{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!unzip -q /content/en-ner-conll-2003.zip -d /content/"
      ],
      "metadata": {
        "id": "am0MK_BNa4eB"
      },
      "id": "am0MK_BNa4eB",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "36c02fac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36c02fac",
        "outputId": "8ac1563f-464c-493e-9f1b-92931af624c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchtext/vocab/__init__.py:4: UserWarning: \n",
            "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
            "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
            "  warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
            "/usr/local/lib/python3.10/dist-packages/torchtext/utils.py:4: UserWarning: \n",
            "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
            "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
            "  warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import gensim\n",
        "from gensim.models import KeyedVectors\n",
        "import numpy as np\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.regularizers import l2\n",
        "from collections import Counter\n",
        "from torchtext.vocab import vocab\n",
        "from tqdm.notebook import tqdm\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "db84429c",
      "metadata": {
        "id": "db84429c"
      },
      "source": [
        "### Declare path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "fc2539ae",
      "metadata": {
        "id": "fc2539ae"
      },
      "outputs": [],
      "source": [
        "data_dir_path = 'en-ner-conll-2003'\n",
        "train_path = os.path.join(data_dir_path, 'train', 'train.tsv')\n",
        "dev_texts_path = os.path.join(data_dir_path, 'dev-0', 'in.tsv')\n",
        "dev_labels_path = os.path.join(data_dir_path, 'dev-0', 'expected.tsv')\n",
        "dev_predicted_path = os.path.join(data_dir_path, 'dev-0', 'out.tsv')\n",
        "test_texts_path = os.path.join(data_dir_path, 'test-A', 'in.tsv')\n",
        "test_predicted_path = os.path.join(data_dir_path, 'test-A', 'out.tsv')\n",
        "word2vec_file_path = 'word2vec_100_3_polish.bin'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4ea0458",
      "metadata": {
        "id": "e4ea0458"
      },
      "source": [
        "### Load files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "4e038df7",
      "metadata": {
        "id": "4e038df7"
      },
      "outputs": [],
      "source": [
        "train_data = pd.read_csv(train_path, sep='\\t', usecols=[0, 1], header=None, names=['label', 'text'])\n",
        "dev_texts_data = pd.read_csv(dev_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])\n",
        "dev_labels_data = pd.read_csv(dev_labels_path, sep='\\t', usecols=[0], header=None, names=['label'])\n",
        "test_texts_data = pd.read_csv(test_texts_path, sep='\\t', usecols=[0], header=None, names=['text'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2bc73a6",
      "metadata": {
        "id": "f2bc73a6"
      },
      "source": [
        "### Build Vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "e0022f7b",
      "metadata": {
        "id": "e0022f7b"
      },
      "outputs": [],
      "source": [
        "def build_vocab(dataset):\n",
        "    counter = Counter()\n",
        "    for document in dataset:\n",
        "        counter.update(document)\n",
        "    return vocab(counter, specials=[\"<unk>\", \"<pad>\", \"<bos>\", \"<eos>\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "7d9fb428",
      "metadata": {
        "id": "7d9fb428"
      },
      "outputs": [],
      "source": [
        "train_X = train_data['text'].apply(lambda x: gensim.utils.simple_preprocess(x))\n",
        "v = build_vocab(train_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "7e793633",
      "metadata": {
        "id": "7e793633"
      },
      "outputs": [],
      "source": [
        "itos = v.get_itos()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "d3cca05a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3cca05a",
        "outputId": "3b611d13-e0a8-442b-bd0f-91b07935f0ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<unk>', '<pad>', '<bos>', '<eos>', 'eu', 'rejects', 'german', 'call', 'to', 'boycott', 'british', 'lamb', 'peter', 'blackburn', 'brussels', 'the', 'european', 'commission', 'said', 'on', 'thursday', 'it', 'disagreed', 'with', 'advice', 'consumers', 'shun', 'until', 'scientists', 'determine', 'whether', 'mad', 'cow', 'disease', 'can', 'be', 'transmitted', 'sheep', 'germany', 'representative', 'union', 'veterinary', 'committee', 'werner', 'zwingmann', 'wednesday', 'should', 'buy', 'sheepmeat', 'from', 'countries', 'other', 'than', 'britain', 'scientific', 'was', 'clearer', 'we', 'do', 'support', 'any', 'such', 'recommendation', 'because', 'see', 'grounds', 'for', 'chief', 'spokesman', 'nikolaus', 'van', 'der', 'pas', 'told', 'news', 'briefing', 'he', 'further', 