Text_Summariser_v5.ipynb

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  "metadata": {
    "colab": {
      "name": "Text Summariser v5.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
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      "cell_type": "code",
      "metadata": {
        "id": "A5v3IIfHigR2",
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      "source": [
        "#importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#importing the natural language toolkit and downloading the important libraries\n",
        "import nltk\n",
        "nltk.download('popular')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading collection 'popular'\n",
            "[nltk_data]    | \n",
            "[nltk_data]    | Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]    |   Package cmudict is already up-to-date!\n",
            "[nltk_data]    | Downloading package gazetteers to /root/nltk_data...\n",
            "[nltk_data]    |   Package gazetteers is already up-to-date!\n",
            "[nltk_data]    | Downloading package genesis to /root/nltk_data...\n",
            "[nltk_data]    |   Package genesis is already up-to-date!\n",
            "[nltk_data]    | Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]    |   Package gutenberg is already up-to-date!\n",
            "[nltk_data]    | Downloading package inaugural to /root/nltk_data...\n",
            "[nltk_data]    |   Package inaugural is already up-to-date!\n",
            "[nltk_data]    | Downloading package movie_reviews to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package movie_reviews is already up-to-date!\n",
            "[nltk_data]    | Downloading package names to /root/nltk_data...\n",
            "[nltk_data]    |   Package names is already up-to-date!\n",
            "[nltk_data]    | Downloading package shakespeare to /root/nltk_data...\n",
            "[nltk_data]    |   Package shakespeare is already up-to-date!\n",
            "[nltk_data]    | Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]    |   Package stopwords is already up-to-date!\n",
            "[nltk_data]    | Downloading package treebank to /root/nltk_data...\n",
            "[nltk_data]    |   Package treebank is already up-to-date!\n",
            "[nltk_data]    | Downloading package twitter_samples to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package twitter_samples is already up-to-date!\n",
            "[nltk_data]    | Downloading package omw to /root/nltk_data...\n",
            "[nltk_data]    |   Package omw is already up-to-date!\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    |   Package wordnet is already up-to-date!\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Package wordnet_ic is already up-to-date!\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Package words is already up-to-date!\n",
            "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package maxent_ne_chunker is already up-to-date!\n",
            "[nltk_data]    | Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]    |   Package punkt is already up-to-date!\n",
            "[nltk_data]    | Downloading package snowball_data to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package snowball_data is already up-to-date!\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package averaged_perceptron_tagger is already up-\n",
            "[nltk_data]    |       to-date!\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection popular\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHquqwDjipTl",
        "colab_type": "code",
        "outputId": "3e71dfec-0d41-4e33-d714-7a05b3563a87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#installing docx2text library\n",
        "pip install docx2txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: docx2txt in /usr/local/lib/python3.6/dist-packages (0.8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oeu66PgIjQYN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing docx2text and changing the docx file imported to text \n",
        "import docx2txt\n",
        "k = docx2txt.process(\"text.docx\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDLMnKZqjV_l",
        "colab_type": "code",
        "outputId": "854bd8fe-78b2-4b76-acfb-e8baa293cddd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#finding the number of characters in the text saved as in the name k\n",
        "len(k)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23172"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
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    },
    {
      "cell_type": "code",
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        "id": "9QBtMoUUjYtU",
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      },
      "source": [
        "#tokenization basically refers to splitting up a larger body of text into smaller lines, words or even creating words for a non-English language.\n",
        "#importing sent_tokenize function from nltk and sentence tokenizing the text \n",
        "from nltk.tokenize import sent_tokenize\n",
        "corpus=sent_tokenize(k)\n",
        "cor=corpus.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kipUJB26jdi8",
        "colab_type": "code",
        "outputId": "6f1f3e69-8c0c-4318-c292-60c3fa8edd9f",
        "colab": {
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        }
      },
      "source": [
        "#finding the number of sentences\n",
        "len(cor)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "131"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "yJ1HJFNAjhbE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing the re library and cleaning the text of any punctuation marks, numbers and eddy spaces\n",
