Next_Word_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Next Word Prediction.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aayushkubb/Deep_Learning_Tutorial/blob/master/Next_Word_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rl0nWwyKFuZ7"
      },
      "source": [
        "Deep Learning for NLP\n",
        "--\n",
        "\n",
        "Next Word Prediction\n",
        "--\n",
        "\n",
        "\n",
        "Autofill/showing what could be the potential sequence of words saves a\n",
        "lot of time while writing emails and makes users happy to use it in any\n",
        "product.\n",
        "\n",
        "Problem\n",
        "--\n",
        "You want to build a model to predict/suggest the next word based on a\n",
        "previous sequence of words using Email Data.\n",
        "\n",
        "Like you see in the below image, language is being suggested as the\n",
        "next word.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1xQcV7rei1McMMeVS76O2a7T5sVKt2J03\"  />"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8OTrfefFwjS"
      },
      "source": [
        "Solution\n",
        "--\n",
        "In this section, we will build an LSTM model to learn sequences of words\n",
        "from email data. We will use this model to predict the next word.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sM3zxOCf24wp"
      },
      "source": [
        "# Import Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LgJ7MsZL26jz"
      },
      "source": [
        "# Importing and installing necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "# Tokenizer\n",
        "from nltk.tokenize.toktok import ToktokTokenizer\n",
        "tokenizer = ToktokTokenizer()\n",
        "\n",
        "import collections\n",
        "from keras.utils import np_utils\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import LSTM\n",
        "from keras.callbacks import ModelCheckpoint"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUAvDKnJ21Ha"
      },
      "source": [
        "# Read Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dkhDvG3kBfYW",
        "outputId": "de85deaf-9926-4062-fd16-c0d3d673e9dc"
      },
      "source": [
        "sms_data = pd.read_csv(\"/content/drive/MyDrive/Case studies/spam.csv\",encoding=\"ISO-8859-1\")\n",
        "sms_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>v1</th>\n",
              "      <th>v2</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     v1  ... Unnamed: 4\n",
              "0   ham  ...        NaN\n",
              "1   ham  ...        NaN\n",
              "2  spam  ...        NaN\n",
              "3   ham  ...        NaN\n",
              "4   ham  ...        NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WqahRtf394Z"
      },
      "source": [
        "list_sms=sms_data['v2'].tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OxdLA5f5Y09"
      },
      "source": [
        "# Data Pre Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q85V8dXY4jJV",
        "outputId": "d60d265c-4381-4eab-bef8-41c80d41408c"
      },
      "source": [
        "# Processing the data\n",
        "\n",
        "# Converting list to string\n",
        "# Useful in general not specific to this dataset\n",
        "from collections import Iterable\n",
        "def flatten(items):\n",
        " \"\"\"Yield items from any nested iterable\"\"\"\n",
        " for x in items:\n",
        "    if isinstance(x, Iterable) and not isinstance(x,(str, bytes)):\n",
        "        for sub_x in flatten(x):\n",
        "            print(\"Hey\")\n",
        "            yield sub_x   # generator function -> all o/p gets stored in a temporary production table\n",
        "    else:\n",
        "        yield x\n",
        "\n",
        "TextData = list(flatten(list_sms))\n",
        "TextData = ''.join(TextData)\n",
        "print(TextData[:50])  ## just checking \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go until jurong point, crazy.. Available only in b\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlENTDM05cZu"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQ_Ohbr5B8rR",
        "outputId": "6669ac4f-2a76-4307-e299-48dcce034420"
      },
      "source": [
        "\n",
        "# Remove unwanted lines and converting into lower case\n",
        "TextData = TextData.replace('\\n','')\n",
        "TextData = TextData.lower()\n",
        "pattern = r'[^a-zA-Z0-9\\s]'\n",
        "\n",
        "TextData = re.sub(pattern, '', TextData)\n",
        "                            \n",
        "# Tokenizing\n",
        "tokens = tokenizer.tokenize(TextData)   # the long sentence is broken into tokens\n",
        "tokens = [token.strip() for token in tokens]  ## optional\n",
        "                            \n",
        "# get the distinct words and sort it\n",
        "word_counts = collections.Counter(tokens)\n",
        "corpusLen = len(tokens) \n",
        "word_c = len(word_counts) \n",
        "\n",
        "print(corpusLen) ## printing total no. of words\n",
        "print(word_c)  ## printing count of words\n",
        "print(word_counts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79788\n",
            "13232\n",
            "Counter({'to': 2233, 'you': 1915, 'i': 1847, 'a': 1410, 'the': 1287, 'u': 1017, 'and': 948, 'in': 861, 'is': 859, 'my': 714, 'me': 694, 'for': 685, 'your': 650, 'of': 616, 'have': 550, 'call': 543, 'it': 538, 'on': 519, 'that': 471, '2': 470, 'are': 450, 'or': 409, 'but': 403, 'so': 395, 'not': 393, 'at': 383, 'be': 382, 'now': 381, 'with': 376, 'ur': 371, 'get': 368, 'will': 365, 'can': 363, 'im': 360, 'do': 346, 'if': 345, 'just': 328, 'we': 309, 'this': 297, '4': 283, 'from': 272, 'go': 271, 'up': 270, 'its': 267, 'when': 264, 'dont': 260, 'ltgt': 257, 'all': 253, 'know': 242, 'out': 240, 'like': 231, 'free': 230, 'got': 223, 'no': 223, 'was': 220, 'ill': 216, 'come': 204, 'am': 203, 'what': 200, 'how': 199, 'time': 187, 'good': 185, 'then': 184, 'send': 180, 'only': 179, 'text': 179, 'love': 175, 'want': 174, 'by': 165, 'need': 164, 'he': 163, 'txt': 161, 'day': 160, 'about': 159, 'going': 159, 'as': 157, 'one': 156, 'there': 152, 'see': 148, 'still': 144, 'n': 142, 'our': 141, 'r': 140, 'mobile': 135, 'home': 135, 'back': 133, 'stop': 130, 'new': 129, 'been': 128, 'reply': 128, 'any': 127, 'think': 124, 'tell': 123, 'take': 120, 'she': 120, 'cant': 120, 'her': 117, 'has': 116, 'some': 115, 'ok': 115, 'phone': 114, 'they': 113, 'an': 113, 'claim': 112, 'lor': 109, 'who': 107, 'week': 103, 'da': 103, 'much': 103, 'more': 102, 'make': 99, 'today': 99, 'please': 98, 'did': 96, 'give': 96, 'd': 95, 'him': 94, 'hope': 93, 'had': 92, 'great': 91, 'here': 89, 'happy': 89, 'should': 89, 'way': 88, 'work': 88, 'prize': 86, 'ask': 86, 'said': 85, 'too': 84, 'after': 84, 'really': 82, 'amp': 82, 'night': 82, 'e': 81, 'say': 81, 'well': 81, 'didnt': 81, 'where': 81, 'were': 80, 'number': 80, 'c': 76, '1': 76, 'right': 76, 'msg': 76, 'pls': 76, 'doing': 75, 'very': 75, 