'study', 'required', 'and', 'if', 'found', 'that', 'action', 'needed', 'taken', 'by', 'proposal', 'last', 'month', 'farm', 'commissioner', 'franz', 'fischler', 'ban', 'brains', 'spleens', 'spinal', 'cords', 'human', 'animal', 'food', 'chains', 'highly', 'specific', 'precautionary', 'move', 'protect', 'health', 'proposed', 'wide', 'measures', 'after', 'reports', 'france', 'under', 'laboratory', 'conditions', 'could', 'contract', 'bovine', 'spongiform', 'encephalopathy', 'bse', 'but', 'agreed', 'review', 'his', 'standing', 'mational', 'officials', 'questioned', 'justified', 'as', 'there', 'only', 'slight', 'risk', 'spanish', 'minister', 'loyola', 'de', 'palacio', 'had', 'earlier', 'accused', 'at', 'an', 'ministers', 'meeting', 'of', 'causing', 'unjustified', 'alarm', 'through', 'dangerous', 'generalisation', 'backed', 'committees', 'are', 'due', 're', 'examine', 'issue', 'early', 'next', 'make', 'recommendations', 'senior', 'have', 'long', 'been', 'known', 'scrapie', 'brain', 'wasting', 'similar', 'which', 'is', 'believed', 'transferred', 'cattle', 'feed', 'containing', 'waste', 'farmers', 'denied', 'danger', 'their', 'expressed', 'concern', 'government', 'avoid', 'might', 'influence', 'across', 'europe', 'what', 'extremely', 'careful', 'how', 'going', 'take', 'lead', 'welsh', 'national', 'nfu', 'chairman', 'john', 'lloyd', 'jones', 'bbc', 'radio', 'bonn', 'has', 'led', 'efforts', 'public', 'consumer', 'confidence', 'collapsed', 'in', 'march', 'report', 'suggested', 'humans', 'illness', 'eating', 'contaminated', 'beef', 'imported', 'year', 'nearly', 'half', 'total', 'imports', 'brought', 'tonnes', 'mutton', 'some', 'percent', 'overall', 'rare', 'hendrix', 'song', 'draft', 'sells', 'almost', 'london', 'handwritten', 'guitar', 'legend', 'jimi', 'sold', 'auction', 'late', 'musician', 'favourite', 'possessions', 'florida', 'restaurant', 'paid', 'pounds', 'ai', 'no', 'telling', 'penned', 'piece', 'hotel', 'stationery', 'end', 'january', 'concert', 'english', 'city', 'nottingham', 'threw', 'sheet', 'paper', 'into', 'audience', 'where', 'retrieved', 'fan', 'buyers', 'also', 'snapped', 'up', 'items', 'were', 'put', 'former', 'girlfriend', 'kathy', 'etchingham', 'who', 'lived', 'him', 'they', 'included', 'black', 'lacquer', 'mother', 'pearl', 'inlaid', 'box', 'used', 'store', 'drugs', 'anonymous', 'australian', 'purchaser', 'bought', 'guitarist', 'died', 'overdose', 'aged', 'china', 'says', 'taiwan', 'spoils', 'atmosphere', 'talks', 'beijing', 'taipei', 'spoiling', 'resumption', 'strait', 'visit', 'ukraine', 'taiwanese', 'vice', 'president', 'lien', 'chan', 'this', 'week', 'infuriated', 'speaking', 'hours', 'chinese', 'state', 'media', 'time', 'right', 'engage', 'political', 'foreign', 'ministry', 'shen', 'guofang', 'reuters', 'necessary', 'opening', 'disrupted', 'authorities', 'quoted', 'top', 'negotiator', 'tang', 'shubei', 'visiting', 'group', 'rivals', 'hold', 'now', 'two', 'sides', 'hostility', 'overseas', 'edition', 'people', 'daily', 'saying', 'television', 'interview', 'read', 'comments', 'gave', 'details', 'why', 'considered', 'considers', 'renegade', 'province', 'opposed', 'all', 'gain', 'greater', 'international', 'recognition', 'rival', 'island', 'practical', 'steps', 'towards', 'goal', 'consultations', 'held', 'set', 'format', 'official', 'xinhua', 'agency', 'executive', 'association', 'relations', 'straits', 'july', 'car', 'registrations', 'pct', 'yr', 'frankfurt', 'first', 'motor', 'vehicles', 'jumped', 'period', 'federal', 'office', 'new', 'cars', 'registered', 'passenger', 'trucks', 'figures', 'represent', 'increase', 'decline', 'bike', 'registration', 'rose', 'growth', 'partly', 'increased', 'number', 'germans', 'buying', 'abroad', 'while', 'manufacturers', 'domestic', 'demand', 'weak', 'posted', 'gains', 'numbers', 'volkswagen', 'ag', 'won', 'slightly', 'more', 'quarter', 'opel', 'together', 'general', 'motors', 'came', 'second', 'place', 'figure', 'third', 'ford', 'or', 'seat', 'porsche', 'fewer', 'compared', 'fell', 'greek', 'socialists', 'give', 'green', 'light', 'pm', 'elections', 'athens', 'socialist', 'party', 'bureau', 'prime', 'costas', 'simitis', 'snap', 'its', 'secretary', 'skandalidis', 'reporters', 'announcement', 'cabinet', 'later', 'dimitris', 'kontogiannis', 'newsroom', 'bayervb', 'sets', 'million', 'six', 'bond', 'following', 'announced', 'manager', 'toronto', 'dominion', 'borrower', 'bayerische', 'vereinsbank', 'amt', 'mln', 'coupon', 'maturity', 'sep', 'type', 'straight', 'iss', 'price', 'pay', 'date', 'full', 'fees', 'reoffer', 'spread', 'bp', 'moody', 'aa', 'listing', 'lux', 'freq', 'denoms', 'sale', 'limits', 'us', 'uk', 'ca', 'neg', 'plg', 'crs', 'deflt', 'force', 'maj', 'gov', 'law', 'home', 'ctry', 'tax', 'provs', 'standard', 'mgt', 'und', 'sell', 'conc', 'praecip', 'underlying', 'govt', 'sept', 'notes', 'joint', 'venantius', 'frn', 'floating', 'rate', 'lehman', 'brothers', 'ab', 'swedish', 'mortgage', 'jan', 'base', 'libor', 'short', 'jp', 'fr', 'yes', 'ipma', 'sweden', 