        "import re\n",
        "corpuss=[]\n",
        "for k in corpus:\n",
        "  text = re.sub('[^a-zA-Z]', ' ', k)#allows just a-z and A-Z and replaces all other characters with space\n",
        "  text = re.sub(r'\\s+', ' ', text)#replaces multiple space sequences with a single space\n",
        "  corpuss.append(text)\n",
        "corpus=corpuss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77YWqMGNjp-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#changing all the alphabetic characters to lower space\n",
        "import string\n",
        "corpuss=[s.lower() for s in corpus]\n",
        "corpus=corpuss.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8dJlIf_juHF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing all the stopwords in the english language from the nltk package\n",
        "from nltk.corpus import stopwords\n",
        "stop_words=stopwords.words('english')\n",
        "def remove_stopwords(sen):                 ## Here we are removing all the stopwords form the sentence\n",
        "    sen_new = \" \".join([i for i in sen if i not in stop_words])\n",
        "    return sen_new\n",
        "cleaned = [remove_stopwords(r.split()) for r in corpuss]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiV3xydVEVqx",
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      },
      "source": [
        "len(stop_words)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "179"
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          "execution_count": 55
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    {
      "cell_type": "code",
      "metadata": {
        "id": "rX8x8ilujxnN",
        "colab_type": "code",
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      },
      "source": [
        "#here we are defining a method to word tokenize\n",
        "def Tokenization(corpus):        \n",
        "    y=[]\n",
        "    for i in range(0,len(corpus)):\n",
        "      a=corpus[i].split(\" \")\n",
        "      y.append(a)\n",
        "    return y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3TSN3dTkArV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#defining a function to renove stop words and calling the Tokenization method to tokenize\n",
        "def remove_stop_words(corpus):\n",
        " \n",
        "    stop_words = nltk.corpus.stopwords.words('english')\n",
        "  \n",
        "    corpus_wo_stopwords=[]\n",
        "    a=Tokenization(corpus)\n",
        "    for i in a:\n",
        "      x=[]\n",
        "      for j in i:\n",
        "        if j not in stop_words:\n",
        "          x=x+[j]\n",
        "      corpus_wo_stopwords+=[x]\n",
        "        \n",
        "    return corpus_wo_stopwords\n",
        "#word tokenizing the raw text and removing stopwords from a copy of the raw text that we have kept untouched without doing sentence tokenization because these words will be used to find their word vectors    \n",
        "PP_corpus = remove_stop_words(corpus)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvbFVWofkIOd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#getting the unique words from the word tokenized list\n",
        "def get_unique_words(PP_corpus):\n",
        "    unique_words=set()\n",
        "    for i in PP_corpus:\n",
        "      for j in i:\n",
        "          unique_words.add(j)\n",
        "    return unique_words\n",
        "  \n",
        "unique_words = get_unique_words(PP_corpus)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aW-w5db0kQTW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#mapping each unique words in a dictionry against an integer\n",
        "def mapping(unique_words):\n",
        "    y1=unique_words\n",
        "    ind=list(range(len(y1)))\n",
        "    d = {k:v for k,v in zip(y1,ind)}\n",
        "    return d\n",
        "    \n",
        "word2int = mapping(unique_words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_7kQVtrkcO1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#getting the data using skip gram model.Skip gram model here is giving us the output as a list of tuples where each tuple has two elements. The first element is the centre word and the second element is the context word. It depends on thewindow length we have provided. \n",
        "import pandas as pd\n",
        "\n",
        "def data_gen(PP_corpus, window_size):\n",
        "   \n",
        "    w=window_size\n",
        "    y=PP_corpus\n",
        "    li=[]\n",
        "    k=0\n",
        "    for i in range(len(y)):\n",
        "      for j in range(len(y[i])):\n",
        "        for h in range(-w,w+1):\n",
        "          if(j+h>=0 and j+h<len(y[i])):\n",
        "            if(not(j==j+h)):\n",
        "              li+=[(y[i][j],y[i][j+h])]\n",
        "    data = li   \n",
        "\n",
        "    return data\n",
        "\n",
        "    \n",
        "data = data_gen(PP_corpus, 2)\n",
        "df = pd.DataFrame(data, columns = ['input', 'label'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "colab": {
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      "source": [
        "data"
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      "execution_count": 0,
      "outputs": [
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          "output_type": "execute_result",
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              " ('year', 'city'),\n",
              " ('year', 'study'),\n",