'last': 75, 'meet': 74, 'won': 73, 'miss': 73, 'them': 73, 'thats': 72, 'would': 72, 'cash': 71, 'find': 71, 'cos': 71, 'every': 70, 'pick': 68, 'contact': 68, 'nokia': 67, 'wat': 67, 'already': 66, 'later': 65, 'life': 65, 'let': 64, 'b': 64, 'keep': 64, '3': 63, 'sent': 63, 'win': 62, 'sure': 62, 'morning': 62, 'why': 61, 'before': 61, 'ive': 60, 'message': 60, 'feel': 60, 'could': 59, 'sorry': 59, 'gonna': 58, 'next': 58, 'over': 58, 'anything': 58, 'went': 58, 'per': 57, 'even': 57, 'which': 57, 'something': 57, 'buy': 57, 'many': 57, 'friends': 55, 'his': 55, 'first': 55, 'babe': 55, 'service': 55, 'care': 55, 'wont': 54, 'always': 54, 'off': 54, 'other': 54, 'wan': 54, 'dun': 53, 'tomorrow': 53, 'also': 53, 'wait': 53, 'around': 52, 'things': 52, 'chat': 52, 'dear': 52, 'us': 52, 'told': 52, 'someone': 51, 'youre': 51, 'thing': 51, 'customer': 50, 'place': 50, 'money': 50, 'waiting': 50, 'guaranteed': 50, 'down': 49, 'late': 49, 'sleep': 49, 'leave': 49, 'getting': 48, 'again': 47, 'year': 47, 'thk': 47, 'nice': 47, 'wish': 47, 'tone': 47, 'help': 46, 'name': 45, 'try': 45, 'thought': 45, 'people': 45, 'gud': 45, 'friend': 45, 'v': 44, 'coming': 44, 'use': 43, 'yes': 43, 'being': 43, 'live': 43, 'trying': 43, 'holiday': 43, 'may': 42, 'draw': 42, 'line': 42, 'soon': 41, 'talk': 41, 'special': 41, '1000': 41, '500': 41, 'mins': 41, 'having': 41, 'lunch': 40, 'best': 40, 'smile': 40, 'few': 40, '5': 40, 'heart': 40, 'man': 39, 'never': 39, 'same': 39, 'stuff': 38, 'half': 38, 'awarded': 38, 'done': 38, 'long': 38, 'account': 38, 'because': 38, 'dat': 38, 'than': 38, 'chance': 38, 'receive': 37, 'latest': 37, '100': 37, 'finish': 37, 'another': 37, 'yet': 37, 'ya': 37, 'guys': 37, 'play': 36, 'havent': 36, 'better': 36, 'fine': 36, 'into': 36, 'mind': 36, 'hows': 35, 'job': 35, 'shows': 35, '2000': 35, 'big': 35, '1st': 35, '150': 34, 'eat': 34, 'end': 34, 'wanna': 34, 'hi': 34, 'thanks': 34, 'luv': 34, 'yeah': 34, 'person': 34, 'tonight': 33, 'car': 33, 'bit': 33, 'landline': 33, 'called': 33, 'start': 33, 'might': 33, 'meeting': 33, 'world': 32, 'sms': 32, 'check': 32, 'real': 32, 'y': 32, 'girl': 32, 'ready': 32, 'box': 32, 'word': 31, 'camera': 31, 'cost': 31, '16': 31, 'hear': 31, 'class': 31, 'between': 31, '18': 31, 'po': 31, 'watch': 31, 'ever': 31, 'quite': 31, 'days': 31, 'enjoy': 31, 'speak': 30, 'code': 30, 'x': 30, '2nd': 30, 'sweet': 30, 'guess': 30, 'birthday': 30, 'little': 30, 'reach': 30, 'video': 30, '6': 30, 'lar': 29, 'hes': 29, 'part': 29, 'dis': 29, 'looking': 29, 'baby': 29, 'made': 29, 'two': 29, 'put': 29, 'den': 29, 'bad': 29, 'early': 28, 'network': 28, 'theres': 28, 'look': 28, 'once': 28, 'without': 28, 'boy': 28, 'offer': 28, 'everything': 28, 'shit': 28, 'id': 27, 'selected': 27, 'pay': 27, 'does': 27, 'doesnt': 27, 'most': 27, 'cool': 27, 'came': 27, 'lot': 27, 'missing': 27, 'shopping': 27, 'easy': 27, 'orange': 27, 'until': 26, 'wif': 26, 'entry': 26, 'goes': 26, 'remember': 26, 'left': 26, 'dinner': 26, 'wanted': 26, 'maybe': 26, 'since': 26, 'means': 26, 'abt': 26, 'says': 26, 'award': 26, 'tv': 26, 'jus': 26, 'able': 26, 'hav': 26, 'collect': 26, 'oh': 26, 'asked': 26, 'problem': 26, 'tcs': 26, 'probably': 26, 'collection': 26, 'enough': 25, 'room': 25, 'month': 25, 'forgot': 25, 'must': 25, 'join': 25, 'til': 25, 'face': 25, 'texts': 25, 'sat': 25, 'actually': 25, 'god': 25, 'princess': 25, 'while': 25, 's': 25, 'town': 25, 'away': 25, 'plus': 25, 'fun': 24, 'k': 24, 'watching': 24, 'bus': 24, 'till': 24, 'ringtone': 24, 'mob': 24, 'bed': 24, 'afternoon': 24, 'price': 24, 'school': 24, 'those': 24, 'liao': 24, 'working': 24, 'wake': 24, 'tones': 24, 'weekly': 24, 'bring': 24, 'attempt': 24, 'fuck': 24, 'gift': 24, 'whats': 24, 'hey': 23, 'valid': 23, 'update': 23, 'pain': 23, '5000': 23, 'making': 23, 'juz': 23, 'bt': 23, 'sexy': 23, 'house': 23, 'years': 23, 'music': 23, 'minutes': 23, 'dad': 23, 'makes': 23, '9': 22, 'tried': 22, 'saw': 22, 'yourself': 22, 'hair': 22, 'show': 22, 'wk': 22, 'run': 22, 'nothing': 22, 'top': 22, 'evening': 22, 'ltdecimalgt': 22, 'plan': 22, 'stay': 22, 'min': 22, 'shall': 22, 'wants': 22, 'guy': 22, 'shes': 22, 'apply': 21, 'colour': 21, 'tc': 21, 'bonus': 21, 'driving': 21, 'anyway': 21, 'pa': 21, 'xmas': 21, 'times': 21, 'leh': 21, 'wid': 21, 'mail': 21, 'plz': 21, '250': 21, 'tot': 21, 'sae': 21, 'details': 21, '800': 21, 'de': 21, 'hours': 20, 'trip': 20, 'mean': 20, 'haf': 20, 'order': 20, 'oso': 20, 'noe': 20, 'missed': 20, 'vouchers': 20, 'club': 20, 'lets': 20, 'calls': 20, 'book': 20, 'hurt': 20, 'change': 20, '7': 20, 'game': 20, 'saying': 20, 'feeling': 20, '10': 20, 'await': 20, 'online': 20, 'weeks': 19, 'pounds': 19, 'delivery': 19, 'yours': 19, 'comes': 19, 'test': 19, 'isnt': 19, 'old': 19, 'points': 19, 'm': 19, 'haha': 19, 'important': 19, 'coz': 19, 'full': 19, 'hour': 19, 'todays': 19, 'calling': 19, 'messages': 19, 'office': 19, 'either': 19, 'these': 19, 'sch': 19, 'both': 19, 'goin': 19, 'charge': 19, 'brother': 18, 'set': 18, 'words': 18, 'weekend': 18, 're': 18, 'though': 18, 'took': 18, 'started': 18, 'wen': 18, 'worry': 18, 'services': 18, 'taking': 18, 'everyone': 18, 'walk': 18, 'wil': 18, 'angry': 18, 'true': 18, '750': 18, 'unsubscribe': 18, '86688': 18, 'dunno': 18, 'post': 18, 'land': 18, 'lose': 18, 'bored': 18, 'simple': 18, 'pics': 18, 'tscs': 18, 'family': 18, 'urgent': 18, '8007': 18, 'drop': 18, 'movie': 18, 'date': 17, 'news': 17, 'ard': 17, 'forget': 17, 'second': 17, 'yesterday': 17, 'cause': 17, 'else': 17, '12hrs': 17, 'bday': 17, 'statement': 17, 'answer': 17, 'lots': 17, 'believe': 17, 'sis': 17, 'tmr': 17, 'finished': 17, 'g': 17, 'ring': 17, 'hot': 17, 'choose': 17, 'food': 17, 'games': 17, 'sister': 17, 'wot': 17, 'hello': 17, 'mei': 17, 'poly': 17, 'drink': 17, 'head': 17, '150ppm': 17, 'rate': 17, 'thinking': 17, 'aft': 17, 'voucher': 17, 'knw': 17, 'drive': 17, 'wife': 17, 'lei': 17, 'neva': 17, 'final': 16, 'pobox': 16, 'uk': 16, 'lesson': 16, 'decided': 16, 'lucky': 16, 'search': 16, 'address': 16, 'unredeemed': 16, 'identifier': 16, 'leaving': 16, 'needs': 16, 'gr8': 16, 'takes': 16, 'found': 16, 'card': 16, 'mobileupd8': 16, 'dating': 16, 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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2YlMvlnCAGm",
        "outputId": "d82c856e-f55e-49d0-d967-3be9f1f7a803"
      },
      "source": [
        "##print(word_c)\n",
        "\n",
        "distinct_words_sorted = [x[0] for x in word_counts.most_common()]\n",
        "##distinct_words_sorted = list(sorted(distinct_words))\n",
        "\n",
        "# Generate indexing for all words\n",
        "word_index = {x: i for i, x in enumerate(distinct_words_sorted)}\n",
        "\n",
