Malayalam_OffensiveLanguageDetection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Mal-OffensiveLanguageDetection.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ask-1710/FIRE2021-OffensiveLanguageDetection/blob/main/Malayalam_OffensiveLanguageDetection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGpqtAgvfn93"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.metrics import f1_score\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDWcscQlsJ8N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16230558-35eb-4ee2-f5bc-3e626c6c13d2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oy3ChnBPi3vf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22cfff9d-4cbe-414d-b51f-975a2d2adc9c"
      },
      "source": [
        "tokenizer_train  = Tokenizer(num_words = 100, oov_token=\"<OOV>\")\n",
        "data_train = pd.read_excel('/content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/Malayalam_offensive_data_Training-YT.xlsx', names=[\"Name\",\"Text\",\"result\"], index_col=False)\n",
        "corpus_train=[]\n",
        "data_train['Text'] = data_train['Text'].astype('str')\n",
        "data_train['Text'] = data_train['Text'].apply(lambda x: x.lower())\n",
        "for i in data_train['Text'].tolist():\n",
        "  if(str(i)!='nan'):\n",
        "    corpus_train.append(i)\n",
        "\n",
        "label_train=[]\n",
        "\n",
        "for i in data_train['result'].tolist():\n",
        "  if(str(i)!='nan'):\n",
        "    label_train.append(i)\n",
        "\n",
        "\n",
        "tokenizer_train .fit_on_texts(corpus_train)\n",
        "total_words_train  = len(tokenizer_train .word_index) + 1\n",
        "\n",
        "print('Mapping of Words and numbers: ')\n",
        "print(tokenizer_train .word_index)\n",
        "print('Number of words for training: ')\n",
        "print(total_words_train )\n",
        "\n",
        "\n",
        "tokenizer = tokenizer_train\n",
        "import pandas as pd\n",
        "data = pd.read_csv('/content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/malayalam_hasoc_tanglish_test_withlabels.tsv',sep='\\t',header=0, names=[\"Name\",\"Text\",\"result\"], index_col=False)\n",
        "corpus=[]\n",
        "data['Text'] = data['Text'].astype('str')\n",
        "data['Text'] = data['Text'].apply(lambda x:x.lower())\n",
        "for i in data['Text'].tolist():\n",
        "  if(str(i)!='nan'):\n",
        "    corpus.append(i)\n",
        "\n",
        "label=[]\n",
        "for i in data['result'].tolist():\n",
        "  if(str(i)!='nan'):\n",
        "    label.append(i)\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mapping of Words and numbers: \n",
            "{'<OOV>': 1, 'oru': 2, 'user': 3, 'url': 4, 'ee': 5, 'ninte': 6, 'nee': 7, 'ennu': 8, 'aanu': 9, 'thanne': 10, 'aa': 11, 'onnum': 12, 'anu': 13, 'njan': 14, 'okke': 15, 'alla': 16, 'nalla': 17, 'e': 18, 'illa': 19, 'athu': 20, 'ulla': 21, 'poyi': 22, 'ath': 23, 'pole': 24, 'onnu': 25, 'ith': 26, 'vere': 27, 'alle': 28, 'enna': 29, 'ne': 30, 'avar': 31, 'ithu': 32, 'poda': 33, 'und': 34, 'bro': 35, 'ente': 36, 'ellam': 37, 'video': 38, 'ingane': 39, 'ella': 40, 'mone': 41, 'parayan': 42, 'ningal': 43, 'but': 44, 'mole': 45, 'kure': 46, 'a': 47, 'paranju': 48, 'sir': 49, 'kond': 50, 'karyam': 51, 'enthu': 52, 'aan': 53, 'ano': 54, 'entha': 55, 'ni': 56, 'eee': 57, 'support': 58, 'muslim': 59, 'para': 60, 'podi': 61, 'ippo': 62, 'appo': 63, 'myre': 64, 'il': 65, 'cinema': 66, 'pani': 67, 'polum': 68, 'um': 69, 'enth': 70, 'enn': 71, 'koode': 72, 'mathram': 73, 'aano': 74, 'vendi': 75, 'ninne': 76, 'sathyam': 77, 'engane': 78, 'kondu': 79, 'swantham': 80, 'comment': 81, 'angane': 82, 'ayi': 83, 'mathi': 84, 'njn': 85, 'poi': 86, 'k': 87, 'enthina': 88, 'thayoli': 89, 'chechi': 90, 'patti': 91, 'nte': 92, 'matham': 93, 'kashtam': 94, 'varum': 95, 'koodi': 96, 'venam': 97, 'kandu': 98, 'ninak': 99, 'eda': 100, 'vannu': 101, 'pakshe': 102, 'ival': 103, 'ille': 104, 'valla': 105, 'case': 106, 'theri': 107, 'onn': 108, 'oke': 109, 'aval': 110, 'malayalam': 111, 'ini': 112, 'ivale': 113, 'aayi': 114, 'undo': 115, 'news': 116, 'kore': 117, 'annu': 118, 'padam': 119, 'enik': 120, 'vare': 121, 'channel': 122, 'ethra': 123, 'ethu': 124, 'avan': 125, 'athe': 126, 'aaa': 127, 'athinu': 128, 'le': 129, 'sthree': 130, 'nair': 131, 'like': 132, 'venda': 133, 'poori': 134, 'aarum': 135, 'full': 136, 'undu': 137, 'nammal': 138, 'ninakku': 139, 'paranja': 140, 'and': 141, 'ariyam': 142, 'interview': 143, 'karanam': 144, 'amma': 145, 'avalude': 146, 'party': 147, 'valiya': 148, 'verum': 149, 'peru': 150, 'ninde': 151, 'kundi': 152, 'enne': 153, 'is': 154, 'cheyyan': 155, 'kooduthal': 156, 'corona': 157, 'paranjath': 158, 'avarude': 159, 'ithra': 160, 'vech': 161, 'ithokke': 162, 'da': 163, 'parayunna': 164, 'avare': 165, 'teacher': 166, 'kollam': 167, 'kandal': 168, 'm': 169, 'avante': 170, 'nammude': 171, 'english': 172, 'film': 173, 'valare': 174, 'adichu': 175, 'veruthe': 176, 'oro': 177, 'nu': 178, 'parayunnath': 179, 'vedi': 180, 'veetil': 181, 'cheyyum': 182, 'kurach': 183, 'eth': 184, 'illatha': 185, 'ullu': 186, '2': 187, 'ellarum': 188, 's': 189, 'veena': 190, 'paranj': 191, 'the': 192, 'ithil': 193, 'de': 194, 'kanda': 195, 'kuttam': 196, 'kanan': 197, 'randu': 198, 'eni': 199, 'nannayi': 200, 'athil': 201, 'mohanlal': 202, 'than': 203, 'i': 204, 'super': 205, 'cheyth': 206, 'family': 207, 'ningalude': 208, 'mon': 209, 'r': 