              " ('year', 'panel'),\n",
              " ('study', 'city'),\n",
              " ('study', 'year'),\n",
              " ('study', 'panel'),\n",
              " ('study', 'comprising'),\n",
              " ('panel', 'year'),\n",
              " ('panel', 'study'),\n",
              " ('panel', 'comprising'),\n",
              " ('panel', 'experts'),\n",
              " ('comprising', 'study'),\n",
              " ('comprising', 'panel'),\n",
              " ('comprising', 'experts'),\n",
              " ('comprising', 'ai'),\n",
              " ('experts', 'panel'),\n",
              " ('experts', 'comprising'),\n",
              " ('experts', 'ai'),\n",
              " ('experts', 'relevant'),\n",
              " ('ai', 'comprising'),\n",
              " ('ai', 'experts'),\n",
              " ('ai', 'relevant'),\n",
              " ('ai', 'areas'),\n",
              " ('relevant', 'experts'),\n",
              " ('relevant', 'ai'),\n",
              " ('relevant', 'areas'),\n",
              " ('relevant', 'focused'),\n",
              " ('areas', 'ai'),\n",
              " ('areas', 'relevant'),\n",
              " ('areas', 'focused'),\n",
              " ('areas', 'attention'),\n",
              " ('focused', 'relevant'),\n",
              " ('focused', 'areas'),\n",
              " ('focused', 'attention'),\n",
              " ('focused', 'eight'),\n",
              " ('attention', 'areas'),\n",
              " ('attention', 'focused'),\n",
              " ('attention', 'eight'),\n",
              " ('attention', 'domains'),\n",
              " ('eight', 'focused'),\n",
              " ('eight', 'attention'),\n",
              " ('eight', 'domains'),\n",
              " ('eight', 'considered'),\n",
              " ('domains', 'attention'),\n",
              " ('domains', 'eight'),\n",
              " ('domains', 'considered'),\n",
              " ('domains', 'salient'),\n",
              " ('considered', 'eight'),\n",
              " ('considered', 'domains'),\n",
              " ('considered', 'salient'),\n",
              " ('considered', 'transportation'),\n",
              " ('salient', 'domains'),\n",
              " ('salient', 'considered'),\n",
              " ('salient', 'transportation'),\n",
              " ('salient', 'service'),\n",
              " ('transportation', 'considered'),\n",
              " ('transportation', 'salient'),\n",
              " ('transportation', 'service'),\n",
              " ('transportation', 'robots'),\n",
              " ('service', 'salient'),\n",
              " ('service', 'transportation'),\n",
              " ('service', 'robots'),\n",
              " ('service', 'healthcare'),\n",
              " ('robots', 'transportation'),\n",
              " ('robots', 'service'),\n",
              " ('robots', 'healthcare'),\n",
              " ('robots', 'education'),\n",
              " ('healthcare', 'service'),\n",
              " ('healthcare', 'robots'),\n",
              " ('healthcare', 'education'),\n",
              " ('healthcare', 'low'),\n",
              " ('education', 'robots'),\n",
              " ('education', 'healthcare'),\n",
              " ('education', 'low'),\n",
              " ('education', 'resource'),\n",
              " ('low', 'healthcare'),\n",
              " ('low', 'education'),\n",
              " ('low', 'resource'),\n",
              " ('low', 'communities'),\n",
              " ('resource', 'education'),\n",
              " ('resource', 'low'),\n",
              " ('resource', 'communities'),\n",
              " ('resource', 'public'),\n",
              " ('communities', 'low'),\n",
              " ('communities', 'resource'),\n",
              " ('communities', 'public'),\n",
              " ('communities', 'safety'),\n",
              " ('public', 'resource'),\n",
              " ('public', 'communities'),\n",
              " ('public', 'safety'),\n",
              " ('public', 'security'),\n",
              " ('safety', 'communities'),\n",
              " ('safety', 'public'),\n",
              " ('safety', 'security'),\n",
              " ('safety', 'employment'),\n",
              " ('security', 'public'),\n",
              " ('security', 'safety'),\n",
              " ('security', 'employment'),\n",
              " ('security', 'workplace'),\n",
              " ('employment', 'safety'),\n",
              " ('employment', 'security'),\n",
              " ('employment', 'workplace'),\n",
              " ('employment', 'entertainment'),\n",
              " ('workplace', 'security'),\n",
              " ('workplace', 'employment'),\n",
              " ('workplace', 'entertainment'),\n",
              " ('workplace', ''),\n",
              " ('entertainment', 'employment'),\n",
              " ('entertainment', 'workplace'),\n",
              " ('entertainment', ''),\n",
              " ('', 'workplace'),\n",
              " ('', 'entertainment'),\n",
              " ('domains', 'report'),\n",
              " ('domains', 'reflects'),\n",
              " ('report', 'domains'),\n",
              " ('report', 'reflects'),\n",
              " ('report', 'progress'),\n",
              " ('reflects', 'domains'),\n",
              " ('reflects', 'report'),\n",
              " ('reflects', 'progress'),\n",
              " ('reflects', 'past'),\n",
              " ('progress', 'report'),\n",
              " ('progress', 'reflects'),\n",
              " ('progress', 'past'),\n",
              " ('progress', 'fifteen'),\n",
              " ('past', 'reflects'),\n",
              " ('past', 'progress'),\n",
              " ('past', 'fifteen'),\n",
              " ('past', 'years'),\n",