        "print(word_index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'to': 0, 'you': 1, 'i': 2, 'a': 3, 'the': 4, 'u': 5, 'and': 6, 'in': 7, 'is': 8, 'my': 9, 'me': 10, 'for': 11, 'your': 12, 'of': 13, 'have': 14, 'call': 15, 'it': 16, 'on': 17, 'that': 18, '2': 19, 'are': 20, 'or': 21, 'but': 22, 'so': 23, 'not': 24, 'at': 25, 'be': 26, 'now': 27, 'with': 28, 'ur': 29, 'get': 30, 'will': 31, 'can': 32, 'im': 33, 'do': 34, 'if': 35, 'just': 36, 'we': 37, 'this': 38, '4': 39, 'from': 40, 'go': 41, 'up': 42, 'its': 43, 'when': 44, 'dont': 45, 'ltgt': 46, 'all': 47, 'know': 48, 'out': 49, 'like': 50, 'free': 51, 'got': 52, 'no': 53, 'was': 54, 'ill': 55, 'come': 56, 'am': 57, 'what': 58, 'how': 59, 'time': 60, 'good': 61, 'then': 62, 'send': 63, 'only': 64, 'text': 65, 'love': 66, 'want': 67, 'by': 68, 'need': 69, 'he': 70, 'txt': 71, 'day': 72, 'about': 73, 'going': 74, 'as': 75, 'one': 76, 'there': 77, 'see': 78, 'still': 79, 'n': 80, 'our': 81, 'r': 82, 'mobile': 83, 'home': 84, 'back': 85, 'stop': 86, 'new': 87, 'been': 88, 'reply': 89, 'any': 90, 'think': 91, 'tell': 92, 'take': 93, 'she': 94, 'cant': 95, 'her': 96, 'has': 97, 'some': 98, 'ok': 99, 'phone': 100, 'they': 101, 'an': 102, 'claim': 103, 'lor': 104, 'who': 105, 'week': 106, 'da': 107, 'much': 108, 'more': 109, 'make': 110, 'today': 111, 'please': 112, 'did': 113, 'give': 114, 'd': 115, 'him': 116, 'hope': 117, 'had': 118, 'great': 119, 'here': 120, 'happy': 121, 'should': 122, 'way': 123, 'work': 124, 'prize': 125, 'ask': 126, 'said': 127, 'too': 128, 'after': 129, 'really': 130, 'amp': 131, 'night': 132, 'e': 133, 'say': 134, 'well': 135, 'didnt': 136, 'where': 137, 'were': 138, 'number': 139, 'c': 140, '1': 141, 'right': 142, 'msg': 143, 'pls': 144, 'doing': 145, 'very': 146, 'last': 147, 'meet': 148, 'won': 149, 'miss': 150, 'them': 151, 'thats': 152, 'would': 153, 'cash': 154, 'find': 155, 'cos': 156, 'every': 157, 'pick': 158, 'contact': 159, 'nokia': 160, 'wat': 161, 'already': 162, 'later': 163, 'life': 164, 'let': 165, 'b': 166, 'keep': 167, '3': 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2725, 'drpd': 2726, 'deeraj': 2727, 'deepak': 2728, 'dawhat': 2729, 'doinghow': 2730, '2wks': 2731, 'lag': 2732, 'stuffim': 2733, 'necessarily': 2734, 'yummy': 2735, 'jolt': 2736, 'networks': 2737, 'parked': 2738, 'naked': 2739, '150pwk': 2740, 'chart': 2741, 'jenny': 2742, '021': 2743, 'grave': 2744, 'bslvyli': 2745, 'tomocant': 2746, 'shocking': 2747, 'scary': 2748, 'crash': 2749, 'blind': 2750, 'thread': 2751, 'funky': 2752, 'tahan': 2753, 'lo': 2754, 'buses': 2755, 'bristol': 2756, 'dahow': 2757, 'record': 2758, 'pence': 2759, '0844': 2760, '861': 2761, 'prepayment': 2762, 'privacy': 2763, 'paperwork': 2764, 'beforejust': 2765, 'bless': 2766, 'misbehaved': 2767, 'alcohol': 2768, 'unemployed': 2769, 'status': 2770, 'alive': 2771, 'breathe': 2772, 'cuddling': 2773, 'agree': 2774, 'happenyou': 2775, 'recognise': 2776, 'ovulation': 2777, 'carehey': 2778, 'grace': 2779, 'loxahatchee': 2780, 'tree': 2781, 'burning': 2782, 'slightly': 2783, 'darlings': 2784, 'wld': 2785, 'sendername': 2786, 'sentdate': 2787, 'goal': 2788, '80086': 2789, 'name1': 2790, 'name2': 2791, 'mobno': 2792, 'adam': 2793, '07123456789': 2794, 'txtno': 2795, 'ads': 2796, 'hostel': 2797, 'expression': 2798, 'irritating': 2799, 'esplanade': 2800, '3650': 2801, '09066382422': 2802, '300603': 2803, 'bcm4284': 2804, 'applebees': 2805, 'greatbhaji': 2806, 'cricketer': 2807, 'sachin': 2808, 'worldvery': 2809, 'outyou': 2810, 'improve': 2811, 'oreo': 2812, 'amy': 2813, 'yagoing': 2814, 'decisions': 2815, 'coping': 2816, '0207': 2817, '153': 2818, 'july': 2819, 'roads': 2820, '09061743806': 2821, 'box326': 2822, 'screamed': 2823, 'removed': 2824, '9ja': 2825, 'broken': 2826, 'infront': 2827, 'ahwhat': 2828, 'taste': 2829, 'thingyou': 2830, 'trade': 2831, 'laterwhat': 2832, 'rec': 2833, '09050002311': 2834, 'b4280703': 2835, '08718727868': 2836, '150ppmno': 2837, 'hyde': 2838, 'anthony': 2839, 'scrounge': 2840, 'forgiven': 2841, 'slide': 2842, 'renewal': 2843, 'transport': 2844, 'definite': 2845, 'nos': 2846, 'ebay': 2847, '020903': 2848, 'xyes': 2849, '24hrs': 2850, '08718738001': 2851, 'tirupur': 2852, 'web': 2853, 'develop': 2854, 'ability': 2855, 'recovery': 2856, 'therei': 2857, 'apologise': 2858, 'cutting': 2859, 'reminding': 2860, 'owns': 2861, 'faggy': 2862, 'demand': 2863, 'milk': 2864, 'loose': 2865, 'pan': 2866, 'thathi': 2867, 'meim': 2868, 'jen': 2869, 'claims': 2870, 'dancing': 2871, 'timei': 2872, 'hardly': 2873, 'wwwtxt2shopcom': 2874, '08712402050': 2875, '10ppm': 2876, 'ag': 2877, '0825': 2878, 'tsunamis': 2879, 'swimming': 2880, 'soiree': 2881, 'nichols': 2882, 'ques': 2883, 'suits': 2884, 'reaction': 2885, 'useful': 2886, 'officially': 2887, 'market': 2888, 'textbuddy': 2889, 'postcode': 2890, 'gaytextbuddycom': 2891, '4882': 2892, '09064019014': 2893, 'uoh': 2894, 'httpwwwetlpcoukexpressoffer': 2895, 'effects': 2896, 'wee': 2897, 'taken': 2898, 'trains': 2899, '40533': 2900, 'rstm': 2901, 'sw7': 2902, '3ss': 2903, 'panic': 2904, 'river': 2905, 'premium': 2906, 'lays': 2907, 'en': 2908, 'yelling': 2909, 'todaywhat': 2910, 'hex': 2911, 'sue': 2912, 'cochin': 2913, 'farm': 2914, '4d': 2915, 'purchase': 2916, 'gpu': 2917, 'aeronautics': 2918, 'professors': 2919, 'calld': 2920, 'aeroplane': 2921, 'hurried': 2922, 'saidif': 2923, '1250': 2924, '09071512433': 2925, '050703': 2926, 'tcsbcm4235wc1n3xx': 2927, 'callcost': 2928, 'mobilesvary': 2929, 'cookies': 2930, 'themthe': 2931, 'ba': 2932, 'spring': 2933, 'methanks': 2934, 'offerthe': 2935, 'nokia6650': 2936, 'txtauction': 2937, '81151': 2938, '4tctxt': 2939, 'attached': 2940, 'itdo': 2941, 'helpline': 2942, '40': 2943, 'thousands': 2944, 'gimme': 2945, 'lip': 2946, 'confused': 2947, 'spare': 2948, 'mt': 2949, 'tscs087147403231winawkage16': 2950, 'timesorry': 2951, 'messagelol': 2952, 'schools': 2953, 'inch': 2954, 'getnot': 2955, 'anywhere': 2956, 'begging': 2957, '0578': 2958, 'nowdont': 2959, 'pole': 2960, 'thot': 2961, '8077': 2962, 'cashto': 2963, '08000407165': 2964, 'getstop': 2965, '88222': 2966, 'bec': 2967, 'hint': 2968, 'galileo': 2969, 'loveme': 2970, 'cappuccino': 2971, 'dearhey': 2972, '09065174042': 2973, 'hol': 2974, 'skyped': 2975, 'kz': 2976, 'given': 2977, 'ultimatum': 2978, 'countin': 2979, 'aburo': 2980, 'daydo': 2981, '08002888812': 2982, 'inconsiderate': 2983, 'nag': 2984, 'recession': 2985, 'hence': 2986, 'soo': 2987, '09066350750': 2988, 'seconds': 2989, 'jays': 2990, 'weighti': 2991, 'shoes': 2992, 'firsti': 2993, 'careful': 2994, 'discreet': 2995, 'named': 2996, 'connections': 2997, 'lotta': 2998, 'virgin': 2999, 'wwwsmsconet': 3000, 'approx': 3001, 'consider': 3002, 'peaceful': 3003, '41685': 3004, 'shesil': 3005, '5k': 3006, '09064011000': 3007, 'cr01327bt': 3008, 'fixedline': 3009, 'downloaded': 3010, 'ear': 3011, 'oil': 3012, 'sup': 3013, 'mac': 3014, 'usb': 3015, 'ie': 3016, 'several': 3017, 'taylor': 3018, 'charles': 3019, 'themob': 3020, '08709222922': 3021, '15pmin': 3022, '78pmin': 3023, 'peak': 3024, 'chip': 3025, 'bslvylhey': 3026, 'minsbut': 3027, 'jeans': 3028, 'tons': 3029, 'scores': 3030, 'application': 3031, 'research': 3032, 'bowl': 3033, 'simpler': 3034, '09050001808': 3035, 'm95': 3036, 'uyo': 3037, 'necklace': 3038, 'racing': 