210, 'avale': 211, 'ðÿ˜‚': 212, 'pavam': 213, 'nayinte': 214, 'undallo': 215, 'ivide': 216, 'arum': 217, 'athum': 218, 'chettan': 219, 'kumar': 220, 'maire': 221, 'kalla': 222, 'keralathil': 223, 'valya': 224, 'ane': 225, 'njangal': 226, 'evide': 227, 'cheythu': 228, 'kuru': 229, 'parayum': 230, 'body': 231, 'pala': 232, 'ivan': 233, 'avastha': 234, 'kunna': 235, 'dislike': 236, 'paisa': 237, 'kand': 238, 'pulle': 239, 'keralam': 240, 'nanam': 241, 'of': 242, 'achan': 243, 'panni': 244, 'jeevitham': 245, 'ivar': 246, 'ennum': 247, 'purath': 248, 'police': 249, 'charcha': 250, 'illathe': 251, 'andi': 252, 'vijayan': 253, 'vivaram': 254, 'adyam': 255, 'thanna': 256, 'allathe': 257, 'paavam': 258, 'joli': 259, 'podo': 260, 'parayunnu': 261, 'eduth': 262, 'kaaryam': 263, 'ninnu': 264, 'cheytha': 265, 'veendum': 266, 'bjp': 267, 'appol': 268, 'vann': 269, 'ettavum': 270, 'respect': 271, 'panna': 272, 'parayaan': 273, 'parayunne': 274, 'yum': 275, 'chila': 276, 'adich': 277, 'ellavarum': 278, 'keralathile': 279, 'veettil': 280, 'ninnum': 281, 'islam': 282, 'avide': 283, 'p': 284, 'avalde': 285, 'dress': 286, 'enikk': 287, 'sangi': 288, 'rss': 289, 'kali': 290, 'mla': 291, 'anennu': 292, 'pokum': 293, 'poya': 294, 'motham': 295, 'myran': 296, 'ipo': 297, 'vaa': 298, '10': 299, 'atho': 300, 'best': 301, 'enikku': 302, 'report': 303, 'samayam': 304, 'njanum': 305, 'hindu': 306, '100': 307, 'correct': 308, 'paranjal': 309, 'athine': 310, 'troll': 311, 'vechu': 312, 'ivalude': 313, 'ithilum': 314, 'kurachu': 315, 'chetta': 316, 'adutha': 317, 'modi': 318, 'aanennu': 319, '5': 320, 'big': 321, 'paint': 322, 'thantha': 323, 'nth': 324, 'thalla': 325, 'ennit': 326, 'thonnunnu': 327, 'samskaram': 328, 'ivde': 329, 'ningalk': 330, 'ivare': 331, 'ore': 332, 'vanna': 333, 'edi': 334, 'nokki': 335, 'movie': 336, 'ind': 337, 'avarde': 338, 'paranjathu': 339, 'vittu': 340, 'entho': 341, 'enthoru': 342, 'comedy': 343, 'u': 344, 'atha': 345, 'famous': 346, 'kai': 347, 'aara': 348, 'parayunnathu': 349, 'ninta': 350, 'evale': 351, 'ithupole': 352, 'nari': 353, 'c': 354, 'bhai': 355, 'ollu': 356, 'daivam': 357, 'communist': 358, 'yude': 359, 'eduthu': 360, 'mosham': 361, 'public': 362, 'fathima': 363, 'po': 364, 'apo': 365, 'reply': 366, 'pore': 367, 'hindi': 368, 'ok': 369, 'ayal': 370, 'ningale': 371, 'ennam': 372, '1': 373, 'time': 374, 'prashnam': 375, 'katta': 376, 'ammayum': 377, 'aane': 378, 'eval': 379, 'ariyilla': 380, 'ullavar': 381, 'mr': 382, 'youtube': 383, 'so': 384, 'avane': 385, 'aalukal': 386, 'ivarude': 387, 'athra': 388, 'ha': 389, 'karyangal': 390, 'muslims': 391, 'media': 392, 'patty': 393, 'ithrem': 394, 'jasla': 395, 'abu': 396, 'maathram': 397, 'vargeeyatha': 398, 'vrithiketta': 399, 'tayoli': 400, 'ye': 401, 'avanu': 402, 'anallo': 403, 'nigal': 404, 'oole': 405, 'ivane': 406, 'ah': 407, 'kaanan': 408, 'ðÿ˜‚ðÿ˜‚': 409, 'cheyan': 410, 'ann': 411, 'kutti': 412, 'name': 413, 'thallu': 414, 'ithanu': 415, 'dialogue': 416, 'ponnu': 417, 'yil': 418, 'ennittu': 419, 'joseph': 420, 'vilichu': 421, 'isis': 422, 'kerala': 423, 'pattu': 424, 'pennu': 425, 'matram': 426, 'thala': 427, 'yes': 428, 'dileep': 429, 'you': 430, 'chumma': 431, 'marupadi': 432, 'unnikrishnan': 433, 'cpim': 434, 'thuni': 435, 'nintta': 436, 'theetam': 437, 'team': 438, 'parayam': 439, 'peril': 440, 'ale': 441, 'allenkil': 442, 'polulla': 443, 'n': 444, 'cheyyunnath': 445, 'kittum': 446, 'ittu': 447, 'pattilla': 448, 'per': 449, 'makkale': 450, 'nine': 451, 'thoori': 452, 'chanaka': 453, 'puthiya': 454, 'keri': 455, 'ketta': 456, 'allallo': 457, 'videos': 458, 'epo': 459, 'ninaku': 460, 'aakum': 461, 'aaya': 462, 'pokki': 463, 'cheythath': 464, 'no': 465, 'vicharam': 466, 'vila': 467, 'ennalum': 468, 'kalyanam': 469, 'endu': 470, 'nallath': 471, 'oola': 472, 'adheham': 473, 'ithrayum': 474, 'satyam': 475, 'thrissur': 476, 'manushyan': 477, 'kochu': 478, 'sudapurl': 479, 'makkal': 480, 'neeyokke': 481, 'kurich': 482, 'nallathu': 483, 'polayadi': 484, 'neeraj': 485, 'love': 486, 'glass': 487, 'sheri': 488, 'vote': 489, 'parayanam': 490, 'evan': 491, 'art': 492, 'painting': 493, 'sfi': 494, 'kmcc': 495, 'thall': 496, 'ithoke': 497, 'iniyum': 498, 'number': 499, 'sex': 500, 'pinne': 501, 'kidannu': 502, 'parvathy': 503, 'perum': 504, 'ninakk': 505, 'ishtam': 506, 'thante': 507, 'mathre': 508, 'tharam': 509, 'photo': 510, 'nice': 511, 'pulli': 512, 'enu': 513, 'tanne': 514, 'parayumbol': 515, 'swayam': 516, 'thonnum': 517, 'nale': 518, 'comments': 519, 'namuk': 520, 'level': 521, 'enda': 522, 'moori': 523, 'matha': 524, 'lokam': 525, 'thonunnu': 526, 'athanu': 527, 'orikkalum': 528, 'follow': 529, 'life': 530, 'nirthi': 531, 'orikalum': 532, 'l': 533, 'eeee': 534, 'akum': 535, 'achanum': 536, 'neee': 537, 'kettu': 538, 'naari': 539, 'ðÿ˜\\x81': 540, 'kitty': 541, 'ennanu': 542, 'live': 543, 'vazhi': 544, 'mathavum': 545, 'polichu': 546, 'varan': 547, 'man': 548, 'madam': 549, 'ippol': 550, 'ðÿ˜‚ðÿ˜‚ðÿ˜‚': 551, 'paranari': 552, 'sasi': 553, 'cheyyanam': 554, 'sandeep': 555, 'scene': 556, 'adi': 557, 'mani': 558, 'munnil': 559, 'thankal': 560, 'chance': 561, 'onum': 562, 'madham': 563, 'problem': 564, 'pottan': 565, 'veruthey': 566, 'ennal': 567, 'teams': 568, 'vaka': 569, 'nadan': 570, 'penn': 571, 'edo': 572, '3': 573, 'lal': 574, 'thanneyanu': 575, 'karyathil': 576, 'veno': 577, 'cheyyunna': 578, 'otta': 579, 'avasanam': 580, 'aayirikkum': 581, 'orma': 582, 'anne': 583, 'sontham': 584, 'to': 585, 