              " ('fifteen', 'progress'),\n",
              " ('fifteen', 'past'),\n",
              " ('fifteen', 'years'),\n",
              " ('fifteen', 'anticipates'),\n",
              " ('years', 'past'),\n",
              " ('years', 'fifteen'),\n",
              " ('years', 'anticipates'),\n",
              " ('years', 'developments'),\n",
              " ('anticipates', 'fifteen'),\n",
              " ('anticipates', 'years'),\n",
              " ('anticipates', 'developments'),\n",
              " ('anticipates', 'coming'),\n",
              " ('developments', 'years'),\n",
              " ('developments', 'anticipates'),\n",
              " ('developments', 'coming'),\n",
              " ('developments', 'fifteen'),\n",
              " ('coming', 'anticipates'),\n",
              " ('coming', 'developments'),\n",
              " ('coming', 'fifteen'),\n",
              " ('coming', 'years'),\n",
              " ('fifteen', 'developments'),\n",
              " ('fifteen', 'coming'),\n",
              " ('fifteen', 'years'),\n",
              " ('fifteen', ''),\n",
              " ('years', 'coming'),\n",
              " ('years', 'fifteen'),\n",
              " ('years', ''),\n",
              " ('', 'fifteen'),\n",
              " ('', 'years'),\n",
              " ('though', 'drawing'),\n",
              " ('though', 'common'),\n",
              " ('drawing', 'though'),\n",
              " ('drawing', 'common'),\n",
              " ('drawing', 'source'),\n",
              " ('common', 'though'),\n",
              " ('common', 'drawing'),\n",
              " ('common', 'source'),\n",
              " ('common', 'research'),\n",
              " ('source', 'drawing'),\n",
              " ('source', 'common'),\n",
              " ('source', 'research'),\n",
              " ('source', 'domain'),\n",
              " ('research', 'common'),\n",
              " ('research', 'source'),\n",
              " ('research', 'domain'),\n",
              " ('research', 'reflects'),\n",
              " ('domain', 'source'),\n",
              " ('domain', 'research'),\n",
              " ('domain', 'reflects'),\n",
              " ('domain', 'different'),\n",
              " ('reflects', 'research'),\n",
              " ('reflects', 'domain'),\n",
              " ('reflects', 'different'),\n",
              " ('reflects', 'ai'),\n",
              " ('different', 'domain'),\n",
              " ('different', 'reflects'),\n",
              " ('different', 'ai'),\n",
              " ('different', 'influences'),\n",
              " ('ai', 'reflects'),\n",
              " ('ai', 'different'),\n",
              " ('ai', 'influences'),\n",
              " ('ai', 'challenges'),\n",
              " ('influences', 'different'),\n",
              " ('influences', 'ai'),\n",
              " ('influences', 'challenges'),\n",
              " ('influences', 'difficulty'),\n",
              " ('challenges', 'ai'),\n",
              " ('challenges', 'influences'),\n",
              " ('challenges', 'difficulty'),\n",
              " ('challenges', 'creating'),\n",
              " ('difficulty', 'influences'),\n",
              " ('difficulty', 'challenges'),\n",
              " ('difficulty', 'creating'),\n",
              " ('difficulty', 'safe'),\n",
              " ('creating', 'challenges'),\n",
              " ('creating', 'difficulty'),\n",
              " ('creating', 'safe'),\n",
              " ('creating', 'reliable'),\n",
              " ('safe', 'difficulty'),\n",
              " ('safe', 'creating'),\n",
              " ('safe', 'reliable'),\n",
              " ('safe', 'hardware'),\n",
              " ('reliable', 'creating'),\n",
              " ('reliable', 'safe'),\n",
              " ('reliable', 'hardware'),\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xyjvzky-klnV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#defining a method for one-hot encoding\n",
        "def to_one_hot_encoding(data_point_index):\n",
        "   \n",
        "    one_hot_encoding=np.zeros(len(unique_words))\n",
        "    one_hot_encoding[data_point_index]=1\n",
        "    return one_hot_encoding"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fu2dWIlzkubN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#storing one hot encoded values of the data generated in X_train and Y_train where the index of the 1 is the value of the word in the dictionary\n",
        "def one_hot_for_skip_gram(word2int, data):\n",
        "    samples=len(data)\n",
        "    vocab=len(word2int.keys())\n",
        "    X=[]\n",
        "    Y=[]\n",
        "    for i in range(0,samples):\n",
        "      X+=[to_one_hot_encoding(word2int[data[i][0]])]\n",
        "      Y+=[to_one_hot_encoding(word2int[data[i][1]])]\n",
        "    X=np.array(X)\n",
        "    Y=np.array(Y)\n",
        "    return X, Y\n",
        "    \n",
        "X_train,Y_train = one_hot_for_skip_gram(word2int, data)    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZpCGPtfk1El",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#importing  keras and all the required components for the model \n",
        "import keras\n",
        "\n",
        "from keras.layers import Input,Dense\n",
        "from keras.models import Model, Sequential\n",
        "from keras import optimizers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiDjQl6mk9bV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#creating the model as we will find the word vectors of each word from the model. We define the model as such as that the length of the word vectors is the number of neurons in the last hidden layer.\n",
