d",
      "b723bca2dbce4fd9b3c4428d7db5dbf5",
      "e7e7a2c991e044eca2293381a6fa0154",
      "c033424ce2c6441caaba0e735649bf70",
      "21ecadd921f84b68848ca3426d87857a",
      "34638e9e60ad4d93a3273437b25d1ac5",
      "317d6ead0228402e9639a1c7b1a31667",
      "c985c1d8ebed47df980153c3dea27c1c",
      "e54f3202282b46d292b96777ca5d1734",
      "3b0915db59fa4d3e81d1c32195c0bbc7",
      "2f1f280c075e49d58be0f12318303111",
      "fca0ad25d6f84c618b38d2c75536337a",
      "3afb5ed10e7240c283dccf0726b622d3",
      "f99e2494dbab426dbf2fea59e53f7ca7",
      "d540c1c2bbbf4bfb8c89c9cc3e58001a",
      "48a845e47b9d45dfa57eb9a40a65c3ea",
      "aa052a4889ae4fd8848e3005e9e26eb0",
      "a2113ab8508a45c1a7e0ea4b8ee7811d",
      "0ca3033bf2d641c8b294df0fc76e1bf7",
      "dfdfe8cd36d2444193232e8ac8f7dd2c",
      "a428fde91d5043baa0cd44fe844ca79e",
      "933cf6220c8c48109ce49ba7d6f43a08",
      "8b42130098fa41ef9bcbcf2b0d86cf0b",
      "2cb1c192fb884bfe84c34894d892f805",
      "9851a58505a1421dbb64840a1da06de5",
      "1ec61c7f469f4c2e94755826cd3ead21",
      "f424a4e43632411d8077e001e6bdb1b9",
      "9d4f6a2b10d54b02bae1ea83d3ebc19a",
      "bd3fec9ed50747cdb5ddabf4bc47cd2e",
      "9e10831462d241639bf3afbd378587d9",
      "beaa8373cdb6426892f609400d70f302",
      "b8a0d2782f674fe5a0557c911f974d3f",
      "e9c16e853f78475887124ef264f3035c",
      "f8f0255bdfcc42e8a521767ff69c2294",
      "44442c56d7eb4446881fa3c181c1334b",
      "6a3073d08c0e4c558f2c4bed9c63e0dd",
      "6d8eb5e40949442bbc6537089b20b5c5",
      "5f9e43aab02b4daa85bf5d12466f1acd",
      "788f30ac37464487987ee74984e3516c",
      "948a7c5963f742ccb39259b9d8c14e0b",
      "85ee04c40f0747bc97ab529145fa8a68",
      "dfd68e999e1b435a9b6a12bc6ad16294",
      "7b6cd5b2360e48cb99d372c0416eaefb",
      "3388b2fde2d146ca9196839944a5a2b9",
      "c0006731cf6f48acba8b37e4d515b687",
      "4c4c1f17b2c241dd996862acb9be8db4"
     ]
    },
    "id": "xqX3Nqt56ttl",
    "outputId": "7cd3cff5-660e-40f3-b58e-a1cfd6b10957",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a4250dbb41549cb8f18585bec3b9c5c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/292 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15d908a381f0426fbd56b94430de55b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/565 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d63161325566473587e0a9c50c093210",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/416k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3d987c625c04404ba4aa0a44b99aa3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.06M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7e7a2c991e044eca2293381a6fa0154",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/134 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "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
      },
      "text/plain": [
       "  0%|          | 0/48 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9851a58505a1421dbb64840a1da06de5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a3073d08c0e4c558f2c4bed9c63e0dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11 [00:00<?, ?ba/s]"
      ]
     },
     "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": {
      "text/html": [
       "\n",
       "  <div id=\"df-a39b188d-8efc-4f2f-85e3-703a830fce40\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>0.994476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>حمله ارتش روسیه به اوکراین</td>\n",
       "      <td>World</td>\n",
       "      <td>0.985831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>افزایش نرخ تورم در کشور</td>\n",
       "      <td>Economy</td>\n",
       "      <td>0.852276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>آمار فوتی‌های کرونا</td>\n",
       "      <td>Society</td>\n",
       "      <td>0.993497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اقدامات شهرداری تهران</td>\n",
       "      <td>City</td>\n",
       "      <td>0.988388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>صعود ایران به جام جهانی</td>\n",
       "      <td>Sport</td>\n",
       "      <td>0.987941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>بیماری آبله میمونی</td>\n",