3039, 'rice': 3040, 'thenyup': 3041, 'crap': 3042, 'borin': 3043, 'chocolate': 3044, 'dayshe': 3045, 'ubi': 3046, 'tech': 3047, 'butt': 3048, 'peace': 3049, 'jesus': 3050, 'blessed': 3051, 'quiet': 3052, 'aunts': 3053, 'helen': 3054, 'ami': 3055, 'meat': 3056, 'lovers': 3057, 'anniversary': 3058, 'secretly': 3059, 'datebox1282essexcm61xn': 3060, 'pattern': 3061, 'plm': 3062, 'sheffield': 3063, 'setting': 3064, 'filling': 3065, 'sufficient': 3066, 'rightly': 3067, 'viva': 3068, 'roast': 3069, 'ls15hb': 3070, 'educational': 3071, 'flirting': 3072, 'bloke': 3073, 'kickoff': 3074, 'sells': 3075, 'drunk': 3076, 'sends': 3077, 'deciding': 3078, 'dot': 3079, 'compare': 3080, 'herself': 3081, 'violet': 3082, 'tulip': 3083, 'lily': 3084, 'prepared': 3085, 'thenno': 3086, '09058091854': 3087, 'box385': 3088, 'm6': 3089, '09050003091': 3090, 'c52': 3091, 'breath': 3092, 'heri': 3093, 'craziest': 3094, 'herlove': 3095, 'singing': 3096, 'curry': 3097, 'lotwill': 3098, '09061221061': 3099, '28days': 3100, 'box177': 3101, 'm221bp': 3102, '2yr': 3103, 'warranty': 3104, 'fret': 3105, 'depressed': 3106, 'aiyah': 3107, 'math': 3108, 'speed': 3109, 'speedchat': 3110, '08000776320': 3111, 'youaiyar': 3112, 'brothers': 3113, 'difficulties': 3114, 'latergood': 3115, 'thereu': 3116, 'sar': 3117, 'nightand': 3118, '4fil': 3119, 'silently': 3120, 'wrc': 3121, 'lucozade': 3122, 'le': 3123, '61200': 3124, 'packs': 3125, 'lucozadecoukwrc': 3126, 'itcould': 3127, 'toot': 3128, 'bigger': 3129, 'daim': 3130, 'makin': 3131, 'jordan': 3132, 'matter': 3133, '2go': 3134, 'beneficiary': 3135, 'appreciated': 3136, 'creepy': 3137, 'finewhen': 3138, 'nok': 3139, '87021': 3140, 'apps': 3141, 'invest': 3142, '84128': 3143, 'hun': 3144, 'flip': 3145, 'uwe': 3146, 'jd': 3147, 'weirdest': 3148, 'bye': 3149, 'thts': 3150, 'uif': 3151, 'dough': 3152, 'control': 3153, 'jerry': 3154, 'irritates': 3155, 'fails': 3156, 'drinkin': 3157, 'birthdate': 3158, 'nydc': 3159, 'items': 3160, 'ps': 3161, 'dads': 3162, 'jamster': 3163, 'gold': 3164, '16only': 3165, 'lions': 3166, 'lionm': 3167, 'lionp': 3168, 'jokin': 3169, 'alrite': 3170, 'colours': 3171, 'whenevr': 3172, 'potter': 3173, 'phoenix': 3174, 'todayjust': 3175, 'canada': 3176, 'cares': 3177, 'interest': 3178, 'chill': 3179, 'ar': 3180, 'georges': 3181, 'jordantxt': 3182, '89080': 3183, 'celeb4': 3184, '0870241182716': 3185, 'tmrw': 3186, 'soul': 3187, 'ned': 3188, 'hurting': 3189, '4a': 3190, 'whn': 3191, 'dance': 3192, 'moji': 3193, 'comei': 3194, 'bar': 3195, 'dick': 3196, '08718730666': 3197, '08718726270150gbpmtmsg18': 3198, 'juan': 3199, 'phonedouble': 3200, 'tlk': 3201, 'ideal': 3202, 'front': 3203, 'tirunelvali': 3204, 'personthats': 3205, 'ki': 3206, 'effect': 3207, 'kidding': 3208, 'sinco': 3209, 'payee': 3210, 'icicibankcom': 3211, 'beware': 3212, 'frauds': 3213, 'disclose': 3214, 'practicing': 3215, 'beneath': 3216, 'pale': 3217, 'silver': 3218, 'silence': 3219, 'wwwldewcom': 3220, 'revision': 3221, 'fran': 3222, 'exeter': 3223, 'whose': 3224, 'coat': 3225, 'painful': 3226, 'everybody': 3227, 'sen': 3228, 'guilty': 3229, 'tues': 3230, 'textpod': 3231, 'wwwtextpodnet': 3232, 'desperate': 3233, 'monkeys': 3234, 'practical': 3235, 'claire': 3236, 'costing': 3237, 'lyfu': 3238, 'lyf': 3239, 'ke': 3240, '09066362231': 3241, 'asapok': 3242, 'lucy': 3243, 'hubby': 3244, 'deartake': 3245, 'thisi': 3246, 'modules': 3247, 'evng': 3248, 'messenger': 3249, 'sha': 3250, 'dawhats': 3251, 'okoh': 3252, 'testing': 3253, 'okhey': 3254, 'format': 3255, 'shortly': 3256, 'forum': 3257, 'ron': 3258, 'aunt': 3259, 'konw': 3260, 'waht': 3261, 'rael': 3262, 'gving': 3263, 'exmpel': 3264, 'jsut': 3265, 'evrey': 3266, 'splleing': 3267, 'wrnog': 3268, 'sitll': 3269, 'raed': 3270, 'wihtuot': 3271, 'ayn': 3272, 'mitsake': 3273, 'sleepsweet': 3274, 'joining': 3275, 'filled': 3276, 'jia': 3277, 'rhythm': 3278, 'wifi': 3279, 'himi': 3280, 'rumour': 3281, '7250': 3282, 'wah': 3283, 'idk': 3284, 'boyfriend': 3285, 'driver': 3286, 'kicks': 3287, 'dime': 3288, 'transfer': 3289, 'falling': 3290, 'smeone': 3291, 'flame': 3292, 'propose': 3293, 'dippeditinadew': 3294, 'lovingly': 3295, 'touched': 3296, 'itwhichturnedinto': 3297, 'gifted': 3298, 'tomeandsaidthis': 3299, 'outk': 3300, 'batch': 3301, 'flaky': 3302, 'sooooo': 3303, 'winning': 3304, 'tooo': 3305, 'confuses': 3306, 'wating': 3307, 'okyou': 3308, 'british': 3309, 'hotels': 3310, '6pm': 3311, 'adoring': 3312, 'dracula': 3313, 'ghost': 3314, 'addamsfa': 3315, 'munsters': 3316, 'exorcist': 3317, 'twilight': 3318, 'constant': 3319, 'cared': 3320, 'allow': 3321, 'moby': 3322, '82242': 3323, 'msg150p': 3324, 'hlp': 3325, '08712317606': 3326, 'switch': 3327, 'six': 3328, 'loud': 3329, 'bagi': 3330, 'roomi': 3331, '80608': 3332, 'wwwmovietriviatv': 3333, '08712405022': 3334, '1x150pwki': 3335, 'partnership': 3336, 'freesend': 3337, '816183': 3338, 'weekstop': 3339, 'messy': 3340, 'traffic': 3341, 'moves': 3342, 'wkg': 3343, 'keeps': 3344, 'gotten': 3345, 'unknown': 3346, '09094646899': 3347, 'vu': 3348, '2007': 3349, 'maangalyam': 3350, 'alaipayuthe': 3351, 'tonighthi': 3352, 'telephone': 3353, 'callfreefone': 3354, '08081560665': 3355, 'of2000': 3356, '18only': 3357, 'calm': 3358, 'up4': 3359, 'xi': 3360, 'youmy': 3361, 'becomes': 3362, 'habit': 3363, 'schedule': 3364, 'contacts': 3365, 'forgets': 3366, '07734396839': 3367, 'ibh': 3368, 'nokia6600': 3369, 'txtauctiontxt': 3370, 'wordstart': 3371, 'no81151': 3372, 'invaders': 3373, 'orig': 3374, 'console': 3375, 'o2coukgames': 3376, 'latermy': 3377, 'transfr': 3378, 'philosophy': 3379, 'foley': 3380, 'prizes': 3381, 'desparate': 3382, '3100': 3383, 'pai': 3384, 'combine': 3385, 'chatting': 3386, 'supervisor': 3387, 'package': 3388, 'g696ga': 3389, 'overi': 3390, 'joanna': 3391, 'replacement': 3392, 'telly': 3393, '12mths': 3394, 'btooth': 3395, 'delete': 3396, 'skype': 3397, 'laundry': 3398, 'waheed': 3399, 'pushes': 3400, 'knees': 3401, 'toooh': 3402, 'dayok': 3403, 'avoiding': 3404, '0776xxxxxxx': 3405, '326': 3406, 'total': 3407, 'heads': 3408, 'vday': 3409, 'therere': 3410, 'table': 3411, 'build': 3412, 'snowman': 3413, 'prescription': 3414, 'electricity': 3415, 'fujitsu': 3416, 'scold': 3417, 'wud': 3418, '09066358152': 3419, 'prompts': 3420, 'disturbing': 3421, 'ideas': 3422, 'stops': 3423, 'upset': 3424, 'flies': 3425, 'woken': 3426, 'aka': 3427, 'ic': 3428, 'delhi': 3429, 'held': 3430, 'latersorry': 3431, 'fringe': 3432, 'distract': 3433, 'proof': 3434, '61610': 3435, 'help08712400602450p': 3436, 'responsibility': 3437, '08006344447': 3438, 'onwards': 3439, 'mehi': 3440, 'kid': 3441, 'energyim': 3442, 'aom': 3443, 'nd': 3444, 'parco': 3445, 'nb': 3446, 'hallaq': 3447, 'bck': 3448, 'jealous': 3449, 'gender': 3450, 'sleepwellamptake': 3451, 'normally': 3452, 'therehey': 3453, 'omg': 3454, 'tming': 3455, 'rub': 3456, 'clever': 3457, 'stamped': 3458, '113': 3459, 'bray': 3460, 'wicklow': 3461, 'eire': 3462, 'meits': 3463, 'wwwidewcom': 3464, 'skillgame': 3465, '1winaweek': 3466, 'xam': 3467, 'manage': 3468, 