        "def create_model():\n",
        "    input_layer=Input(shape=(len(unique_words),))\n",
        "    hidden_layer=Dense(11)(input_layer)\n",
        "    output_layer=Dense(len(unique_words),activation='softmax')(hidden_layer)\n",
        "    model=Model(input=[input_layer],output=[output_layer])\n",
        "    model.compile(optimizer='Adam',loss='categorical_crossentropy')\n",
        "    model.summary()\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePREGkj3lCa8",
        "colab_type": "code",
        "outputId": "d900218b-d515-4b3f-a07a-1ef8a92d52ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "#creating the model and naming it as 'model'\n",
        "model=create_model()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 1031)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 11)                11352     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1031)              12372     \n",
            "=================================================================\n",
            "Total params: 23,724\n",
            "Trainable params: 23,724\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=[<tf.Tenso..., outputs=[<tf.Tenso...)`\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9Zzl5A_lHnU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#keeping a copy of the model\n",
        "import copy\n",
        "model_before = copy.copy(model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NYwVcDElPUN",
        "colab_type": "code",
        "outputId": "8d84310d-6c50-4db7-dd25-a7ad53b87d2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Model fitting is creating that simplified representation in a way that can generally be used successfully given new data.\n",
        "model.fit(X_train, Y_train, epochs = 1000, batch_size = 200)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "8418/8418 [==============================] - 1s 98us/step - loss: 6.9311\n",
            "Epoch 2/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 6.9050\n",
            "Epoch 3/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.8545\n",
            "Epoch 4/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 6.7717\n",
            "Epoch 5/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.6650\n",
            "Epoch 6/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.5564\n",
            "Epoch 7/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.4703\n",
            "Epoch 8/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 6.4172\n",
            "Epoch 9/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.3915\n",
            "Epoch 10/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.3780\n",
            "Epoch 11/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 6.3693\n",
            "Epoch 12/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.3614\n",
            "Epoch 13/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 6.3541\n",
            "Epoch 14/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 6.3470\n",
            "Epoch 15/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.3401\n",
            "Epoch 16/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 6.3330\n",
            "Epoch 17/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 6.3258\n",
            "Epoch 18/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 6.3181\n",
            "Epoch 19/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 6.3101\n",
            "Epoch 20/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.3020\n",
            "Epoch 21/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.2935\n",
            "Epoch 22/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.2841\n",
            "Epoch 23/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.2742\n",
            "Epoch 24/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.2637\n",
            "Epoch 25/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 6.2524\n",
            "Epoch 26/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.2408\n",
            "Epoch 27/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 6.2278\n",
            "Epoch 28/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 6.2146\n",
            "Epoch 29/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.2002\n",
            "Epoch 30/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 6.1849\n",
            "Epoch 31/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 6.1686\n",
            "Epoch 32/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 6.1513\n",
            "Epoch 33/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 6.1331\n",
            "Epoch 34/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.1137\n",
            "Epoch 35/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 6.0932\n",
            "Epoch 36/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.0719\n",
            "Epoch 37/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.0491\n",
            "Epoch 38/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 6.0255\n",
            "Epoch 39/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 6.0012\n",
            "Epoch 40/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 5.9754\n",
            "Epoch 41/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 5.9489\n",
            "Epoch 42/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 5.9212\n",
            "Epoch 43/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 5.8927\n",
            "Epoch 44/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 5.8634\n",