'issued', 'off', 'emtn', 'programme', 'port', 'update', 'syria', 'lloyds', 'shipping', 'intelligence', 'service', 'lattakia', 'aug', 'waiting', 'tartous', 'presently', 'israel', 'plays', 'down', 'fears', 'war', 'colleen', 'siegel', 'jerusalem', 'outgoing', 'peace', 'current', 'tensions', 'between', 'appeared', 'storm', 'teacup', 'itamar', 'rabinovich', 'ambassador', 'washington', 'conducted', 'unfruitful', 'negotiations', 'looked', 'like', 'damascus', 'wanted', 'talk', 'rather', 'fight', 'appears', 'me', 'syrian', 'priority', 'still', 'negotiate', 'syrians', 'confused', 'definitely', 'tense', 'assessment', 'here', 'essentially', 'winding', 'term', 'will', 'replaced', 'eliahu', 'ben', 'elissar', 'israeli', 'envoy', 'egypt', 'wing', 'likud', 'politician', 'sent', 'message', 'via', 'committed', 'open', 'without', 'preconditions', 'slammed', 'creating', 'called', 'launching', 'hysterical', 'campaign', 'against', 'reported', 'recently', 'test', 'fired', 'missile', 'arms', 'purchases', 'defensive', 'purposes', 'hafez', 'al', 'assad', 'ready', 'enter', 'david', 'levy', 'tension', 'mounted', 'since', 'benjamin', 'netanyahu', 'took', 'june', 'vowing', 'retain', 'golan', 'heights', 'captured', 'middle', 'east', 'deadlocked', 'over', 'despite', 'previous', 'willingness', 'concessions', 'february', 'voices', 'coming', 'out', 'bad', 'not', 'good', 'expressions', 'declarations', 'must', 'worrying', 'artificial', 'very', 'those', 'become', 'prisoners', 'expect', 'face', 'answer', 'our', 'want', 'god', 'forbid', 'one', 'benefits', 'wars', 'channel', 'calming', 'signal', 'source', 'spokesmen', 'confirm', 'messages', 'reassure', 'cairo', 'united', 'states', 'moscow', 'polish', 'diplomat', 'denies', 'nurses', 'stranded', 'libya', 'tunis', 'tabloid', 'refusing', 'exit', 'visas', 'trying', 'return', 'working', 'north', 'african', 'country', 'true', 'today', 'knowledge', 'nurse', 'kept', 'her', 'received', 'complaint', 'embassy', 'charge', 'affaires', 'tripoli', 'tadeusz', 'awdankiewicz', 'telephone', 'poland', 'labour', 'would', 'send', 'team', 'investigate', 'probe', 'prompted', 'complaining', 'about', 'work', 'non', 'payment', 'salaries', 'estimated', 'iranian', 'opposition', 'leaders', 'meet', 'baghdad', 'hassan', 'hafidh', 'exile', 'based', 'iraq', 'vowed', 'extend', 'iran', 'kurdish', 'rebels', 'attacked', 'troops', 'deep', 'inside', 'mujahideen', 'khalq', 'statement', 'leader', 'massoud', 'rajavi', 'met', 'kurdistan', 'democratic', 'kdpi', 'rastegar', 'voiced', 'rebel', 'kurds', 'emphasised', 'resistance', 'continue', 'stand', 'side', 'compatriots', 'movement', 'signals', 'level', 'cooperation', 'oppositions', 'heavily', 'bombarded', 'targets', 'northern', 'pursuit', 'guerrillas', 'iraqi', 'areas', 'outside', 'control', 'bordering', 'patriotic', 'puk', 'kdp', 'main', 'factions', 'forces', 'ousted', 'kuwait', 'gulf', 'clashes', 'parties', 'broke', 'weekend', 'most', 'serious', 'fighting', 'sponsored', 'ceasefire', 'shelling', 'positions', 'qasri', 'region', 'suleimaniya', 'near', 'border', 'days', 'killed', 'wounded', 'attack', 'both', 'turkey', 'mount', 'air', 'land', 'strikes', 'own', 'southern', 'protects', 'possible', 'attacks', 'saudi', 'riyal', 'rates', 'steady', 'quiet', 'summer', 'trade', 'manama', 'spot', 'dollar', 'interbank', 'deposit', 'mainly', 'dealers', 'kingdom', 'changes', 'market', 'holidays', 'dealer', 'deposits', 'three', 'months', 'funds', 'approves', 'arafat', 'flight', 'west', 'bank', 'palestinian', 'yasser', 'permission', 'fly', 'territory', 'ending', 'brief', 'plo', 'crisis', 'adviser', 'problem', 'aircraft', 'pass', 'airspace', 'expected', 'travel', 'before', 'monday', 'nabil', 'abu', 'rdainah', 'scheduled', 'shimon', 'peres', 'town', 'ramallah', 'venue', 'changed', 'gaza', 'clearance', 'helicopters', 'stop', 'keeping', 'grounded', 'subsequently', 'cancelled', 'civilian', 'affairs', 'allenby', 'bridge', 'crossing', 'jordan', 'decided', 'flying', 'lifted', 'busy', 'schedule', 'free', 'palestinians', 'barred', 'erez', 'checkpoint', 'planned', 'overflying', 'helicopter', 'attempt', 'bar', 'defeated', 'may', 'undermine', 'authority', 'conduct', 'afghan', 'uae', 'taleban', 'guards', 'hilary', 'gush', 'dubai', 'arab', 'emirates', 'russian', 'hostages', 'escaped', 'militia', 'afghanistan', 'few', 'dhabi', 'touch', 'issuing', 'them', 'documents', 'homeland', 'objection', 'added', 'islamic', 'overpowered', 'seven', 'aircrew', 'sharjah', 'friday', 'board', 'captivity', 'kandahar', 'hand', 'red', 'crescent', 'possibly', 'tuesday', 'silent', 'when', 'asked', 'back', 'capital', 'kabul', 'headquarters', 'controlled', 'burhanuddin', 'rabbani', 'overthrow', 'men', 'currently', 'did', 'elaborate', 'russians', 'aerostan', 'firm', 'republic', 'tatarstan', 'hostage', 'mig', 'fighter', 'forced', 'cargo', 'plane', 'august', 'shipment', 'ammunition', 'albania', 'evidence', 'military', 'crew', 'nationality', 'coincidental', 'numerous', 'diplomatic', 'attempts', 'failed', 'armed', 'kalashnikov', 'automatic', 'rifles', 