'mandan': 586, 'thammil': 587, 'nokku': 588, 'lu': 589, 'food': 590, 'aaaa': 591, 'subscribe': 592, 'cinemayil': 593, 'oral': 594, 'plz': 595, 'pacha': 596, 'potti': 597, 'my': 598, 'areyum': 599, 'lokath': 600, 'koppe': 601, 'makale': 602, 'alukal': 603, 'mol': 604, 'idan': 605, 'aaanu': 606, 'first': 607, 'indian': 608, 'visham': 609, 'maha': 610, 'metty': 611, 'enikum': 612, 'avde': 613, 'india': 614, 'athinte': 615, 'kodukkan': 616, 'angu': 617, 'ithinte': 618, 'muhammed': 619, 'kurichu': 620, 'shesham': 621, 'main': 622, 'eppo': 623, 'kitti': 624, 'kalam': 625, 'evar': 626, 'santhosh': 627, 'ldf': 628, 'randum': 629, 'jai': 630, 'sneham': 631, 'cpm': 632, 'enthayalum': 633, 'sunny': 634, 'edutha': 635, 'ganesh': 636, 'in': 637, 'ithinu': 638, 'thooki': 639, 'thalli': 640, 'show': 641, 'thendi': 642, 'boss': 643, 'thanik': 644, 'aah': 645, 'paranje': 646, 'kallan': 647, 'fraud': 648, 'kanunna': 649, 'sathyathil': 650, 'feminist': 651, 'kollanam': 652, 'id': 653, 'vedio': 654, 'vp': 655, 'jaleel': 656, 'shibu': 657, 'pennungal': 658, 'kazhinju': 659, 'etho': 660, 'ariyatha': 661, 'kondalle': 662, 'thallede': 663, 'ante': 664, 'ntha': 665, 'anna': 666, 'thangal': 667, 'idu': 668, 'baki': 669, 'mudi': 670, 'kaanum': 671, 'kanditt': 672, 'vannal': 673, 'neram': 674, 'cash': 675, 'samshayam': 676, 'fans': 677, 'poo': 678, 'kandilla': 679, 'ammede': 680, 'kandaal': 681, 'nite': 682, 'aadyam': 683, 'vari': 684, 'ninakkokke': 685, 'mari': 686, 'allalo': 687, 'vayya': 688, 'chiri': 689, 'parayathe': 690, 'malayala': 691, 'be': 692, 'are': 693, 'all': 694, 'montha': 695, 'sheriya': 696, 'award': 697, 'negative': 698, 'onu': 699, 'nn': 700, 'short': 701, 'kanikan': 702, 'egane': 703, 'vartha': 704, 'kettal': 705, 'vera': 706, 'culture': 707, 'kittan': 708, 'onne': 709, 'or': 710, 'elle': 711, 'enthanu': 712, 'akan': 713, 'illalo': 714, 'bakki': 715, 'undakki': 716, 'janangale': 717, 'thomas': 718, 'that': 719, 'moly': 720, 'ullath': 721, 'aanallo': 722, 'god': 723, 'anno': 724, 'mulayum': 725, '6': 726, 'look': 727, 'aaru': 728, 'ariyaam': 729, 'china': 730, 'thonni': 731, 'malayalikal': 732, 'as': 733, 'purathu': 734, 'udf': 735, 'election': 736, 'parayu': 737, 'last': 738, 'nakki': 739, 'iyal': 740, 'v': 741, 'undenkil': 742, \"it's\": 743, 'koduthu': 744, 'avark': 745, 'shani': 746, 'athinulla': 747, 'nanni': 748, 'thilakan': 749, 'oolakal': 750, 'pakka': 751, 'lobby': 752, 'varunna': 753, 'babu': 754, 'steel': 755, 'poli': 756, 'ennokke': 757, 'samooham': 758, 'mathrame': 759, 'nthina': 760, 'padilla': 761, 'eppol': 762, 'chintha': 763, 'kk': 764, 'quran': 765, 'poor': 766, 'fake': 767, 'character': 768, 'shaji': 769, 'for': 770, 'avalk': 771, 'ammayude': 772, 'publicity': 773, 'paripadi': 774, 'chodyam': 775, 'arya': 776, 'vilich': 777, 'neeyum': 778, 'angne': 779, 'kodukk': 780, 'thallaye': 781, 'aadhyam': 782, 'myra': 783, 'vilikkunna': 784, 'mare': 785, 'thane': 786, 'rate': 787, 'cheyanam': 788, 'kodutha': 789, 'oh': 790, 'kayari': 791, 'etc': 792, 'biriyani': 793, 'padicha': 794, 'aarelum': 795, 'oruthan': 796, 'vitt': 797, 'rahul': 798, 'vargeeya': 799, 'madi': 800, 'ayirikkum': 801, 'cheythal': 802, 'thett': 803, 'cheyyu': 804, 'muzhuvanum': 805, 'chorichil': 806, 'setta': 807, 'konnu': 808, 'varunnu': 809, 'varu': 810, 'evante': 811, 'kallam': 812, 'thurannu': 813, 'alukale': 814, 'pennine': 815, 'janangal': 816, '50': 817, 'd': 818, 'ooo': 819, 'adhyam': 820, 'aduth': 821, 'loka': 822, 'koch': 823, 'adipoli': 824, 'oombiya': 825, 'aareyum': 826, 'kutty': 827, 'poye': 828, 'dhanya': 829, 'kalayan': 830, 'ond': 831, 'ayirunnu': 832, 'thanneya': 833, 'daivame': 834, 'mathy': 835, 'ammaye': 836, 'vallatha': 837, 'njangale': 838, 'manasilayi': 839, 'ororo': 840, 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14142, 'payakkam': 14143, 'chennathanelum': 14144, 'yochikkunnath': 14145, 'ithokkeyanu': 14146, 'padikknm': 14147, 'exploit': 14148, 'cheyyunn': 14149, 'veendu': 14150, 'athma': 14151, 'parishodhana': 14152, 'angerude': 14153, 'masterurl': 14154, 'pottttã\\xa0': 14155, 'chothyamanu': 14156, 'chothikkune': 14157, 'abhimugham': 14158, \"'madyamangal\": 14159, \"'mansilakendathe\": 14160, 'swarjinte': 14161, 'shaily': 14162, 'manasilakkanolumponna': 14163, 'madyma': 14164, 'niroopakaronnum': 14165, 'keralathililla': 14166, 'chodhyangalum': 14167, 'pakwamaaya': 14168, 'marupadiyum': 14169, 'vytsthyayum': 14170, 'vannuuu': 14171, 'prasthanam': 14172, 'partiye': 14173, 'baduvan': 14174, 'kandillennu': 14175, 'rashtreeyatheethamayi': 14176, 'nadathiya': 14177, 'samsarashailyil': 14178, 'chodekunnavanane': 14179, 'verayal': 14180, 'saghavina': 14181, 'nadinne': 14182, 'manja': 14183, 'ramayilokkay': 14184, 'yendhina': 14185, 'sagavay': 14186, 'thakkam': 14187, 'ldfnte': 14188, 'asaanam': 14189, 'choriyalanu': 14190, 'kaazhchapad': 14191, 'uthu': 14192, 'kanukkaa': 14193, 'verrutheyalla': 14194, 'muraleedharran': 14195, 'oody': 14196, 'kozha': 14197, 'vangiyathu': 14198, 'parayatte': 14199, 'shornur': 14200, 'suhruthee': 14201, 'evaneippolulla': 14202, 'chettagalanu': 14203, 'naadinei': 14204, 'nashipurlðÿ‘¹ðÿ‘¹ðÿ‘¹': 14205, 'edhonnum': 14206, 'thallill': 14207, 'parayillatta': 14208, 'edhokke': 14209, 'aanutta': 14210, 'theetanghalðÿ’©': 14211, 'bharikunnathu': 14212, 'endum': 14213, 'aakallooo': 14214, 'avanate': 14215, 'partyum': 14216, 'ivda': 14217, 'partykal': 14218, 'udavanam': 14219, 'con': 14220, 'exurl': 14221, 'kilavanmara': 14222, 'cherupakar': 