       "      <td>Science</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a39b188d-8efc-4f2f-85e3-703a830fce40')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-a39b188d-8efc-4f2f-85e3-703a830fce40 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-a39b188d-8efc-4f2f-85e3-703a830fce40');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "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": {
      "text/html": [
       "\n",
       "  <div id=\"df-502c8436-5274-4a10-9851-7ee887811dca\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>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",
       "      <th>Culture</th>\n",
       "      <th>IT</th>\n",
       "      <th>LifeSkills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Politics</th>\n",
       "      <td>2026</td>\n",
       "      <td>82</td>\n",
       "      <td>85</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>88</td>\n",
       "      <td>288</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    <tr>\n",
       "      <th>LifeSkills</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-502c8436-5274-4a10-9851-7ee887811dca')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-502c8436-5274-4a10-9851-7ee887811dca button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-502c8436-5274-4a10-9851-7ee887811dca');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "id": "t76h-25dj39y"
   },
   "source": [
    "<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>"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "News-Classification.ipynb",
   "provenance": []
  },
  "gpuClass": "standard",
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "0a4250dbb41549cb8f18585bec3b9c5c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_fce2351d56994af3a5c0315d8869d305",
       "IPY_MODEL_29496b4c5e0740eb877fbf2d23bd899e",
       "IPY_MODEL_6cd049c9ea9d4a5983624285d32ca36e"
      ],
      "layout": "IPY_MODEL_67f88c33b18b4b75a70205f706f97f09"
     }
    },
    "0ca3033bf2d641c8b294df0fc76e1bf7": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "0d027469e6f944ad80471234db5ccfaa": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "15d908a381f0426fbd56b94430de55b1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_69ea65d3288e4fcea3f01b37f8218d11",
       "IPY_MODEL_9f2fe5b01c704480aab6be4478938491",
       "IPY_MODEL_c634f5e4536844c19df9f412b51bbce6"
      ],
      "layout": "IPY_MODEL_61fccaea3c6b479f9dec4e33f22e685c"
     }
    },
    "1b3f13f817eb412f85d64de718a0ddf6": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "1cd22488e9b64330b9a16cfbbc8e03a4": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "1ec61c7f469f4c2e94755826cd3ead21": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_9e10831462d241639bf3afbd378587d9",
      "placeholder": "​",
      "style": "IPY_MODEL_beaa8373cdb6426892f609400d70f302",
      "value": "100%"
     }
    },
    "21ecadd921f84b68848ca3426d87857a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_3b0915db59fa4d3e81d1c32195c0bbc7",
      "max": 134,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_2f1f280c075e49d58be0f12318303111",
      "value": 134
     }
    },
    "24771d2561fd41f8ba9b54677d715745": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "254b0fa4c48845b88a731471e7d1782c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "26e576ffba414bad8f699009b5817ad4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e7a8fa985fef4035a381fb05638c102d",
      "placeholder": "​",
      "style": "IPY_MODEL_b723bca2dbce4fd9b3c4428d7db5dbf5",
      "value": " 1.06M/1.06M [00:00&lt;00:00, 1.35MB/s]"
     }
    },
    "2723fa305a1f41cbb1ec6ee7227a7164": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "29496b4c5e0740eb877fbf2d23bd899e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_85b5f1fbba34444886d91c821ef7ed1b",
      "max": 292,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_254b0fa4c48845b88a731471e7d1782c",
      "value": 292
     }
    },
    "2cb1c192fb884bfe84c34894d892f805": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "2f1f280c075e49d58be0f12318303111": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "305da9204d634e08b74cb9cb9471a783": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "317d6ead0228402e9639a1c7b1a31667": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "3388b2fde2d146ca9196839944a5a2b9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "34638e9e60ad4d93a3273437b25d1ac5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_fca0ad25d6f84c618b38d2c75536337a",