'doing', 'regular', 'maintenance', 'ilyushin', 'left', 'sunday', 'saddam', 'meets', 'russia', 'zhirinovsky', 'hussein', 'ultra', 'nationalist', 'vladimir', 'maintain', 'friendship', 'newspapers', 'stressed', 'during', 'keenness', 'papers', 'liberal', 'duma', 'parliament', 'calling', 'immediate', 'lifting', 'embargo', 'imposed', 'invasion', 'press', 'help', 'sanctions', 'blamed', 'delaying', 'establishment', 'ties', 'namely', 'economic', 'resume', 'visited', 'twice', 'october', 'invited', 'attend', 'referendum', 'presidency', 'extended', 'years', 'digest', 'these', 'leading', 'stories', 'verified', 'does', 'vouch', 'accuracy', 'thawra', 'turkish', 'part', 'fair', 'november', 'shipload', 'rice', 'arrives', 'umm', 'qasr', 'lebanon', 'beirut', 'nahar', 'confrontation', 'escalating', 'hizbollah', 'hariri', 'threats', 'serve', 'safir', 'speaker', 'berri', 'preparing', 'parliamentary', 'battle', 'lists', 'prepared', 'anwar', 'continued', 'criticism', 'violation', 'incidents', 'occurred', 'ad', 'diyar', 'financial', 'pakistan', 'step', 'election', 'incomplete', 'list', 'nida', 'watan', 'maronite', 'patriarch', 'sfeir', 'sorrow', 'violations', 'cme', 'live', 'feeder', 'calls', 'range', 'mixed', 'chicago', 'futures', 'ranged', 'cent', 'higher', 'lower', 'livestock', 'analysts', 'strong', 'tone', 'cash', 'markets', 'prompt', 'outlook', 'bullish', 'lend', 'bull', 'spreading', 'however', 'likely', 'prices', 'drift', 'evening', 'ahead', 'record', 'amount', 'feedlot', 'traded', 'kindercare', 'debt', 'hit', 'results', 'montgomery', 'ala', 'learning', 'centers', 'inc', 'buyback', 'mean', 'extraordinary', 'loss', 'fiscal', 'company', 'began', 'par', 'value', 'outstanding', 'philip', 'maslowe', 'officer', 'preschool', 'child', 'care', 'offered', 'opportunity', 'reduce', 'weighted', 'average', 'interest', 'costs', 'improve', 'future', 'flows', 'earnings', 'research', 'alert', 'starts', 'snet', 'analyst', 'blake', 'bath', 'started', 'england', 'corp', 'outperform', 'rating', 'target', 'estimate', 'per', 'share', 'immediately', 'available', 'closed', 'auchard', 'wall', 'street', 'gateway', 'data', 'sciences', 'net', 'rises', 'phoenix', 'summary', 'consolidated', 'thousands', 'except', 'ended', 'jul', 'income', 'revenue', 'software', 'services', 'operating', 'balance', 'equivalents', 'assets', 'shareholders', 'equity', 'backing', 'chooses', 'le', 'monde', 'paris', 'afternoon', 'dated', 'front', 'page', 'africans', 'seeking', 'renew', 'obtain', 'residence', 'rights', 'say', 'alain', 'juppe', 'proposals', 'insufficient', 'hunger', 'strike', 'enters', 'th', 'day', 'church', 'rally', 'attracts', 'sympathisers', 'flnc', 'corsican', 'announces', 'truce', 'night', 'business', 'pages', 'shutdown', 'bally', 'french', 'factories', 'points', 'shoe', 'industry', 'undercut', 'low', 'wage', 'competition', 'failure', 'keep', 'abreast', 'trends', 'sud', 'ptt', 'telecom', 'elements', 'social', 'unrest', 'weeks', 'well', 'repairs', 'lift', 'heidrun', 'oil', 'output', 'statoil', 'oslo', 'plugged', 'water', 'injection', 'wells', 'oilfield', 'mid', 'norway', 'reopened', 'operator', 'den', 'norske', 'stats', 'oljeselskap', 'accounted', 'dip', 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'discovery', 'chopra', 'malley', 'walton', 'iain', 'romero', 'jimenez', 'antoine', 'lebouc', 'curry', 'affleck', 'sorensen', 'ekimov', 'giunluca', 'gorini', 'breukink', 'wilfried', 'peeters', 'bart', 'voskamp', 'randolph', 'lietti', 'mg', 'technogym', 'maarten', 'bakker', 'olaf', 'zevenaar', 'venray', 'dayer', 'darryn', 'fiedler', 'florian', 'rousseau', 'georgios', 'chimonetos', 'lampros', 'vasilopoulos', 'magali', 'faure', 'kathrin', 'freitag', 'felicia', 'ballanger', 'grichina', 'tanya', 'dubnicoff', 'annett', 'neumann', 'enioukhina', 'restricts', 'englishmen', 'skying', 'flighted', 'pavilion', 'inswinging', 'saqlain', 'edgbaston', 'lombardi', 'polti', 'clerq', 'jemison', 'servais', 'knaven', 'tunbridge', 'hove', 'athey', 'saggers', 'watkin', 'solanki', 'barnett', 'pooley', 'ramprakash', 'fay', 'mcgeechan', 'grobbelaar', 'bruce', 'zifa', 'pamire', 'duvillard', 'argyle', 'segers', 'fashanu', 'heng', 'suan', 'lim', 'pleaded', 'accepting', 'jenson', 'krizan', 'lila', 'osterloh', 'nanne', 'dahlman', 'slide', 'yankee', 'mulholland', 'kingdome', 'ejected', 'marzano', 'brushed', 'wengert', 'herrera', 'minors', 'baldwin', 'ozzie', 'guillen', 'nilsson', 'offerman', 'rick', 'huisman', 'hentgen', 'tossing', 'orel', 'hershiser', 'belle', 'yorkers', 'aggressiveness', 'adjusting', 'louder', 'messed', 'adrenalin', 'lanes', 'pitching', 'winningest', 'ron', 'wright', 'greenville', 'phenom', 'andruw', 'abdellaoui', 'graef', 'honour', 'assembled', 'quartets', 'anchor', 'autograph', 'ornate', 'dillard', 'lindy', 'remigino', 'hines', 'hasely', 'crawford', 'inspired', 'gina', 'tillman', 'bachini', 'emon', 'bois', 'bronckhorst', 'ferdy', 'positively', 'neighbourly', 'consulates', 'thessaloniki', 'observance', 'picks', 'bold', 'ogilvy', 'mather', 'communication', 'linked', 