14223, 'udakanam': 14224, 'veeran': 14225, 'aayirunu': 14226, 'partikarum': 14227, 'kanakkan': 14228, 'avaroke': 14229, 'arkko': 14230, 'vendiyan': 14231, 'orronn': 14232, 'kuttunnathðÿ¤¬ðÿ¤¬ðÿ¤¬ðÿ¤¬ðÿ¤¬ðÿ˜\\x8f': 14233, 'radhakrishnan': 14234, 'wowww': 14235, 'combo': 14236, 'aluvem': 14237, 'curryum': 14238, 'ðÿ˜„': 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'oombikkondirikkum': 18505, 'kerunna': 18506, 'sudaapurl': 18507, 'alliyooo': 18508, 'ellathintem': 18509, 'allelm': 18510, 'avdem': 18511, 'thiripp': 18512, 'ondakan': 18513, 'sanghikalkk': 18514, 'prathyeka': 18515, 'kazhv': 18516, 'thoooooo': 18517, 'swabhvam': 18518, 'koottathil': 18519, 'jihad': 18520, 'shanthosham': 18521, 'pakshee': 18522, 'vidunnath': 18523, 'f': 18524, 'apamanm': 18525, 'undakkya': 18526, 'leavanmaray': 18527, 'veruthay': 18528, 'vidarathu': 18529, 'sushicho': 18530, 'chillapole': 18531, 'thale': 18532, 'veshyanet': 18533, 'thettangal': 18534, 'ethelum': 18535, 'vikasana': 18536, 'kanikumo': 18537, 'ayyyo': 18538, 'parayarth': 18539, 'pakshathine': 18540, 'kityale': 18541, 'orazhcha': 18542, 'mugm': 18543, 'kannte': 18544, 'officer': 18545, 'varthaaaanam': 18546, 'kaynjaaal': 18547, 'athaaaan': 18548, 'partiyide': 18549, 'prathikalkk': 18550, 'sambavikkaaar': 18551, 'tayolikalude': 18552, 'adichuuuu': 18553, 'pottichittuuuu': 18554, 'neyokkeee': 18555, 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'niyamathine': 18657, 'mathiii': 18658, 'ðÿ¤¦â€\\x8dâ™€ï¸\\x8fðÿ¤¦â€\\x8dâ™€ï¸\\x8f': 18659, 'vrithikettavan': 18660, 'nallavane': 18661, 'mattulavarde': 18662, 'abhinayikunu': 18663, 'veruthupoi': 18664, 'ã\\xa0a': 18665, 'pannaye': 18666, 'abbas': 18667, 'sahodaree': 18668, 'polullavara': 18669, 'mayakkuvidhyayil': 18670, 'veezhunnath': 18671, 'parayaanilla': 18672, 'tendiku': 18673, 'mottam': 18674, 'daivama': 18675, 'marokke': 18676, 'varunnada': 18677, 'kandaalum': 18678, 'enthaanen': 18679, 'manasilakk': 18680, 'indavillaðÿ˜\\xa0': 18681, 'avantay': 18682, 'okkay': 18683, 'teerkan': 18684, 'yetra': 18685, 'valiyad': 18686, 'aakumðÿ˜¡': 18687, 'parayanm': 18688, 'vakkunnath': 18689, 'mosamakiyit': 18690, 'manyanayi': 18691, 'chechee': 18692, 'apamanikkukayan': 18693, 'follower': 18694, 'muslimsine': 18695, 'boxesil': 18696, 'ethrekk': 18697, 'mandaranmaranonn': 18698, 'peechu': 18699, 'peechuuu': 18700, 'mashura': 18701, 'yodoppam': 18702, 'thenne': 18703, 'legally': 18704, 'cheythittundo': 18705, 'thalararuthu': 18706, 'munnottupova': 18707, 'prarthanakonde': 18708, 'nilikkan': 18709, 'sidhikkooo': 18710, 'njngalku': 18711, 'dhivam': 18712, 'koodeyundavatte': 18713, 'thalayuyarthi': 18714, 'jeevikkk': 18715, 'ðÿ˜\\x8dðÿ˜\\x8dðÿ˜˜ðÿ˜˜': 18716, 'veettukarkkethire': 18717, 'ninakkano': 18718, 'eetho': 18719, 'pattathath': 18720, 'eyutjivachitund': 18721, 'kallananenn': 18722, 'randamathevale': 18723, 'kettiyathenne': 18724, 'kandkondan': 18725, 'sherin': 18726, 'muslimayal': 18727, 'munnu': 18728, 'kettam': 18729, 'polumðÿ˜\\x8fðÿ¤\\xadpakshe': 18730, 'mathasthara': 18731, 'kettanamðÿ˜€ðÿ˜€ðÿ˜ƒðÿ˜ƒ': 18732, 'matoru': 18733, 'orulumait': 18734, 'relationshipnu': 18735, 'padillairnu': 18736, 'pannide': 18737, 'cheythitte': 18738, 'avanm': 18739, 'naarighalm': 18740, 'sprtn': 18741, 'fansum': 18742, 'hostel': 18743, 'neighbours': 18744, 'kelkanam': 18745, 'adiyanu': 18746, 'wifemar': 18747, 'adhyabaryaye': 18748, 'bheeshanipeduthiyanu': 18749, 'randambaryaye': 18750, 'ketiyath': 18751, 'patichilla': 18752, 'kanarillaðÿ˜œðÿ˜œðÿ˜œðÿ˜œ': 18753, 'athea': 18754, 'chilavinu': 18755, 'illatthakondu': 18756, 'kodutthu': 18757, 'uduthorungi': 18758, 'abhipathram': 18759, 'paranjathinu': 18760, 'ennae': 18761, 'supports': 18762, 'pongaala': 18763, 'vidhu': 18764, 'varghese': 18765, 'pootich': 18766, 'actingum': 18767, 'nallaval': 18768, 'edunnavarkkellam': 18769, 'enieoru': 18770, 'kallatharathe': 18771, 'ulkollathirikkuka': 18772, 'porathadhin': 18773, 'negetive': 18774, 'ittavarkedhire': 18775, 'chood': 18776, 'aavukayum': 18777, 'ðÿ˜¤': 18778, 'aloychit': 18779, 'thonind': 18780, 'endaa': 18781, 'udayipteratin': 18782, 'avalanu': 18783, 'kootunikkunne': 18784, 'snehamulla': 18785, 'baryayum': 18786, 'barthavine': 18787, 'panku': 18788, 'vakkan': 18789, 'agrwhikkilla': 18790, 'suhan': 18791, 'fool': 18792, 'aannu': 18793, 'avanodoppam': 18794, 'pokunnundaum': 18795, 'ori': 18796, 'velakkari': 18797, 'suhana': 18798, 'thamasichirunne': 18799, 'know': 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18897, 'amc': 18898, 'rimi': 18899, 'tomynte': 18900, 'vid': 18901, 'nok': 18902, 'chrich': 18903, 'eshvara': 18904, 'engeru': 18905, 'kanarilla': 18906, 'ðÿ˜«': 18907, 'cheythitt': 18908, 'sync': 18909, 'cheythittu': 18910, 'ithaanallo': 18911, 'kelkunna': 18912, 'thalapathy': 18913, 'njanðÿ˜\\x90ðÿ˜\\xad': 18914, 'avarano': 18915, 'karanakkari': 18916, 'cheyatha': 18917, 'tettinte': 18918, 'nikkano': 18919, 'aalojikaan': 18920, 'pattoola': 18921, 'eppayum': 18922, 'vayak': 18923, 'ntem': 18924, 'makkalem': 18925, 'ellaatinum': 18926, 'friendl': 18927, 'nilkaaa': 18928, 'uppak': 18929, 'aayus': 18930, 'neetikodukane': 18931, 'swanthamaayi': 18932, 'chindhikkunnathakum': 18933, 'itite': 18934, 'poyathe': 18935, 'oruthyil': 18936, 'ninnalo': 18937, 'bhagyavanaya': 18938, 'rakshappettu': 18939, 'okkeyan': 18940, 'nadinthe': 18941, 'achanteyum': 18942, 'ammayudem': 18943, 'ariyathondaaaa': 18944, 'kandavarde': 18945, 'ethi': 18946, 'nokkanulla': 18947, 'vyagratha': 18948, 