      "placeholder": "​",
      "style": "IPY_MODEL_3afb5ed10e7240c283dccf0726b622d3",
      "value": " 134/134 [00:00&lt;00:00, 4.04kB/s]"
     }
    },
    "3590a192b5ff424eba3ef7e7685e9576": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_3a3de175272a45c587f93a529b8bda0c",
      "placeholder": "​",
      "style": "IPY_MODEL_b706541bc94a4ffbba05aae29f5db7ec",
      "value": " 416k/416k [00:00&lt;00:00, 1.65MB/s]"
     }
    },
    "39ea7e424f6e405ab55014ff6bcf57e9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "3a3de175272a45c587f93a529b8bda0c": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "3afb5ed10e7240c283dccf0726b622d3": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "3b0915db59fa4d3e81d1c32195c0bbc7": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "42e548498ed6450d9e9fe392024f0e1f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "44442c56d7eb4446881fa3c181c1334b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "48a845e47b9d45dfa57eb9a40a65c3ea": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_a428fde91d5043baa0cd44fe844ca79e",
      "max": 48,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_933cf6220c8c48109ce49ba7d6f43a08",
      "value": 48
     }
    },
    "4c4c1f17b2c241dd996862acb9be8db4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "4e6ee148818d4db895dbe9aab4768493": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_f0ad55b6958447b98f0dc90ddd376f3f",
       "IPY_MODEL_a98f62a3ceaa4be98aecdb6a469ecc0f",
       "IPY_MODEL_9b0eb70307384a93810c6a347727d833"
      ],
      "layout": "IPY_MODEL_81a94d36714f491db689cd9a19aef03d"
     }
    },
    "4eccd5eed8044c38ab684ca45eba2fac": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_f94110bd59494e82a54f2b09b0f5aa36",
      "max": 1108824,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_6903fc948e97465c86d95c5637fe8e0d",
      "value": 1108824
     }
    },
    "5f9e43aab02b4daa85bf5d12466f1acd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7b6cd5b2360e48cb99d372c0416eaefb",
      "max": 11,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_3388b2fde2d146ca9196839944a5a2b9",
      "value": 11
     }
    },
    "61fccaea3c6b479f9dec4e33f22e685c": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "67f88c33b18b4b75a70205f706f97f09": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6903fc948e97465c86d95c5637fe8e0d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "69ea65d3288e4fcea3f01b37f8218d11": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_1cd22488e9b64330b9a16cfbbc8e03a4",
      "placeholder": "​",
      "style": "IPY_MODEL_dcffc897809f4db0bb083268e81f2b79",
      "value": "Downloading: 100%"
     }
    },
    "6a3073d08c0e4c558f2c4bed9c63e0dd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_6d8eb5e40949442bbc6537089b20b5c5",
       "IPY_MODEL_5f9e43aab02b4daa85bf5d12466f1acd",
       "IPY_MODEL_788f30ac37464487987ee74984e3516c"
      ],
      "layout": "IPY_MODEL_948a7c5963f742ccb39259b9d8c14e0b"
     }
    },
    "6aa88d25b7fe444483ca90d783ba2789": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "6cd049c9ea9d4a5983624285d32ca36e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_1b3f13f817eb412f85d64de718a0ddf6",
      "placeholder": "​",
      "style": "IPY_MODEL_e88fb1dd95c6474d82cab28636752fdb",
      "value": " 292/292 [00:00&lt;00:00, 3.73kB/s]"
     }
    },
    "6d8eb5e40949442bbc6537089b20b5c5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_85ee04c40f0747bc97ab529145fa8a68",
      "placeholder": "​",
      "style": "IPY_MODEL_dfd68e999e1b435a9b6a12bc6ad16294",
      "value": "100%"
     }
    },
    "6e1fa17badaa4e559c4a437ee3c18ad6": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "76e5193b3bd44a8584c2b858441c7435": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "788f30ac37464487987ee74984e3516c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c0006731cf6f48acba8b37e4d515b687",
      "placeholder": "​",
      "style": "IPY_MODEL_4c4c1f17b2c241dd996862acb9be8db4",
      "value": " 11/11 [00:00&lt;00:00, 21.01ba/s]"
     }
    },