'divorces', 'romantically', 'petition', 'presenter', 'mocked', 'gymnasium', 'liffe', 'apt', 'automated', 'pit', 'tabular', 'gilt', 'eurolira', 'euroswiss', 'installs', 'kinshasa', 'cne', 'deployed', 'deadlines', 'yoka', 'lye', 'mudaba', 'progressively', 'maniema', 'shaba', 'bandundu', 'installation', 'mobutu', 'sese', 'seko', 'object', 'insists', 'ikimi', 'wanting', 'ministerial', 'continuation', 'saro', 'wiwa', 'restricted', 'beatrix', 'guns', 'forbidden', 'categories', 'djamena', 'idriss', 'deby', 'nomads', 'fraternite', 'matin', 'establishes', 'bauza', 'donwahi', 'douati', 'alphonse', 'illicit', 'swine', 'jour', 'lakpe', 'appoints', 'severin', 'kouame', 'tanny', 'mahala', 'zvornik', 'lieutenant', 'marriner', 'retaliation', 'angry', 'mob', 'barreled', 'lightning', 'berovo', 'cathedral', 'thunderstorm', 'kornblum', 'milosevic', 'greste', 'slobodan', 'defuse', 'manipulation', 'srpska', 'bolster', 'patron', 'rectify', 'expulsions', 'blessing', 'banja', 'luka', 'biljana', 'plavsic', 'oversee', 'mini', 'choosing', 'comprised', 'rbls', 'rao', 'roubles', 'ffk', 'illiquid', 'harder', 'artyom', 'danielyan', 'kosovo', 'boycotted', 'institutions', 'bajgora', 'donje', 'ljupce', 'podujevo', 'celopek', 'revoked', 'cracked', 'moderates', 'cradle', 'meningitis', 'emanuil', 'ceausu', 'babes', 'infectious', 'virus', 'lasts', 'gastro', 'intestinal', 'tract', 'vukasin', 'filipovic', 'supplying', 'staging', 'mismanagement', 'finmin', 'raul', 'matos', 'azocar', 'imf', 'hosted', 'seminar', 'metric', 'sail', 'cartagena', 'coffee', 'ocei', 'foods', 'jpr', 'cops', 'racket', 'thirteen', 'lago', 'nacion', 'credibility', 'undermined', 'scandals', 'indictment', 'piotti', 'dishonest', 'purged', 'brave', 'kandir', 'cetes', 'nervousness', 'ordinated', 'acceptances', 'pagares', 'masked', 'oaxaca', 'assaults', 'credits', 'oversupply', 'shortfall', 'inflow', 'lezama', 'newroom', 'chilpancingo', 'bickering', 'acatepec', 'fanned', 'intimidate', 'stirred', 'agir', 'ensemble', 'pour', 'droits', 'exerted', 'psychological', 'grabs', 'aguirre', 'pessina', 'restaurants', 'travelled', 'condemn', 'punish', 'landless', 'peasants', 'countryside', 'ecumenical', 'endemic', 'massacred', 'para', 'lucas', 'moreira', 'neves', 'bishops', 'flees', 'passport', 'consular', 'soliciting', 'icac', 'graft', 'bribe', 'scams', 'honduran', 'passports', 'thinks', 'solve', 'woes', 'rajapat', 'warnings', 'pullouts', 'chatichai', 'choonhavan', 'chavalit', 'yongchaiyudh', 'aspiration', 'insincere', 'slowdown', 'switched', 'monarchy', 'yassin', 'ramle', 'fundamentalist', 'wrecking', 'confined', 'freeing', 'skhirat', 'purely', 'vital', 'conlon', 'triumphal', 'abruptly', 'eavesdrop', 'conversations', 'campaigns', 'invaluable', 'captivated', 'worried', 'senator', 'dianne', 'feinstein', 'bump', 'dogged', 'wrongdoing', 'misconduct', 'questionable', 'judgment', 'selecting', 'advisers', 'revert', 'revelled', 'heartland', 'hoarse', 'voice', 'resolute', 'wit', 'stilted', 'glib', 'piper', 'jaffray', 'compiled', 'gen', 'muscular', 'dystrophy', 'technique', 'telethon', 'method', 'muscle', 'degenerates', 'adulthood', 'dystrophin', 'altered', 'minimise', 'susceptibility', 'injected', 'mice', 'bred', 'genes', 'experiment', 'fibers', 'diminishing', 'oilseeds', 'oilseed', 'assn', 'cargill', 'mitsui', 'dreyfus', 'dns', 'ww', 'donation', 'taichung', 'procurement', 'soybeans', 'bunge', 'pnw', 'pl', 'buyer', 'upland', 'sorghum', 'pima', 'poultry', 'fruits', 'gaining', 'quota', 'onic', 'panel', 'cutout', 'offal', 'moines', 'steer', 'uneven', 'goldstein', 'hiring', 'matched', 'heist', 'jewelry', 'showboat', 'capt', 'andrews', 'videotape', 'suitcase', 'resembling', 'wholesaler', 'schein', 'suitcases', 'closet', 'jewellers', 'twenties', 'distract', 'mcgilley', 'description', 'simpson', 'hints', 'financially', 'jury', 'gag', 'prohibits', 'acquittal', 'crying', 'blues', 'erroneous', 'jam', 'distanced', 'buffalo', 'wildly', 'supportive', 'gifts', 'supermarket', 'reprinted', 'editions', 'vitriol', 'journalism', 'dignify', 'surgeon', 'discarding', 'amputated', 'improperly', 'disposed', 'deformed', 'infant', 'prosthesis', 'orthopedic', 'educational', 'freezer', 'spoiled', 'crab', 'trap', 'flesh', 'cop', 'shrub', 'guys', 'bagged', 'checker', 'pines', 'toting', 'earl', 'feugill', 'camouflaged', 'shaggy', 'freeze', 'marines', 'attaching', 'strips', 'burlap', 'disguise', 'staking', 'robberies', 'stakeout', 'dla', 'logistics', 'nbc', 'consultant', 'reshaped', 'ire', 'repositioning', 'gluten', 'meal', 'pickup', 'pellets', 'decatur', 'il', 'cedar', 'rapids', 'ia', 'cereals', 'ecus', 'provisionally', 'optional', 'enjoys', 'subtracting', 'shipyard', 'cherbourg', 'yeard', 'cutback', 'indre', 'managements', 'curtail', 'estates', 'hub', 'gadoon', 'unearthed', 'cages', 'tando', 'allahyar', 'hyderabad', 'recorder', 'sui', 'technologists', 'sugarcane', 'pillaged', 'gujar', 'megawatt', 'mohib', 'textile', 'defaulted', 'leasing', 'modarabas', 'multiply', 'shahid', 'privatisation', 'sindh', 'restraining', 'javedan', 'dadabhoy', 