'pattumengil': 18949, 'kalku': 18950, 'upakaramulla': 18951, 'keralthil': 18952, 'sambaav': 18953, 'bahulaaami': 18954, 'oraaal': 18955, 'jeevichirukkunnu': 18956, 'kazinnaaal': 18957, 'aaalukal': 18958, 'trollanmar': 18959, 'kandillannu': 18960, 'andaingilum': 18961, 'daut': 18962, 'undangil': 18963, 'chothikkamalloo': 18964, 'kettunnavan': 18965, 'mattavale': 18966, 'committe': 18967, 'alll': 18968, 'kod': 18969, 'bhayamkaram': 18970, 'thudangumo': 18971, 'theerakuna': 18972, 'prostitutes': 18973, 'theeruvo': 18974, 'ellrum': 18975, 'abinaym': 18976, 'kanikum': 18977, 'henna': 18978, 'pediyum': 18979, 'dairyamayi': 18980, 'contact': 18981, 'cheytholoo': 18982, 'enney': 18983, 'vishwasikkam': 18984, 'ndhonnaa': 18985, 'chindhichillaaa': 18986, 'chodhichu': 18987, 'cheythit': 18988, 'vavachi': 18989, 'ittil': 18990, 'alllakil': 18991, 'million': 18992, 'adikum': 18993, 'ozivaakiyirunnangil': 18994, 'ippozum': 18995, 'jeevikkumaayorunnnoo': 18996, 'stage': 18997, 'ulsavamaakkunna': 18998, 'kalaakaaran': 18999, 'jayane': 19000, 'saahasika': 19001, 'rolukalum': 19002, 'cheyyumayirunnu': 19003, 'thrishur': 19004, 'pokumpo': 19005, 'chalakudi': 19006, 'railwaystation': 19007, 'varumpo': 19008, 'ariyathay': 19009, 'nirayum': 19010, 'yu': 19011, 'manichettaðÿ˜¥ðÿ˜¥ðÿ˜¥': 19012, 'ethepole': 19013, 'marakkar': 19014, 'kazhiyumbol': 19015, '10cr': 19016, 'lalunni': 19017, 'kyu': 19018, 'shamblaminkilum': 19019, 'undello': 19020, 'velayum': 19021, 'kooliyum': 19022, 'onninum': 19023, 'kollilathavan': 19024, 'nadakunilallo': 19025, 'teetam': 19026, 'teeni': 19027, 'stardom': 19028, 'janmathil': 19029, 'manasilavila': 19030, 'venamengilm': 19031, 'ðÿ¤\\xadðÿ˜‚ðÿ˜‚': 19032, 'thalakan': 19033, 'ivanum': 19034, 'deepu': 19035, 'thilakann': 19036, 'nashtavumilllallow': 19037, 'chaithatha': 19038, 'kurea': 19039, 'mukha': 19040, 'azhinjn': 19041, 'veenu': 19042, 'soubinte': 19043, 'industryill': 19044, 'ninumaan': 19045, 'ammamyk': 19046, 'ammamma': 19047, 'achaaachanu': 19048, 'koooopppp': 19049, 'verapurl': 19050, 'mullipoyi': 19051, 'athinaanu': 19052, 'swatham': 19053, 'baviye': 19054, 'pedithonddan': 19055, 'jeevikkedaa': 19056, 'vaanameyy': 19057, 'mashu': 19058, 'spex': 19059, 'vechitulla': 19060, 'hjb': 19061, 'chyyo': 19062, 'ishtaayi': 19063, 'baryood': 19064, 'thuranne': 19065, 'bashi': 19066, 'pasu': 19067, 'chechiyum': 19068, 'ezhunelkunathu': 19069, 'orh': 19070, 'ahana': 19071, 'chachiii': 19072, 'kettirikkan': 19073, 'rasamaaaa': 19074, 'edanamttooooðÿ˜„ðÿ˜„ðÿ˜„ðÿ˜„': 19075, 'ravindran': 19076, 'sammathikkam': 19077, 'orthirikkuvennulathanu': 19078, 'bhayankaram': 19079, 'signs': 19080, 'vayassilulla': 19081, 'karyanga': 19082, 'ormayundakoðÿ˜‚ðÿ˜\\x81ðÿ˜\\x8d': 19083, 'trollanonum': 19084, 'ilato': 19085, 'thankq': 19086, 'ithokkey': 19087, 'cheyunndu': 19088, 'pretheekshikunnuâ™¥ï¸\\x8fâ™¥ï¸\\x8fðÿ™\\x8f': 19089, 'enikenthu': 19090, 'vevalathi': 19091, 'ithiney': 19092, 'kaanunathu': 19093, 'aayitta': 19094, 'kaananey': 19095, 'ethratholam': 19096, 'nanamillaloda': 19097, 'veenidathu': 19098, 'urulan': 19099, 'pennugaley': 19100, 'poley': 19101, 'olichirikunney': 19102, 'student': 19103, 'aavunulla': 19104, 'arhadha': 19105, 'undonnu': 19106, 'nokkattey': 19107, 'ðÿ¤£ðÿ¤£njan': 19108, 'vallyavan': 19109, 'thanneyado': 19110, 'cheriyavaney': 19111, 'athondalley': 19112, 'nokkan': 19113, 'ivdey': 19114, 'aanagil': 19115, 'sirey': 19116, '3500': 19117, 'reveal': 19118, 'porey': 19119, 'nakka': 19120, 'kaasinu': 19121, 'padipikaney': 19122, 'nasippikkan': 19123, 'kacha': 19124, 'irangunavorudu': 19125, 'masheyðÿ™\\x8fðÿ™\\x8f': 19126, 'fann': 19127, 'anuto': 19128, 'samsarikanam': 19129, 'bhagara': 19130, 'ayyoo': 19131, 'poottandu': 19132, 'mentalist': 19133, 'anandhuvinte': 19134, 'thattip': 19135, 'vishvasikan': 19136, 'onule': 19137, 'ðÿ˜\\x8fðÿ˜\\x8f': 19138, 'njanm': 19139, 'chinthicha': 19140, 'eeparachil': 19141, 'vicharikum': 19142, 'enullath': 19143, 'paranjond': 19144, 'irunnal': 19145, 'angneye': 19146, 'thettukarayalum': 19147, 'ethichuthannu': 19148, 'weakness': 19149, 'churiki': 19150, 'ketan': 19151, 'irakkanam': 19152, 'thonunn': 19153, 'ulupp': 19154, 'ðÿ˜\\x9d': 19155, 'kunjilla': 19156, 'inganey': 19157, 'kutavunnathano': 19158, 'ethics': 19159, 'kannikanakum': 19160, 'padipurlthe': 19161, 'illngl': 19162, 'ninglkk': 19163, 'chettanmare': 19164, 'night': 19165, 'ittamathiyayirunnu': 19166, 'veetukarkum': 19167, 'kanamallo': 19168, 'kandamrigam': 19169, 'thottupokum': 19170, 'kashttamanid': 19171, 'parayalo': 19172, 'boar': 19173, 'avashyathn': 19174, 'madiyallo': 19175, 'rahasiyamayicheyyunnad': 19176, 'parasyamaayi': 19177, 'dano': 19178, 'save': 19179, 'date': 19180, 'predharshipurl': 19181, 'ullathalla': 19182, 'ellathinnum': 19183, 'athintethaya': 19184, 'marayokke': 19185, 'allatha': 19186, 'vangunna': 19187, 'photographers': 19188, 'sahoo': 19189, 'kunjungalum': 19190, 'samoohavum': 19191, 'kanunnathale': 19192, 'vaisali': 19193, 'policea': 19194, 'joliyumille': 19195, 'qualifications': 19196, 'atinu': 19197, 'anusarichulla': 19198, 'penkutiye': 19199, 'tarum': 19200, 'jayaraminte': 19201, 'kodukanamnu': 19202, 'theerumanikendat': 19203, 'madhave': 19204, 'kettiyolan': 19205, 'malakayile': 19206, 'asif': 19207, 'sachu': 19208, 'kuttii': 19209, 'teasing': 19210, 'entethum': 19211, 'angnanu': 19212, 'kootiya': 19213, 'ooombiya': 19214, 'swarna': 19215, 'kambanikalum': 19216, 'nadanmarum': 19217, 'nadimaarudem': 19218, 'parasyamgalum': 