    "7b6cd5b2360e48cb99d372c0416eaefb": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7ed3a0ad9741490191eeda10f5301611": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "80eb807346ee47beb13e507981dff31d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "81a94d36714f491db689cd9a19aef03d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "85b5f1fbba34444886d91c821ef7ed1b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "85ee04c40f0747bc97ab529145fa8a68": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "8b42130098fa41ef9bcbcf2b0d86cf0b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "933cf6220c8c48109ce49ba7d6f43a08": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "948a7c5963f742ccb39259b9d8c14e0b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9851a58505a1421dbb64840a1da06de5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_1ec61c7f469f4c2e94755826cd3ead21",
       "IPY_MODEL_f424a4e43632411d8077e001e6bdb1b9",
       "IPY_MODEL_9d4f6a2b10d54b02bae1ea83d3ebc19a"
      ],
      "layout": "IPY_MODEL_bd3fec9ed50747cdb5ddabf4bc47cd2e"
     }
    },
    "9b0eb70307384a93810c6a347727d833": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e14b8037f6134a88ad117a3fd427fbf1",
      "placeholder": "​",
      "style": "IPY_MODEL_fed517506bd342b3b35981c55292adf4",
      "value": " 452M/452M [00:08&lt;00:00, 55.7MB/s]"
     }
    },
    "9d4f6a2b10d54b02bae1ea83d3ebc19a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_f8f0255bdfcc42e8a521767ff69c2294",
      "placeholder": "​",
      "style": "IPY_MODEL_44442c56d7eb4446881fa3c181c1334b",
      "value": " 11/11 [00:00&lt;00:00, 23.46ba/s]"
     }
    },
    "9e10831462d241639bf3afbd378587d9": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9f2fe5b01c704480aab6be4478938491": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7ed3a0ad9741490191eeda10f5301611",
      "max": 565,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_42e548498ed6450d9e9fe392024f0e1f",
      "value": 565
     }
    },
    "a2113ab8508a45c1a7e0ea4b8ee7811d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a35b627b057b44168844bc5c253903f1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "a428fde91d5043baa0cd44fe844ca79e": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a98f62a3ceaa4be98aecdb6a469ecc0f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_76e5193b3bd44a8584c2b858441c7435",
      "max": 473451616,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_b5826453722b451f9e18fe0ad9f2845b",
      "value": 473451616
     }
    },
    "aa052a4889ae4fd8848e3005e9e26eb0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_8b42130098fa41ef9bcbcf2b0d86cf0b",
      "placeholder": "​",
      "style": "IPY_MODEL_2cb1c192fb884bfe84c34894d892f805",
      "value": " 48/48 [00:02&lt;00:00, 15.74ba/s]"
     }
    },
    "b25f8bee169b4c39a0b3e930b2805c6c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "b5826453722b451f9e18fe0ad9f2845b": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "b706541bc94a4ffbba05aae29f5db7ec": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b723bca2dbce4fd9b3c4428d7db5dbf5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b8a0d2782f674fe5a0557c911f974d3f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "b925b7826a22488eaf7cfda65402d35f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "bd3fec9ed50747cdb5ddabf4bc47cd2e": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "beaa8373cdb6426892f609400d70f302": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "c0006731cf6f48acba8b37e4d515b687": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "c033424ce2c6441caaba0e735649bf70": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_c985c1d8ebed47df980153c3dea27c1c",
      "placeholder": "​",
      "style": "IPY_MODEL_e54f3202282b46d292b96777ca5d1734",
      "value": "Downloading: 100%"
     }
    },
    "c634f5e4536844c19df9f412b51bbce6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_6aa88d25b7fe444483ca90d783ba2789",
      "placeholder": "​",
      "style": "IPY_MODEL_de8c250ca5824385bf09eeac4fa0f9cc",
      "value": " 565/565 [00:00&lt;00:00, 9.69kB/s]"
     }
    },
    "c985c1d8ebed47df980153c3dea27c1c": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "cfa13ea00bbc48d388838be61588b2b4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_80eb807346ee47beb13e507981dff31d",