'pvt', 'directions', 'occupying', 'rebelled', 'wrecked', 'mined', 'halo', 'roadsides', 'qotbuddin', 'hilal', 'officiated', 'sprinkling', 'petrol', 'andhra', 'pradesh', 'rarest', 'beings', 'roasted', 'appellants', 'plotted', 'elephant', 'tramples', 'rampaging', 'trampled', 'hari', 'maya', 'poudels', 'madhumalla', 'asleep', 'beast', 'kingdoms', 'elephants', 'overrun', 'kudapokuna', 'welikanda', 'vavuniya', 'undercover', 'vicorp', 'sabourin', 'cfo', 'denver', 'bestop', 'boulder', 'socal', 'edison', 'kilovolt', 'kv', 'wildfire', 'raging', 'conroy', 'crews', 'residues', 'megawatts', 'mw', 'containment', 'arid', 'windy', 'leong', 'munis', 'municipals', 'semiconductor', 'expectation', 'emerge', 'modem', 'networking', 'elias', 'moosa', 'roberston', 'pmc', 'chipsets', 'severance', 'randall', 'soundview', 'focusing', 'develops', 'shrink', 'hambrecht', 'quist', 'slowing', 'housecall', 'sink', 'underperform', 'hospice', 'limitation', 'subsidiaries', 'forma', 'oasis', 'gallagher', 'obscene', 'swore', 'hate', 'supermodel', 'noel', 'laryngitis', 'patsy', 'kensit', 'tickets', 'slough', 'upbeat', 'pretax', 'hammerson', 'warburg', 'stances', 'maintaining', 'rental', 'incomes', 'shaved', 'nigel', 'mobbs', 'birt', 'shrinks', 'seasonally', 'canadians', 'burea', 'shaken', 'backdrop', 'espionage', 'rings', 'jomhuri', 'eslami', 'photographing', 'pan', 'turkism', 'exiles', 'fallahiyan', 'distillates', 'kerosene', 'sort', 'noticeable', 'differential', 'ports', 'exceed', 'cargoes', 'lowest', 'suspicious', 'overhang', 'sulphur', 'manuscript', 'thieves', 'testament', 'akhbar', 'undated', 'pretended', 'wyche', 'invoked', 'recess', 'confirming', 'predecessor', 'mabus', 'shops', 'shutters', 'exporters', 'purchasing', 'destinations', 'southland', 'mainframe', 'delivering', 'constitutes', 'contends', 'nba', 'continually', 'baxter', 'immuno', 'miilion', 'secures', 'cos', 'chipset', 'hotels', 'doubletree', 'toxic', 'compuserve', 'impasse', 'authorised', 'trashed', 'nudge', 'gop', 'reporter', 'beachcomber', 'highlands', 'debris', 'bearing', 'markings', 'conducting', 'fireball', 'wallets', 'shoes', 'mechanical', 'leila', 'corcoran', 'writes', 'enslaved', 'pleads', 'abraham', 'maimed', 'poses', 'volunteers', 'preserved', 'microfilm', 'stowed', 'rarely', 'enlistment', 'discharge', 'certificates', 'unveiling', 'memorial', 'shall', 'oportunity', 'enjoyment', 'sic', 'slavery', 'oppreses', 'liberty', 'outpouring', 'curse', 'separated', 'matron', 'plead', 'dishonourable', 'prisoner', 'thoughtless', 'folly', 'notation', 'regiment', 'pardon', 'anguish', 'amputee', 'terrible', 'haunted', 'posed', 'chested', 'insight', 'rhythms', 'carbondale', 'ulysses', 'writings', 'poetic', 'linguistic', 'treasure', 'trove', 'sociology', 'seared', 'consciousness', 'confederacy', 'retirees', 'budge', 'weidman', 'shepherded', 'predicts', 'databases', 'battlefields', 'discharged', 'incapacity', 'inebetude', 'arise', 'masturbation', 'vow', 'sylvie', 'ajaccio', 'mediterranean', 'defused', 'debre', 'corse', 'lax', 'powerless', 'beauvau', 'nightly', 'figaro', 'shaky', 'racked', 'principally', 'backlash', 'om', 'gruppen', 'omlx', 'pulpex', 'pulp', 'timber', 'recycled', 'complements', 'softs', 'cocoa', 'unpredictable', 'risky', 'hedge', 'tankage', 'naphtha', 'kero', 'throughput', 'bunkering', 'blenkinsop', 'pantomime', 'enacting', 'brandished', 'theatre', 'prop', 'gorleben', 'depot', 'catania', 'sicily', 'slaying', 'nephew', 'puglisi', 'salvatore', 'botta', 'cemetery', 'sicilian', 'hardened', 'tip', 'consciences', 'nitto', 'santapaola', 'knelt', 'ambush', 'cemetary', 'canteen', 'limerick', 'warders', 'takings', 'italians', 'hiv', 'pensioner', 'harassing', 'hookers', 'aids', 'pietro', 'cruising', 'hurling', 'blanks', 'sighted', 'arctic', 'spitzbergen', 'rune', 'ntb', 'vnukovo', 'tupolev', 'longyearbyen', 'airstrip', 'rescuers', 'opera', 'mountainside', 'barentsburg', 'galeforce', 'meteorological', 'cellars', 'trees', 'uprooted', 'roofs', 'trains', 'cm', 'communes', 'rmt', 'turnhout', 'scouts', 'camping', 'meadow', 'hindered', 'excavations', 'jumet', 'lightly', 'tides', 'pesetas', 'chemicals', 'madness', 'overreacted', 'liberte', 'leveilly', 'finns', 'captive', 'tampere', 'luxury', 'ilkka', 'laasonen', 'iltalehti', 'audi', 'ingolstadt', 'demel', 'hedged', 'kekkila', 'fertilisers', 'saplings', 'trend', 'fluctuations', 'untroubled', 'bumble', 'predict', 'echoing', 'automotive', 'equally', 'predicting', 'rationalised', 'destocked', 'destocking', 'relaxed', 'cycle', 'fraction', 'commenting', 'piston', 'kolbenschmidt', 'obstacles', 'huddart', 'jolts', 'zealands', 'waiau', 'hanmer', 'prone', 'earthquakes', 'smooth', 'trevelyan', 'yearly', 'absolutely', 'predicated', 'precepts', 'trough', 'draconian', 'rentals', 'reclamation', 'narrowly', 'downtrend', 'hover', 'touching', 'intraday', 'technically', 'doreen', 'siow', 'siam', 'banker', 'bishi', 'int', 'jcr', 'jbri', 'daewoo', 'dacom', 'commem', 'manufactures', 'ski', 'antara', 'evident', 'happening', 'motivated', 'utomo', 'indonesias', 