            "Epoch 45/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 5.8334\n",
            "Epoch 46/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 5.8026\n",
            "Epoch 47/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 5.7714\n",
            "Epoch 48/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 5.7394\n",
            "Epoch 49/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 5.7074\n",
            "Epoch 50/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 5.6743\n",
            "Epoch 51/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 5.6413\n",
            "Epoch 52/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.6082\n",
            "Epoch 53/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.5746\n",
            "Epoch 54/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 5.5415\n",
            "Epoch 55/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.5083\n",
            "Epoch 56/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.4752\n",
            "Epoch 57/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.4423\n",
            "Epoch 58/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 5.4095\n",
            "Epoch 59/1000\n",
            "8418/8418 [==============================] - 1s 77us/step - loss: 5.3772\n",
            "Epoch 60/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.3455\n",
            "Epoch 61/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 5.3140\n",
            "Epoch 62/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.2828\n",
            "Epoch 63/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 5.2520\n",
            "Epoch 64/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 5.2216\n",
            "Epoch 65/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 5.1916\n",
            "Epoch 66/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 5.1616\n",
            "Epoch 67/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 5.1325\n",
            "Epoch 68/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 5.1035\n",
            "Epoch 69/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 5.0749\n",
            "Epoch 70/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 5.0468\n",
            "Epoch 71/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 5.0193\n",
            "Epoch 72/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 4.9920\n",
            "Epoch 73/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 4.9651\n",
            "Epoch 74/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 4.9385\n",
            "Epoch 75/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 4.9128\n",
            "Epoch 76/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 4.8871\n",
            "Epoch 77/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 4.8621\n",
            "Epoch 78/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 4.8372\n",
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            "Epoch 867/1000\n",
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            "Epoch 868/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9328\n",
            "Epoch 869/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9329\n",
            "Epoch 870/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 2.9326\n",
            "Epoch 871/1000\n",
            "8418/8418 [==============================] - 1s 79us/step - loss: 2.9328\n",
            "Epoch 872/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9322\n",
            "Epoch 873/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9319\n",
            "Epoch 874/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9312\n",
            "Epoch 875/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9309\n",
            "Epoch 876/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9306\n",
            "Epoch 877/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9309\n",
            "Epoch 878/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9301\n",
            "Epoch 879/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9301\n",
            "Epoch 880/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9296\n",
            "Epoch 881/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9296\n",
            "Epoch 882/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9292\n",
            "Epoch 883/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9289\n",
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            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9281\n",
            "Epoch 886/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 2.9280\n",
            "Epoch 887/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9278\n",
            "Epoch 888/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 2.9274\n",
            "Epoch 889/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9272\n",
            "Epoch 890/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9263\n",
            "Epoch 891/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9266\n",
            "Epoch 892/1000\n",
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            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9256\n",
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            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9244\n",
            "Epoch 899/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9237\n",
            "Epoch 900/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 2.9241\n",