      "placeholder": "​",
      "style": "IPY_MODEL_d49cc3dc643244bbaefceebf8aa329e4",
      "value": "Downloading: 100%"
     }
    },
    "d49cc3dc643244bbaefceebf8aa329e4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "d540c1c2bbbf4bfb8c89c9cc3e58001a": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_0ca3033bf2d641c8b294df0fc76e1bf7",
      "placeholder": "​",
      "style": "IPY_MODEL_dfdfe8cd36d2444193232e8ac8f7dd2c",
      "value": "100%"
     }
    },
    "d63161325566473587e0a9c50c093210": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_dcc994a80fc745fe9d4f6b7291c85365",
       "IPY_MODEL_dcb24a531a934bc09695c07802e81e0f",
       "IPY_MODEL_3590a192b5ff424eba3ef7e7685e9576"
      ],
      "layout": "IPY_MODEL_305da9204d634e08b74cb9cb9471a783"
     }
    },
    "dcb24a531a934bc09695c07802e81e0f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_6e1fa17badaa4e559c4a437ee3c18ad6",
      "max": 426422,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_b25f8bee169b4c39a0b3e930b2805c6c",
      "value": 426422
     }
    },
    "dcc994a80fc745fe9d4f6b7291c85365": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_2723fa305a1f41cbb1ec6ee7227a7164",
      "placeholder": "​",
      "style": "IPY_MODEL_39ea7e424f6e405ab55014ff6bcf57e9",
      "value": "Downloading: 100%"
     }
    },
    "dcffc897809f4db0bb083268e81f2b79": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "de8c250ca5824385bf09eeac4fa0f9cc": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "dfd68e999e1b435a9b6a12bc6ad16294": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "dfdfe8cd36d2444193232e8ac8f7dd2c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e14b8037f6134a88ad117a3fd427fbf1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e3d987c625c04404ba4aa0a44b99aa3d": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_cfa13ea00bbc48d388838be61588b2b4",
       "IPY_MODEL_4eccd5eed8044c38ab684ca45eba2fac",
       "IPY_MODEL_26e576ffba414bad8f699009b5817ad4"
      ],
      "layout": "IPY_MODEL_e4c7d57280f044fda3b9c33fa77767fb"
     }
    },
    "e4c7d57280f044fda3b9c33fa77767fb": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e54f3202282b46d292b96777ca5d1734": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e7a8fa985fef4035a381fb05638c102d": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e7e7a2c991e044eca2293381a6fa0154": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_c033424ce2c6441caaba0e735649bf70",
       "IPY_MODEL_21ecadd921f84b68848ca3426d87857a",
       "IPY_MODEL_34638e9e60ad4d93a3273437b25d1ac5"
      ],
      "layout": "IPY_MODEL_317d6ead0228402e9639a1c7b1a31667"
     }
    },
    "e88fb1dd95c6474d82cab28636752fdb": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "e9c16e853f78475887124ef264f3035c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "f0ad55b6958447b98f0dc90ddd376f3f": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_0d027469e6f944ad80471234db5ccfaa",
      "placeholder": "​",
      "style": "IPY_MODEL_24771d2561fd41f8ba9b54677d715745",
      "value": "Downloading: 100%"
     }
    },
    "f424a4e43632411d8077e001e6bdb1b9": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b8a0d2782f674fe5a0557c911f974d3f",
      "max": 11,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_e9c16e853f78475887124ef264f3035c",
      "value": 11
     }
    },
    "f8f0255bdfcc42e8a521767ff69c2294": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f94110bd59494e82a54f2b09b0f5aa36": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f99e2494dbab426dbf2fea59e53f7ca7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_d540c1c2bbbf4bfb8c89c9cc3e58001a",
       "IPY_MODEL_48a845e47b9d45dfa57eb9a40a65c3ea",
       "IPY_MODEL_aa052a4889ae4fd8848e3005e9e26eb0"
      ],
      "layout": "IPY_MODEL_a2113ab8508a45c1a7e0ea4b8ee7811d"
     }
    },
    "fca0ad25d6f84c618b38d2c75536337a": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "fce2351d56994af3a5c0315d8869d305": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_b925b7826a22488eaf7cfda65402d35f",
      "placeholder": "​",
      "style": "IPY_MODEL_a35b627b057b44168844bc5c253903f1",
      "value": "Downloading: 100%"
     }
    },
    "fed517506bd342b3b35981c55292adf4": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}