'diamonds': 3469, 'china': 3470, 'mcat': 3471, 'processits': 3472, 'sacrifice': 3473, 'beg': 3474, 'morphine': 3475, 'pleaseyou': 3476, 'stayin': 3477, 'sorted': 3478, 'freemsg': 3479, 'satisfy': 3480, 'cld': 3481, 'dayswhat': 3482, 'wales': 3483, 'killed': 3484, 'smashed': 3485, 'everybodys': 3486, 'tok': 3487, 'specific': 3488, 'pages': 3489, 'figures': 3490, 'neck': 3491, 'continue': 3492, 'billion': 3493, 'classes': 3494, 'distance': 3495, 'wati': 3496, 'turning': 3497, 'ref': 3498, 'belive': 3499, 'slots': 3500, 'management': 3501, 'spoiled': 3502, 'complaint': 3503, 'lk': 3504, 'lov': 3505, '01223585334': 3506, '2c': 3507, 'shagged': 3508, '2end': 3509, '88066': 3510, 'dinneri': 3511, 'waited': 3512, 'huge': 3513, 'mids': 3514, 'oranges': 3515, 'upd8': 3516, '21870000hi': 3517, 'mailbox': 3518, 'messaging': 3519, '21': 3520, '09056242159': 3521, 'retrieve': 3522, 'poem': 3523, 'thatll': 3524, 'quizwin': 3525, 'duchess': 3526, '82277unsub': 3527, '008704050406': 3528, 'sp': 3529, 'nahi': 3530, 'wo': 3531, 'jo': 3532, 'dan': 3533, 'surfing': 3534, 'staring': 3535, 'noti': 3536, 'problemi': 3537, 'cm': 3538, 'unnecessarily': 3539, '80082': 3540, '08701417012150p': 3541, 'weigh': 3542, 'regret': 3543, 'gamestar': 3544, 'active': 3545, '250k': 3546, 'scoring': 3547, 'nowsky': 3548, 'opinions': 3549, 'mina': 3550, 'propsd': 3551, 'gv': 3552, 'lv': 3553, 'lttrs': 3554, 'threw': 3555, 'aproach': 3556, 'dt': 3557, 'truck': 3558, 'speeding': 3559, 'wn': 3560, 'girld': 3561, 'thy': 3562, 'lived': 3563, 'happily': 3564, '2gthr': 3565, 'evrydy': 3566, 'msgsd': 3567, 'paragon': 3568, 'bluff': 3569, 'wwwfullonsmscom': 3570, 'brandy': 3571, 'rum': 3572, 'gin': 3573, 'vodka': 3574, 'scotch': 3575, 'shampain': 3576, 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'rememberujhhhhhhh': 11269, 'shipped': 11270, 'sandiago': 11271, 'parantella': 11272, 'poopmm': 11273, 'hugging': 11274, 'pi': 11275, 'sweater': 11276, 'mango': 11277, 'irritating1': 11278, 'involved': 11279, 'possibleanytime': 11280, 'lordo': 11281, 'ampurity': 11282, 'evngur': 11283, 'landmark': 11284, 'bob': 11285, 'barry': 11286, '83738': 11287, 'luckme': 11288, 'finek': 11289, 'consent': 11290, 'forms': 11291, 'vur': 11292, 'tonexs': 11293, 'renewed': 11294, 'wwwclubzedcouk': 11295, 'billing': 11296, 'msgif': 11297, 'busyhow': 11298, '9jadoing': 11299, 'ushow': 11300, 'youdone': 11301, 'tomomathews': 11302, 'tait': 11303, 'edwards': 11304, 'andersonyeah': 11305, 'haunt': 11306, 'promoting': 11307, 'jokeno': 11308, 'engin': 11309, 'todaythanks': 11310, 'crowd': 11311, '8000930705': 11312, 'snowboarding': 11313, 'goa': 11314, 'affair': 11315, 'livesee': 11316, 'christmassy': 11317, 'herek': 11318, 'lovethe': 11319, 'tcwhat': 11320, 'plansrecpt': 11321, '13': 11322, 'processedbaaaaaaaabe': 11323, 'youonly': 11324, 'ignoring': 11325, 'isdear': 11326, 'drivewhen': 11327, 'backcan': 11328, 'shola': 11329, 'medicine': 11330, 'sagamu': 11331, 'lautech': 11332, 'vital': 11333, 'completes': 11334, 'education': 11335, 'zealandyes': 11336, 'itjuz': 11337, 'qetmany': 11338, 'ni8just': 11339, 'homesorry': 11340, 'minsdun': 11341, 'browser': 11342, 'surfas': 11343, 'subscribers': 11344, 'gb': 11345, 'stopawesome': 11346, 'bittake': 11347, 'wellyou': 11348, 'lifeyou': 11349, 'thati': 11350, 'conversations': 11351, 'usget': 11352, 'timeyour': 11353, 'sensesrespect': 11354, 'overemphasiseor': 11355, 'lifefor': 11356, 'headset': 11357, 'adpyou': 11358, 'hiya': 11359, 'internal': 11360, 'extract': 11361, 'themif': 11362, 'desad': 11363, 'wishhow': 11364, 'godyou': 11365, 'immedyeah': 11366, 'upv': 11367, 'skint': 11368, 'fancied': 11369, 'bevieswaz': 11370, 'othrs': 11371, 'spoon': 11372, 'watchng': 11373, 'planet': 11374, 'earthsofa': 11375, 'comfey': 11376, 'quitting': 11377, 'least5times': 11378, 'wudnt': 11379, 'mehow': 11380, 'housestill': 11381, 'coast': 11382, 'backmmm': 11383, 'mealright': 11384, 'ima': 11385, 'sleepupdate': 11386, 'frequently': 11387, 'messageit': 11388, 'dawas': 11389, 'ateso': 11390, 'moneyaiyar': 11391, 'cupboard': 11392, 'comehey': 11393, 'earlyyar': 11394, 'route': 11395, 'too2mro': 11396, 'goodnightdont': 11397, 'buckscan': 11398, 'lib': 11399, 'yetsounds': 11400, 'nightdont': 11401, 'floppy': 11402, 'snappy': 11403, '08712460324housemaid': 11404, 'officeunderstandhow': 11405, 'donothing': 11406, 'risk': 11407, 'grasp': 11408, 'flavourim': 11409, 'laready': 11410, 'reachingdear': 11411, 'denying': 11412, 'dom': 11413, 'pmcome': 11414, 'ffffuuuuuuu2': 11415, 'elsemerry': 11416, 'annieplease': 11417, 'abouti': 11418, 'likeurgent': 11419, '3ajawesome': 11420, 'minuteand': 11421, 'julianaland': 11422, 'oblivious': 11423, 'upsetits': 11424, 'madive': 11425, 'dehydratedor': 11426, 'minim': 11427, 'reachstorming': 11428, 'personif': 11429, 'mapquest': 11430, 'dogwood': 11431, 'driveaight': 11432, 'tonightdie': 11433, 'archive': 11434, 'sadwelcome': 11435, 'ukmobiledate': 11436, '08719839835': 11437, 'mgs': 11438, '89123this': 11439, 'lengths': 11440, 'behalf': 11441, 'stunning': 11442, 'sorrythanks': 11443, 'visa': 11444, 'gucci': 11445, 'shasorry': 11446, 'shits': 11447, 'tonighthe': 11448, 'lehranjith': 11449, 'holdcheers': 11450, 'babesozi': 11451, 'culdnt': 11452, 'talkbut': 11453, 'wannatell': 11454, 'wenwecan': 11455, 'xhey': 11456, 'gymshe': 11457, 'wifemuch': 11458, 'lolnothing': 11459, 'smsing': 11460, 'meetinglemme': 11461, 'semesterwa': 11462, 'efficient': 11463, 'thanx3': 11464, 'enjoys': 11465, 'sleepwant': 11466, '20pminwe': 11467, 'ttylill': 11468, '515pm': 11469, 'comeoh': 11470, 'lorerutupalam': 11471, 'thandiyachuy': 11472, 'invention': 11473, 'flyim': 11474, 'jokingnoits': 11475, 'lyricswhat': 11476, 'reckon': 11477, 'thankstrue': 11478, 'nevr': 11479, 'unrecognized': 11480, 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11533, 'kissnow': 11534, 'comemoney': 11535, 'wining': 11536, '946': 11537, 'nextsure': 11538, 'gtthat': 11539, 'roomate': 11540, 'graduatedcongrats': 11541, '150pmmen': 11542, 'adjustable': 11543, 'cooperative': 11544, 'allows': 11545, 'wifethat': 11546, 'nottingham': 11547, '63miles': 11548, '40mph': 11549, 'howhat': 11550, 'nothi': 11551, 'mornin': 11552, 'thanku': 11553, 'samedo': 11554, '2morroactually': 11555, 'vive': 11556, 'partcos': 11557, 'guessed': 11558, 'hubbys': 11559, '89938': 11560, 'strings': 11561, '150ea': 11562, 'otbox': 11563, '731': 11564, 'la1': 11565, '7ws': 11566, 'gain': 11567, 'schoolhow': 11568, 'itxxhiya': 11569, 'nextdont': 11570, 'beside': 11571, 'warmi': 11572, 'brisk': 11573, 'walks': 11574, 'dearlyok': 11575, 'invitedgr8': 11576, 'sexiest': 11577, 'dirtiest': 11578, '89070': 11579, '89070i': 11580, 'steve': 11581, 'tellmiss': 11582, 'waynever': 11583, 'morningnormally': 11584, 'dailydare': 11585, 'carpartys': 11586, 'contribute': 11587, 'greatly': 11588, 'moreurgh': 11589, 'coach': 11590, 'smells': 11591, 'duvet': 11592, 'predictive': 11593, 'wordhiya': 11594, 'meetfreemsg': 11595, 'chatim': 11596, 'w8in': 11597, '4utxt': 11598, '2rcvive': 11599, 