'extinguished', 'injures', 'exaggerate', 'rents', 'haneda', 'itami', 'contesting', 'stymies', 'dismantle', 'considerable', 'directorate', 'bureaucrats', 'mandarin', 'secretaries', 'src', 'distillation', 'stuck', 'varied', 'exchanger', 'petrolem', 'chevron', 'texaco', 'loxley', 'reviewed', 'jobless', 'inem', 'cur', 'prod', 'adj', 'trln', 'fcast', 'def', 'qtr', 'epa', 'annualised', 'ine', 'variation', 'corresponds', 'relate', 'finances', 'obstructing', 'lasa', 'zabala', 'gal', 'prescriptions', 'gomez', 'liano', 'aznar', 'bch', 'hive', 'coopers', 'lybrand', 'emigrates', 'lenzing', 'viscose', 'fibre', 'preview', 'schillings', 'fibres', 'economies', 'fundamentalists', 'masterminds', 'ideologists', 'financers', 'clarifications', 'clarification', 'amending', 'donna', 'topic', 'pundits', 'debated', 'forgo', 'unflagging', 'sidestepped', 'bentsen', 'viewed', 'distinctively', 'widness', 'timely', 'instances', 'stands', 'expertise', 'faust', 'bailard', 'biehl', 'kaiser', 'awfully', 'greenspan', 'reappointment', 'perelstein', 'mainstay', 'amstetten', 'ewald', 'brenner', 'stieglmair', 'brunmayr', 'marino', 'hartson', 'samokhalova', 'spirited', 'quigley', 'numerical', 'superiority', 'goulnara', 'fatkoullina', 'snatching', 'lapped', 'ingrid', 'haringa', 'lancien', 'tatiana', 'stiajkina', 'arndt', 'vikstedt', 'boyden', 'godras', 'kombouare', 'abderdeen', 'aitken', 'affection', 'ginola', 'influential', 'influenced', 'imola', 'doohan', 'bayle', 'norifumi', 'abe', 'luca', 'cadalora', 'criville', 'tadayuki', 'okada', 'checa', 'shinichi', 'itoh', 'swe', 'bradley', 'hughes']\n"
          ]
        }
      ],
      "source": [
        "print(itos)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "fa981cf6",
      "metadata": {
        "id": "fa981cf6"
      },
      "outputs": [],
      "source": [
        "v.set_default_index(v[\"<unk>\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ed6fe85",
      "metadata": {
        "id": "4ed6fe85"
      },
      "source": [
        "### Preprocess data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reformat_ner_dict = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}"
      ],
      "metadata": {
        "id": "q3jPiHDvdkEJ"
      },
      "id": "q3jPiHDvdkEJ",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "149c6b1f",
      "metadata": {
        "id": "149c6b1f"
      },
      "outputs": [],
      "source": [
        "def fit_data_Y(column):\n",
        "    dt = [\n",
        "        [reformat_ner_dict[token] for token in row.split()] for row in column\n",
        "    ]\n",
        "    return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]\n",
        "\n",
        "def fit_data_X(dt):\n",
        "    return [\n",
        "        torch.tensor(\n",
        "            [v[\"<bos>\"]] + [v[token] for token in document.split()] + [v[\"<eos>\"]],\n",
        "            dtype=torch.long,\n",
        "        )\n",
        "        for document in dt\n",
        "    ]\n",
        "\n",
        "\n",
        "train_X = fit_data_X(train_data['text'])\n",
        "train_Y = fit_data_Y(train_data['label'])\n",
        "dev_X = fit_data_X(dev_texts_data['text'])\n",
        "dev_Y = fit_data_Y(dev_labels_data['label'])\n",
        "test_X = fit_data_X(test_texts_data['text'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1fa44315",
      "metadata": {
        "id": "1fa44315"
      },
      "source": [
        "### Create model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_tags = len(reformat_ner_dict)"
      ],
      "metadata": {
        "id": "jAUWEqOse936"
      },
      "id": "jAUWEqOse936",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "1eeecf36",
      "metadata": {
        "id": "1eeecf36"
      },
      "outputs": [],
      "source": [
        "class LSTM(torch.nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(LSTM, self).__init__()\n",
        "        self.emb = torch.nn.Embedding(len(v.get_itos()), 100)\n",
        "        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)\n",
        "        self.fc1 = torch.nn.Linear(256, num_tags)\n",
        "\n",
        "    def forward(self, x):\n",
        "        emb = torch.relu(self.emb(x))\n",
        "        lstm_output, (h_n, c_n) = self.rec(emb)\n",
        "        out_weights = self.fc1(lstm_output)\n",
        "        return out_weights"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lstm = LSTM()\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(lstm.parameters())"
      ],
      "metadata": {
        "id": "CIRuZkFjfaBc"
      },
      "id": "CIRuZkFjfaBc",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation model"
      ],
      "metadata": {
        "id": "wJ4trgNPfcPF"
      },
      "id": "wJ4trgNPfcPF"
    },
    {
      "cell_type": "code",
      "source": [
        "def get_accuracy(y_true, y_pred):\n",
        "    hit = 0\n",
        "    missed = 0\n",
        "\n",
        "    for p, t in zip(y_pred, y_true):\n",
        "        if p == t:\n",