19219, 'paarambarya': 19220, 'vaadavum': 19221, 'kinattilidenta': 19222, 'samaym': 19223, 'aduthath': 19224, 'manirathnamthinte': 19225, 'malayalatilum': 19226, 'thandayodum': 19227, 'sheelichatha': 19228, 'dickface': 19229, 'thandade': 19230, 'unddada': 19231, 'perukki': 19232, 'madiya': 19233, 'sundaryamind': 19234, 'inganeparayan': 19235, 'chetanano': 19236, 'chechiyanonn': 19237, 'manasum': 19238, 'orumenayila': 19239, 'parasayam': 19240, 'achanmaraya': 19241, 'annugaleyum': 19242, 'cheyunadanu': 19243, 'koduthale': 19244, 'magalodulla': 19245, 'undavullo': 19246, 'kannikunadu': 19247, 'bagathum': 19248, 'thetundh': 19249, 'vazhikke': 19250, 'veraan': 19251, 'paadilaarnu': 19252, 'kettikolaam': 19253, 'ennem': 19254, 'kuttiyem': 19255, 'mathiðÿ˜ž': 19256, 'chakkiye': 19257, 'trollanmaarkku': 19258, 'pokaruthu': 19259, 'kalidasan': 19260}\n",
            "Number of words for training: \n",
            "19261\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yY4xXUJ_i5-X",
        "outputId": "2204172a-899c-4641-d547-d42c6c5e5611"
      },
      "source": [
        "input_sequences = []\n",
        "labels = []\n",
        "for line in range(len(corpus)):\n",
        "  if label[line] == 'OFF':\n",
        "    l = 0\n",
        "  else:\n",
        "    l = 1\n",
        "  token_list = tokenizer.texts_to_sequences([corpus[line]])[0]\n",
        "  input_sequences.append(token_list)\n",
        "  labels.append(l)\n",
        "  \n",
        "# pad sequences\n",
        "input_sequences = np.array(pad_sequences(input_sequences, maxlen=100, padding='pre'))\n",
        "\n",
        "# create predictors and label\n",
        "xs = input_sequences[:,:]\n",
        "ys = tf.keras.utils.to_categorical(labels, num_classes=2)\n",
        "print('Number of validation samples:', end=' ')\n",
        "print(len(ys),len(xs))\n",
        "\n",
        "#TRAIN\n",
        "labels = []\n",
        "input_sequences = []\n",
        "for line in range(len(corpus_train)):\n",
        "  if label_train[line] == 'OFF':\n",
        "    l = 0\n",
        "  else:\n",
        "    l = 1\n",
        "\n",
        "  token_list_train = tokenizer.texts_to_sequences([corpus_train[line]])[0]\n",
        "  input_sequences.append(token_list_train)\n",
        "  labels.append(l)\n",
        "  \n",
        "# pad sequences \n",
        "input_sequences = np.array(pad_sequences(input_sequences, maxlen=100, padding='pre'))\n",
        "xs_train= input_sequences[:,:]\n",
        "ys_train = tf.keras.utils.to_categorical(labels, num_classes=2)\n",
        "print('Number of training samples : ', len(ys_train),len(xs_train))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of validation samples: 950 950\n",
            "Number of training samples :  3999 3999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3T0edvahwvwQ"
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint "
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8i6kp3-8i8Wl"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(total_words_train, 100, input_length=100))\n",
        "model.add(Bidirectional(LSTM(150)))\n",
        "model.add(Dense(2, activation='softmax'))\n",
        "adam = Adam(learning_rate=0.01)\n",
        "model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWgdw6wx20ux"
      },
      "source": [
        "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=2, verbose=0, mode='auto')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MVHWceE21sn"
      },
      "source": [
        "ModelCheckpointcheckpoint = ModelCheckpoint('/content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/best_model_mal.h5',\n",
        "                             monitor='val_loss',\n",
        "                             mode='min',\n",
        "                             save_best_only=True,\n",
        "                             verbose=1)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1LCW7Jxv6wb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "096c2342-91d5-4270-b4c2-dfcc7f395bf8"
      },
      "source": [
        "history = model.fit(xs_train, ys_train, epochs=10, verbose=1,validation_data=(xs, ys), callbacks=[earlystop, ModelCheckpointcheckpoint])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "125/125 [==============================] - 23s 77ms/step - loss: 0.7097 - accuracy: 0.4894 - val_loss: 0.7119 - val_accuracy: 0.4747\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.71187, saving model to /content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/best_model_mal.h5\n",
            "Epoch 2/10\n",
            "125/125 [==============================] - 6s 52ms/step - loss: 0.6995 - accuracy: 0.5016 - val_loss: 0.6910 - val_accuracy: 0.5200\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.71187 to 0.69103, saving model to /content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/best_model_mal.h5\n",
            "Epoch 3/10\n",
            "125/125 [==============================] - 6s 47ms/step - loss: 0.6966 - accuracy: 0.5234 - val_loss: 0.6899 - val_accuracy: 0.5653\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.69103 to 0.68994, saving model to /content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/best_model_mal.h5\n",
            "Epoch 4/10\n",
            "125/125 [==============================] - 6s 47ms/step - loss: 0.6859 - accuracy: 0.5531 - val_loss: 0.6787 - val_accuracy: 0.5611\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.68994 to 0.67867, saving model to /content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/best_model_mal.h5\n",