'successwhen': 11600, 'classnever': 11601, 'url': 11602, 'onenever': 11603, '24th': 11604, 'inhaha': 11605, 'dubsackhey': 11606, 'lil': 11607, 'beverage': 11608, 'vpist': 11609, 'xgreat': 11610, 'surrender': 11611, 'allsymptoms': 11612, 'yoursun': 11613, 'exams7': 11614, 'deargettin': 11615, 'rdy': 11616, 'compi': 11617, 'eveningpiss': 11618, 'itnow': 11619, 'backwardsthink': 11620, 'doim': 11621, 'upgood': 11622, 'abstract': 11623, 'babehow': 11624, 'getcome': 11625, 'vikkyim': 11626, 'timetell': 11627, 'ishoneybee': 11628, 'gmgngegnshould': 11629, 'gbut': 11630, 'himhiya': 11631, 'africa': 11632, 'avin': 11633, 'tomorrowwanna': 11634, 'chitchat': 11635, 'logon': 11636, '8883': 11637, '4217': 11638, 'w1a': 11639, '6zf': 11640, '118pmsg': 11641, 'rcvdcha': 11642, 'quiteamuzing': 11643, 'thatscool': 11644, 'babeomg': 11645, 'ateurgent': 11646, '1000call': 11647, '09071512432': 11648, '300603tcsbcm4235wc1n3xxcallcost150ppmmobilesvary': 11649, 'herenot': 11650, 'rows': 11651, '87066had': 11652, 'recordsurgent': 11653, '18no': 11654, 'engagement': 11655, 'fixd': 11656, 'bthmm': 11657, 'njan': 11658, 'vilikkamt': 11659, 'sudnnot': 11660, 'maths': 11661, 'chapter': 11662, 'finishwow': 11663, 'chop': 11664, 'offfor': 11665, '1x150pwknoooooooo': 11666, 'fairu': 11667, '09065171142stopsms08718727870150ppmmila': 11668, 'help08718728876ill': 11669, 'firsgi': 11670, 'split': 11671, 'somethingtomorrow': 11672, 'tomorrowwell': 11673, '150right': 11674, 'yoursits': 11675, 'soonyo': 11676, 'supcarlos': 11677, 'bitfull': 11678, 'heat': 11679, 'applyed': 11680, 'paim': 11681, 'resthi': 11682, 'sumfing': 11683, 'stoptxtstophey': 11684, 'betterand': 11685, 'latethanx': 11686, 'twooh': 11687, 'kaiez': 11688, 'hiphop': 11689, 'oxygen': 11690, 'resort': 11691, 'roller': 11692, 'optionwe': 11693, 'minsy': 11694, 'thisjust': 11695, 'youwat': 11696, 'finishsorry': 11697, 'dohow': 11698, 'cleaninghows': 11699, '430sunshine': 11700, 'recorder': 11701, 'canname': 11702, 'australia': 11703, 'mquiz': 11704, 'oredigood': 11705, 'bottomnvm': 11706, 'lehsorry': 11707, 'meetingthis': 11708, 'showrreceived': 11709, 'uponthey': 11710, 'ceilingu': 11711, 'presnts': 11712, 'bcz': 11713, 'jeevithathile': 11714, 'irulinae': 11715, 'neekunna': 11716, 'prakasamanu': 11717, 'sneham': 11718, 'prakasam': 11719, 'ennal': 11720, 'mns': 11721, 'islove': 11722, 'mejus': 11723, 'blowing': 11724, 'alreadyim': 11725, 'youlol': 11726, 'storeplease': 11727, 'prizetodays': 11728, '7634': 11729, '7684': 11730, 'applyonly': 11731, 'difficulteverybody': 11732, 'yougot': 11733, 'adventure': 11734, 'bitim': 11735, 'atoh': 11736, 'firmware': 11737, 'helpthese': 11738, 'morphinehow': 11739, 'hairk': 11740, 'ahaoh': 11741, 'wordscaptain': 11742, 'vijaykanth': 11743, 'captain': 11744, 'tvhe': 11745, 'nowdo': 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'tonightat': 12781, 'tomorrowabout': 12782, 'yetpls': 12783, 'dollarshi': 12784, 'mumhas': 12785, 'beendropping': 12786, 'theplace': 12787, 'adressill': 12788, 'mewhat': 12789, 'leaveim': 12790, 'thursday': 12791, 'yay': 12792, 'nowjolly': 12793, 'xyeah': 12794, 'thinkingkkim': 12795, 'moneyi': 12796, '1here': 12797, 'oyster': 12798, 'sashimi': 12799, 'rumblingmy': 12800, 'daget': 12801, '150hahahause': 12802, 'dearjus': 12803, 'elsegood': 12804, 'kisskothi': 12805, 'marandrathabut': 12806, 'topic': 12807, 'riteok': 12808, 'correctlythanks': 12809, 'mindaah': 12810, 'armdear': 12811, 'sirsalam': 12812, 'alaikkumpride': 12813, 'shopwe': 12814, 'qatarrakhesh': 12815, 'indianpls': 12816, 'numberrespectful': 12817, 'regardsgal': 12818, 'galcan': 12819, 'boyy': 12820, 'galno': 12821, 'heaven': 12822, 'princegnwhat': 12823, 'happywishing': 12824, 'weeksweet': 12825, 'yousir': 12826, 'letterdude': 12827, 'pisces': 12828, 'aquarius': 12829, 'nowx': 12830, '2yrs': 12831, 'steyn': 12832, 'wicketneither': 12833, 'sterm': 12834, 'resolved': 12835, 'holsgarbage': 12836, 'jam': 12837, 'hannaford': 12838, 'wheat': 12839, 'chexno': 12840, 'pride': 12841, 'grownup': 12842, 'stuffwhy': 12843, 'yousounds': 12844, 'costume': 12845, 'endmy': 12846, 'gymyoud': 12847, 'jerkare': 12848, 'thereand': 12849, 'stinku': 12850, 'textcomp': 12851, 'follows': 12852, 'subsequent': 12853, 'charged150pmsg2': 12854, '84128custcare': 12855, '08712405020no': 12856, 'openings': 12857, 'upchargewhen': 12858, 'overwat': 12859, 'nakedno': 12860, '8hr': 12861, 'timedo': 12862, 'boxim': 12863, 'homeno': 12864, 'planyup': 12865, 'heegood': 12866, 'dayguai': 12867, 'joggingaiyo': 12868, 'watliving': 12869, 'okayokhmm': 12870, 'tomorrowthis': 12871, 'astrology': 12872, 'replyhey': 12873, 'ryans': 12874, 'friendyep': 12875, 'program': 12876, 'slackingim': 12877, 'officestill': 12878, 'formsdon': 12879, 'mentor': 12880, 'percent': 12881, 'surecall': 12882, '09095350301': 12883, 'erotic': 12884, 'ecstacy': 12885, 'ratecamera': 12886, 'daysa': 12887, '09066380611just': 12888, 'datesimportant': 12889, 'dept': 12890, '13404': 12891, '08717507382': 12892, 'datingi': 12893, 'coincidencelol': 12894, 'matterk': 12895, 'messagei': 12896, 'sane': 12897, 'helpingthe': 12898, 'leading': 12899, '151': 12900, 'pause': 12901, '08718726270free': 12902, 'gr8prizes': 12903, '8800': 12904, 'psp': 12905, 'wktxt': 12906, 'httpwwwgr8prizescom': 12907, '08715705022somebody': 12908, 'spacebucksits': 12909, 'weathers': 12910, 'coolyou': 12911, '02070836089i': 12912, 'squeezed': 12913, 'dadgood': 12914, 'meremove': 12915, 'maintaining': 12916, 'stuffhow': 12917, '5im': 12918, 'dreading': 12919, 'thou': 12920, 'xhow': 12921, 'suggestion': 12922, 'lands': 12923, 'helps': 12924, 'forgt': 12925, 'problemim': 12926, 'pussynot': 12927, 'fights': 12928, 'niteokok': 12929, 'plannt': 12930, 'toldwatching': 12931, 'ajith': 12932, 'ahooooooh': 12933, 'yoville': 12934, 'phoneall': 12935, 'asda': 12936, 'counts': 12937, 'doingi': 12938, 'chechidunno': 12939, 'onias': 12940, 'officer': 12941, 'ohthats': 12942, 'respectfulhi': 12943, 'bffs': 12944, 'carly': 12945, 'nowhard': 12946, 'someonethat': 12947, 'seperatedud': 12948, 'eveningbabes': 12949, 'brolly': 12950, 'franxxhi': 12951, 'welli': 12952, 'kisses': 12953, 'xxxso': 12954, 'wanpls': 12955, 'prometazine': 12956, 'syrup': 12957, '5mls': 12958, 'feedso': 12959, 'thendear': 12960, 'dearyup': 12961, 'pubthere': 12962, 'singapore': 12963, 'nitegr8': 12964, 'victoria': 12965, 'album': 12966, 'duenite': 12967, 'pocay': 12968, 'wocay': 12969, '4eva': 12970, '2morrowxxxxeast': 12971, 'coastyou': 12972, 'broth': 12973, 'ramen': 12974, 'aboutmy': 12975, 'lightnope': 12976, 'weekso': 12977, 'onim': 12978, 'fowler': 12979, 'storyi': 12980, 'monthhi': 12981, 'confirmedok': 12982, 'ksry': 12983, 'sivatats': 12984, 'askdsorry': 12985, 'meotherwise': 12986, 'natuitionoh': 12987, 'flew': 12988, 'windowsanta': 12989, '09058094583': 12990, 'gist': 12991, 'oher': 12992, '1526': 12993, 'pubcafe': 12994, 'canif': 12995, 'aroundif': 12996, 'attentionthanx': 12997, 'helpyoure': 12998, 'thatjesus': 12999, 'findim': 13000, 'tix': 13001, 'larmode': 13002, 