        "            hit += 1\n",
        "        else:\n",
        "            missed += 1\n",
        "\n",
        "    accuracy = hit / (hit + missed)\n",
        "    return accuracy"
      ],
      "metadata": {
        "id": "8zkCdkWTf472"
      },
      "id": "8zkCdkWTf472",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_model(dataset_tokens, dataset_labels, model):\n",
        "    Y_true = []\n",
        "    Y_pred = []\n",
        "    for i in tqdm(range(len(dataset_labels))):\n",
        "        batch_tokens = dataset_tokens[i].unsqueeze(0)\n",
        "        tags = list(dataset_labels[i].numpy())\n",
        "        Y_true += tags\n",
        "\n",
        "        Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
        "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
        "        Y_pred += list(Y_batch_pred.numpy())\n",
        "\n",
        "    return get_accuracy(Y_true, Y_pred)"
      ],
      "metadata": {
        "id": "ljxy_LC-feYD"
      },
      "id": "ljxy_LC-feYD",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "ec76b0f6",
      "metadata": {
        "id": "ec76b0f6"
      },
      "source": [
        "### Train model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "e72a055c",
      "metadata": {
        "colab": {
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            "dev_accuracy: 0.8298116716194782\n"
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      ],
      "source": [
        "NUM_EPOCHS = 100\n",
        "dev_accuracy_history = list()\n",
        "for i in range(NUM_EPOCHS):\n",
        "    lstm.train()\n",
        "    for i in tqdm(range(len(train_Y))):\n",
        "        batch_tokens = train_X[i].unsqueeze(0)\n",
        "        tags = train_Y[i].unsqueeze(1)\n",
        "\n",
        "        predicted_tags = lstm(batch_tokens)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    lstm.eval()\n",
        "    dev_accuracy = eval_model(dev_X, dev_Y, lstm)\n",
        "    dev_accuracy_history.append(dev_accuracy)\n",
        "    print(\"dev_accuracy:\", dev_accuracy)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "561f4db3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "561f4db3",
        "outputId": "fa67db09-c62a-4d2a-8a53-11c67f268433"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(dev_accuracy_history, label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Model Accuracy Over Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4d0b9315",
      "metadata": {
        "id": "4d0b9315"
      },
      "source": [
        "### Predict and save results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reversed_ner_dict = {v: k for k, v in reformat_ner_dict.items()}\n",
        "print(reversed_ner_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9rzUXtEpEbd",
        "outputId": "9ac23ad6-372d-4c10-dd6d-0ae4d26fef79"
      },
      "id": "T9rzUXtEpEbd",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'O', 1: 'B-PER', 2: 'I-PER', 3: 'B-ORG', 4: 'I-ORG', 5: 'B-LOC', 6: 'I-LOC', 7: 'B-MISC', 8: 'I-MISC'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "54f93a9b",
      "metadata": {
        "id": "54f93a9b"
      },
      "outputs": [],
      "source": [
        "def predict_and_save(X, filename):\n",
        "    Y_predicted = []\n",
        "    for i in tqdm(range(len(X))):\n",
        "        batch_tokens = X[i].unsqueeze(0)\n",
        "        Y_batch_pred_weights = lstm(batch_tokens).squeeze(0)\n",
        "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
        "        Y_processed_pred = \" \".join(reversed_ner_dict[item] for item in Y_batch_pred.numpy()[1:-1])\n",
        "        Y_predicted.append(Y_processed_pred)\n",
        "    Y_predicted_df = pd.DataFrame(Y_predicted, columns=['predicted_label'])\n",
        "    Y_predicted_df.to_csv(filename, sep='\\t', index=False, header=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "9d3b3867",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "566ec5e3d1cf4656bc0734626430d2f1",
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            "cb5160bcf41d489583c374b60936382a",
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            "d921b46d2f694c36b35dae2d228c4b20",
            "88209c87b9594ec1830dcd0254c57dc6"
          ]
        },
        "id": "9d3b3867",
        "outputId": "82c1983f-16a6-4289-e83e-64a014de8e16"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/215 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "566ec5e3d1cf4656bc0734626430d2f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/230 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dev_predicted = predict_and_save(dev_X, dev_predicted_path)\n",
        "test_predicted = predict_and_save(test_X, test_predicted_path)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
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      "file_extension": ".py",
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