            "Epoch 5/10\n",
            "125/125 [==============================] - 6s 47ms/step - loss: 0.6783 - accuracy: 0.5671 - val_loss: 0.6647 - val_accuracy: 0.6063\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.67867 to 0.66470, saving model to /content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/best_model_mal.h5\n",
            "Epoch 6/10\n",
            "125/125 [==============================] - 6s 47ms/step - loss: 0.6738 - accuracy: 0.5821 - val_loss: 0.6638 - val_accuracy: 0.5821\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.66470 to 0.66384, saving model to /content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/train/best_model_mal.h5\n",
            "Epoch 7/10\n",
            "125/125 [==============================] - 6s 47ms/step - loss: 0.6725 - accuracy: 0.5844 - val_loss: 0.6841 - val_accuracy: 0.5842\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.66384\n",
            "Epoch 8/10\n",
            "125/125 [==============================] - 6s 47ms/step - loss: 0.6578 - accuracy: 0.5939 - val_loss: 0.6856 - val_accuracy: 0.5684\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.66384\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puHFmirMJVtb",
        "outputId": "15509c4a-082c-4177-a494-c427c4bc3943"
      },
      "source": [
        "# Compute f1-score \n",
        "y_pred = model.predict(xs).argmax(axis=-1)\n",
        "y_true = ys.argmax(axis=-1)\n",
        "\n",
        "print(f1_score(y_true, y_pred))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.662828947368421\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVu6NJ_EYKfl",
        "outputId": "95cf4cf6-3fa8-4404-d93e-b7331f584f6a"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 100, 100)          1926100   \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 300)               301200    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2)                 602       \n",
            "=================================================================\n",
            "Total params: 2,227,902\n",
            "Trainable params: 2,227,902\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpD8G4Ev49UT"
      },
      "source": [
        "import re"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBkxvf2M4i9R"
      },
      "source": [
        "categories = ['Off','Not']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnC9WX51wdVd"
      },
      "source": [
        "test_df = pd.read_csv('/content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/test/mal-offensive-withoutlabels - Sheet1.csv', names=[\"id\",\"text\",\"label\"], index_col=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvwdFQac35d3"
      },
      "source": [
        "test_df['text'] = test_df['text'].apply(lambda x: x.lower())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XCPpThM46EQ"
      },
      "source": [
        "Demotify"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBEyxrpXbiLf",
        "outputId": "95b34e77-fba5-449d-b181-779468bbe380"
      },
      "source": [
        "!pip install emoji\n",
        "import emoji"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting emoji\n",
            "  Downloading emoji-1.5.0.tar.gz (185 kB)\n",
            "\u001b[?25l\r\u001b[K     |█▊                              | 10 kB 26.7 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 20 kB 22.7 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 30 kB 15.9 MB/s eta 0:00:01\r\u001b[K     |███████                         | 40 kB 12.8 MB/s eta 0:00:01\r\u001b[K     |████████▉                       | 51 kB 8.1 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 61 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 71 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 81 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 92 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 102 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 112 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 122 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 133 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 143 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 153 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 163 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 174 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 184 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 185 kB 6.8 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-1.5.0-py3-none-any.whl size=187457 sha256=83366552fd92a68df23bba1c9083b94b535eddfa81858d37b83944e3d01a35fb\n",
            "  Stored in directory: /root/.cache/pip/wheels/db/b5/f6/b39abf14e94b3d6640613bbe630a66c10ccf7a12882d064fb5\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-1.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJXwlzfJ45vK"
      },
      "source": [
        "def deEmojify(text):\n",
        "    regrex_pattern = re.compile(pattern = \"[\"\n",
        "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
        "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
        "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
        "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
        "                           \"]+\", flags = re.UNICODE)\n",
        "    return regrex_pattern.sub(r'',text)\n",