'leftam': 13003, 'biolas': 13004, 'fnewhat': 13005, 'youdoing': 13006, 'xxxhey': 13007, 'liaothank': 13008, 'brothasby': 13009, 'worc': 13010, 'foregate': 13011, 'shrub': 13012, 'get4an18th': 13013, 'muchyou': 13014, '32000': 13015, 'legitimat': 13016, 'efreefone': 13017, 'thinkgood': 13018, 'shopthe': 13019, 'hourand': 13020, 'leasti': 13021, 'receipts': 13022, 'pendentso': 13023, 'worriesaight': 13024, 'tonightlatest': 13025, 'toilet': 13026, 'stolen': 13027, 'cops': 13028, 'onsac': 13029, 'onjust': 13030, 'hu': 13031, 'navigate': 13032, 'choosing': 13033, 'require': 13034, 'guidance': 13035, 'girlswhat': 13036, 'talks': 13037, 'addressexcept': 13038, 'chick': 13039, 'boobsim': 13040, 'nowwishing': 13041, 'revealing': 13042, 'itfor': 13043, 'sparkling': 13044, 'breaks': 13045, '45': 13046, '0121': 13047, '2025050': 13048, 'wwwshortbreaksorgukarun': 13049, 'amtsorry': 13050, 'gyno': 13051, 'belong': 13052, '08002986906ok': 13053, 'wattxt': 13054, 'stoptxtstop': 13055, 'wwwgambtvu': 13056, '2nitei': 13057, 'treasure': 13058, 'togethershall': 13059, 'anywayhttptms': 13060, 'wmlid820554ad0a1705572711firsttruec': 13061, 'notxtcoukurgent': 13062, '09050000332': 13063, '150ppmok': 13064, 'enoughyupwell': 13065, 'mewheres': 13066, 'mummys': 13067, 'positive': 13068, 'negative': 13069, 'hmmmmdhoni': 13070, 'titleso': 13071, 'winyes': 13072, 'commandwhat': 13073, 'workno': 13074, 'stressful': 13075, 'dayhave': 13076, 'holbyshall': 13077, 'foodurgent': 13078, '09064017295': 13079, 'pmso': 13080, 'li': 13081, 'lecturer': 13082, 'repeating': 13083, 'yeovil': 13084, 'motor': 13085, 'max': 13086, 'easyalso': 13087, 'rhode': 13088, 'bong': 13089, 'gtofcourse': 13090, 'songs2p': 13091, '08448350055': 13092, '2p': 13093, 'planettalkinstantcom': 13094, 'againoh': 13095, 'againu': 13096, 'latermarvel': 13097, 'ultimate': 13098, 'spiderman': 13099, 'spider': 13100, '83338': 13101, '8ball': 13102, 'wallpaperi': 13103, 'tamilnaduthen': 13104, 'peoplecool': 13105, 'tip': 13106, 'ingood': 13107, 'outsms': 13108, '9aewhy': 13109, 'gtnow': 13110, 'againlove': 13111, 'nightprivate': 13112, '07808247860': 13113, '08719899229': 13114, '40411': 13115, '061104apo': 13116, 'onlyperhaps': 13117, 'identification': 13118, 'uniwait': 13119, 'minwhat': 13120, 'megods': 13121, 'limit': 13122, 'boundaries': 13123, 'endless': 13124, 'nightmachan': 13125, 'goodnightlol': 13126, 'ini': 13127, 'centreits': 13128, 'reassuring': 13129, 'worldjust': 13130, 'leaveoh': 13131, 'young': 13132, 'dayya': 13133, 'referin': 13134, 'meis': 13135, 'liaoso': 13136, 'onwardsmiles': 13137, 'miles': 13138, 'nytby': 13139, 'itcan': 13140, 'saibaba': 13141, 'colanyno': 13142, 'anythingyou': 13143, 'chic': 13144, 'declarethats': 13145, 'yougoing': 13146, 'tomorrowyou': 13147, 'p399i': 13148, 'lately': 13149, 'drugsprivate': 13150, '49557': 13151, '261104total': 13152, 'disappointment': 13153, 'irritation': 13154, 'itwhat': 13155, 'tantrums': 13156, 'pointcompliments': 13157, 'sidehappened': 13158, 'adventuringhey': 13159, 'chief': 13160, 'numberi': 13161, 'stopaiyah': 13162, 'watwant': 13163, '20pmin': 13164, 'gsex': 13165, '2667': 13166, 'wc1n': 13167, '3xxi': 13168, 'midnightjust': 13169, 'surfingasked': 13170, '3mobile': 13171, 'chatlines': 13172, 'inclu': 13173, 'servs': 13174, 'l8er': 13175, 'bailiff': 13176, '800yeah': 13177, 'ritearmand': 13178, 'epsilonu': 13179, 'ahim': 13180, 'mouse': 13181, 'desk': 13182, 'readyhi': 13183, 'durban': 13184, 'numberic': 13185, 'childporn': 13186, 'thenhad': 13187, 'recordsno': 13188, 'vyou': 13189, 'jumpers': 13190, 'hat': 13191, 'belt': 13192, 'cribbscool': 13193, 'herewen': 13194, 'spiritual': 13195, 'greathave': 13196, 'withhahahause': 13197, 'dearwell': 13198, 'barring': 13199, 'sudden': 13200, 'influx': 13201, 'cashyeh': 13202, 'kane': 13203, 'shud': 13204, 'pshewmissing': 13205, 'muchno': 13206, 'units': 13207, 'accent': 13208, '4years': 13209, 'dental': 13210, 'nmde': 13211, 'examsorry': 13212, 'shitanything': 13213, 'lorget': 13214, 'dump': 13215, 'heap': 13216, 'lowes': 13217, 'boringok': 13218, 'salesman': 13219, 'consideringard': 13220, 'lorwhy': 13221, 'leireminder': 13222, 'postcodethis': 13223, '087187272008': 13224, 'now1': 13225, 'btnationalratewill': 13226, 'homepity': 13227, 'soany': 13228, 'suggestionsthe': 13229, 'bitching': 13230, 'freerofl': 13231}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYzlKtiGGZGh"
      },
      "source": [
        "# Next step: Data preparation for modeling\n",
        "\n",
        "Here we are dividing the mails into sequence of words with a fixed length\n",
        "of 5 words (you can choose anything based on the business problem and\n",
        "computation power). We are splitting the text by words sequences. When\n",
        "creating these sequences, we slide this window along the whole document\n",
        "one word at a time, allowing each word to learn from its preceding one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08QlDo9_CEMa",
        "outputId": "183d787e-fcb0-468a-a323-f0ddb12959d9"
      },
      "source": [
        "# prepare the dataset of input to output pairs encoded as integers\n",
        "# Generate the data for the model\n",
        "# input = the input sentence to the model with index\n",
        "# output = output of the model with index\n",
        "\n",
        "InputData = []\n",
        "OutputData = []\n",
        "\n",
        "# decide on sentence length\n",
        "# sentence_length = 25\n",
        "sentence_length = 5\n",
        "\n",
        "for i in range(0 , corpusLen - sentence_length):\n",
        " X = tokens[i : i + sentence_length]\n",
        " Y = tokens[i + sentence_length]\n",
        " InputData.append([word_index[char] for char in X])\n",
        " OutputData.append(word_index[Y])\n",
        " \n",
        "print(InputData[0])\n",
        "print (\"\\n\")\n",
        "print(OutputData[0])   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[41, 379, 4033, 863, 680]\n",
            "\n",
            "\n",
            "681\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a3lgc4965Am"
      },
      "source": [
        "# reverse the dictionary\n",
        "index_words={j:i for i,j in word_index.items()}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_YXVhh67Fmg",
        "outputId": "45f21b1d-d5c6-4a1b-c6c5-6e7df35fd3bf"
      },
      "source": [
        "print(\" \".join([index_words.get(i) for i in InputData[0]]))\n",
        "print(index_words.get(OutputData[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "go until jurong point crazy\n",
            "available\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEO6bhFiCILG",
        "outputId": "a1780332-aa5c-4f53-8422-abc276aad9b6"
      },
      "source": [
        "# Generate X\n",
        "X = np.reshape(InputData, (len(InputData), sentence_length, 1))\n",
        "print(X.shape[0], X.shape[1], X.shape[2])\n",
        "print(\"----------------------------------\")\n",
        "print(X[0])\n",
        "print(\"----------------------------------\")\n",
        "print(len(X[0]))\n",
        "print(\"----------------------------------\")\n",
        "\n",
        "# One hot encode the output variable\n",
        "Y = np_utils.to_categorical(OutputData)\n",
        "print(Y)\n",
        "print(\"----------------------------------\")\n",
        "print(len(Y[0]))\n",
        "print(\"----------------------------------\")\n",
        "print(Y.shape[0], Y.shape[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79783 5 1\n",
            "----------------------------------\n",
            "[[  41]\n",
            " [ 379]\n",
            " [4033]\n",
            " [ 863]\n",
            " [ 680]]\n",
            "----------------------------------\n",
            "5\n",
            "----------------------------------\n",
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [1. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n",
            "----------------------------------\n",
            "13232\n",
            "----------------------------------\n",
            "79783 13232\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qR9D9sFGhW3"
      },
      "source": [
        "> Next step : Model building\n",
        "    \n",
        "We will now define the LSTM model. Here we define a single hidden LSTM\n",
        "layer with 256 memory units. This model uses dropout 0.2. The output\n",
        "layer is using the softmax activation function. Here we are using the ADAM\n",
        "optimizer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUKE37BuBR_a",
        "outputId": "94ee72bc-1581-4711-bc53-0a2c80361bec"
      },
      "source": [
        "X.shape[1], X.shape[2],Y.shape[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 1, 13232)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5h62pnZrCJm9"
      },
      "source": [
        "# define the LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2])))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(Y.shape[1], activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
        "# https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/\n",
        "\n",
        "\n",
        "#define the checkpoint\n",
        "file_name_path=\"weights-improvement-{epoch:02d}-{loss:.4f}.hdf5\"\n",
        "checkpoint = ModelCheckpoint(file_name_path, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
        "# # https://machinelearningmastery.com/check-point-deep-learning-models-keras/\n",
        "\n",
        "callbacks = [checkpoint]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pth4x79wGl0k"
      },
      "source": [
        "We can now fit the model to the data. Here we use 5 epochs and a\n",
        "batch size of 128 patterns. For better results, you can use more epochs like\n",
        "50 or 100. And of course, you can use them on more data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCSkPdJHCLZd",
        "outputId": "7840f9b7-598a-4a5d-e428-6ab3444d0a0f"
      },
      "source": [
        "\n",
        "#fit the model\n",
        "model.fit(X, Y, epochs=5, batch_size=128, callbacks=callbacks)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "624/624 [==============================] - 82s 128ms/step - loss: 7.6504\n",
            "\n",
            "Epoch 00001: loss improved from inf to 7.41367, saving model to weights-improvement-01-7.4137.hdf5\n",
            "Epoch 2/5\n",
            "624/624 [==============================] - 78s 124ms/step - loss: 7.0576\n",
            "\n",
            "Epoch 00002: loss improved from 7.41367 to 7.06721, saving model to weights-improvement-02-7.0672.hdf5\n",
            "Epoch 3/5\n",
            "282/624 [============>.................] - ETA: 42s - loss: 6.8832"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HElaG8M5GpcB"
      },
      "source": [
        "> Note We have not split the data into training and testing data.\n",
        "We are not interested in the accurate model. As we all know, deep\n",
        "learning models will require a lot of data for training and take a lot\n",
        "of time to train, so we are using a model checkpoint to capture all of\n",
        "the model weights to file. We will use the best set of weights for our\n",
        "prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4HH7w-yGr9M"
      },
      "source": [
        "After running the above code, you will have weight checkpoint files\n",
        "in your local directory. Pick the network weights file that is saved in your working directory. For example, when we ran this example, below was the checkpoint with the smallest loss that we achieved with 5 epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEe4-AGnCNEx"
      },
      "source": [
        "# load the network weights\n",
        "file_name = \"/content/weights-improvement-05-6.6146.hdf5\"\n",
        "model.load_weights(file_name)\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDoXdlMEGvpL"
      },
      "source": [
        "> Last step: Predicting next word\n",
        "\n",
        "We will randomly generate a sequence of words and input to the model\n",
        "and see what it predicts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRP8h8Y2EH4q"
      },
      "source": [
        "# Generating random sequence\n",
        "start = numpy.random.randint(0, len(InputData))  # producing a random no. between 0 and 79788\n",
        "input_sent = InputData[start]  # getting the sentence or bag of words (of len 25 words) \n",
        "\n",
        "# Generate index of the next word of the email\n",
        "X = numpy.reshape(input_sent, (1, len(input_sent), 1))\n",
        "predict_word = model.predict(X, verbose=0)\n",
        "print(predict_word)  # holds the probabilities of all the words\n",
        "\n",
        "print(len(predict_word[0])) # shows that the predict word list has probabilities of 13344 words\n",
        "\n",
        "index = numpy.argmax(predict_word)  # find the index of the highest probability word\n",
        "# Must read : https://stackoverflow.com/questions/28697993/numpy-what-is-the-logic-of-the-argmin-and-argmax-functions\n",
        "\n",
        "print(input_sent)\n",
        "print (\"\\n\")\n",
        "print(index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2t0kfbWFfbT"
      },
      "source": [
        "# Convert these indexes back to words\n",
        "for key , value in word_index.items():\n",
        "  if value == index:\n",
        "    ans=key\n",
        "    break\n",
        "\n",
        "sentence=[]\n",
        "\n",
        "for i in input_sent:\n",
        "  for key, value in word_index.items():\n",
        "    if value==i:\n",
        "      sentence.append(key)\n",
        "    \n",
        "\n",
        "for i in sentence:\n",
        "  print(i, end =' ')\n",
        "\n",
        "print (\"\\n\")\n",
        "\n",
        "print(ans)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkpVpa85FhYH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}