        "\n",
        "test_df['text'] = test_df['text'].apply(lambda x:deEmojify(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_epVmFLt6oPD"
      },
      "source": [
        "# !pip install indic-nlp-library\n",
        "# from indicnlp.transliterate.unicode_transliterate import UnicodeIndicTransliterator\n",
        "# from indicnlp.transliterate.unicode_transliterate import ItransTransliterator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACK5F5ux-iQS",
        "outputId": "70492fb4-1fb9-4060-e6f6-e7d2cb9afb65"
      },
      "source": [
        "!pip install langdetect"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langdetect\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
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            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from langdetect) (1.15.0)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993242 sha256=03c30b814b333f89b35e07554d6dcc620d0cb93a349b9f9b27f95b79d88a838b\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/96/8a/f90c59ed25d75e50a8c10a1b1c2d4c402e4dacfa87f3aff36a\n",
            "Successfully built langdetect\n",
            "Installing collected packages: langdetect\n",
            "Successfully installed langdetect-1.0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8QWevst-g8G"
      },
      "source": [
        "from langdetect import detect"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lqw_98Vyohy",
        "outputId": "83dfa40a-81bd-4dbc-bee7-5537722d73fc"
      },
      "source": [
        "!pip install indic-transliteration"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting indic-transliteration\n",
            "  Downloading indic_transliteration-2.3.2-py3-none-any.whl (119 kB)\n",
            "\u001b[K     |████████████████████████████████| 119 kB 11.6 MB/s \n",
            "\u001b[?25hCollecting regex==2020.10.11\n",
            "  Downloading regex-2020.10.11-cp37-cp37m-manylinux2010_x86_64.whl (662 kB)\n",
            "\u001b[K     |████████████████████████████████| 662 kB 42.5 MB/s \n",
            "\u001b[?25hCollecting typer==0.3.2\n",
            "  Downloading typer-0.3.2-py3-none-any.whl (21 kB)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.7/dist-packages (from indic-transliteration) (0.10.2)\n",
            "Collecting selenium==3.141.0\n",
            "  Downloading selenium-3.141.0-py2.py3-none-any.whl (904 kB)\n",
            "\u001b[K     |████████████████████████████████| 904 kB 38.3 MB/s \n",
            "\u001b[?25hCollecting backports.functools-lru-cache==1.6.4\n",
            "  Downloading backports.functools_lru_cache-1.6.4-py2.py3-none-any.whl (5.9 kB)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from selenium==3.141.0->indic-transliteration) (1.24.3)\n",
            "Requirement already satisfied: click<7.2.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer==0.3.2->indic-transliteration) (7.1.2)\n",
            "Installing collected packages: typer, selenium, regex, backports.functools-lru-cache, indic-transliteration\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2019.12.20\n",
            "    Uninstalling regex-2019.12.20:\n",
            "      Successfully uninstalled regex-2019.12.20\n",
            "Successfully installed backports.functools-lru-cache-1.6.4 indic-transliteration-2.3.2 regex-2020.10.11 selenium-3.141.0 typer-0.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbxlAfLSzl6o"
      },
      "source": [
        "from indic_transliteration import sanscript\n",
        "from indic_transliteration.sanscript import transliterate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pw-YtL_yy7x4"
      },
      "source": [
        "def testing(test_sentence):\n",
        "  try:\n",
        "    if test_sentence and detect(test_sentence) == \"ml\":\n",
        "      test_sentence = transliterate(test_sentence, sanscript.MALAYALAM, sanscript.ITRANS)\n",
        "      test_sentence = test_sentence.lower()\n",
        "      print('MAL', test_sentence)\n",
        "      \n",
        "    tokens = tokenizer.texts_to_sequences(test_sentence)[0]\n",
        "    if len(tokens)>0:\n",
        "      ans = model.predict(tokens)\n",
        "      return categories[np.argmax(ans[0])]\n",
        "    else:\n",
        "      print('empty tokens')\n",
        "      return 'Not'\n",
        "  except Exception as e:\n",
        "    print(e)\n",
        "    print(test_sentence)\n",
        "    return 'Not'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J30lq03Vy7vM"
      },
      "source": [
        "test_df['label'] = test_df['text'].apply(lambda x: testing(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6_KLGk6y7sW",
        "outputId": "5e1467d0-dad7-40f9-8880-d2831ae02db1"
      },
      "source": [
        "len(test_df[test_df['label']=='Not']) / len(test_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.659"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uu45mbtuy7qC",
        "outputId": "1b3ab7a7-82c6-42d8-c715-d73d60fdefdf"
      },
      "source": [
        "len(test_df[test_df['label']=='Off']) / len(test_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.341"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEMdlJKiy7n2"
      },
      "source": [
        "test_df.to_csv('/content/drive/MyDrive/Datasets/HASOC-Dravidian-CodeMix-FIRE 2021/test/mal-results.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}