            "Epoch 901/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9234\n",
            "Epoch 902/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 2.9232\n",
            "Epoch 903/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9231\n",
            "Epoch 904/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9228\n",
            "Epoch 905/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9221\n",
            "Epoch 906/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9223\n",
            "Epoch 907/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9221\n",
            "Epoch 908/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9217\n",
            "Epoch 909/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9214\n",
            "Epoch 910/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9209\n",
            "Epoch 911/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9207\n",
            "Epoch 912/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9208\n",
            "Epoch 913/1000\n",
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            "Epoch 914/1000\n",
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            "Epoch 915/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9198\n",
            "Epoch 916/1000\n",
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            "Epoch 917/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.9192\n",
            "Epoch 918/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 2.9191\n",
            "Epoch 919/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 2.9179\n",
            "Epoch 920/1000\n",
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            "Epoch 921/1000\n",
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            "Epoch 922/1000\n",
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            "Epoch 923/1000\n",
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            "Epoch 924/1000\n",
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            "Epoch 925/1000\n",
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            "Epoch 926/1000\n",
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            "Epoch 927/1000\n",
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            "Epoch 928/1000\n",
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            "Epoch 929/1000\n",
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            "Epoch 930/1000\n",
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            "Epoch 931/1000\n",
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            "Epoch 932/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9149\n",
            "Epoch 933/1000\n",
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            "Epoch 934/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 2.9144\n",
            "Epoch 935/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 2.9140\n",
            "Epoch 936/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9138\n",
            "Epoch 937/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9134\n",
            "Epoch 938/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 2.9131\n",
            "Epoch 939/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 2.9134\n",
            "Epoch 940/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 2.9129\n",
            "Epoch 941/1000\n",
            "8418/8418 [==============================] - 1s 77us/step - loss: 2.9128\n",
            "Epoch 942/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 2.9120\n",
            "Epoch 943/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9120\n",
            "Epoch 944/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9118\n",
            "Epoch 945/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9115\n",
            "Epoch 946/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9111\n",
            "Epoch 947/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9110\n",
            "Epoch 948/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9110\n",
            "Epoch 949/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9106\n",
            "Epoch 950/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9102\n",
            "Epoch 951/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 2.9100\n",
            "Epoch 952/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9096\n",
            "Epoch 953/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9096\n",
            "Epoch 954/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9088\n",
            "Epoch 955/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9088\n",
            "Epoch 956/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9085\n",
            "Epoch 957/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9085\n",
            "Epoch 958/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9078\n",
            "Epoch 959/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9076\n",
            "Epoch 960/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.9075\n",
            "Epoch 961/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.9073\n",
            "Epoch 962/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9070\n",
            "Epoch 963/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9067\n",
            "Epoch 964/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9067\n",
            "Epoch 965/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9064\n",
            "Epoch 966/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9057\n",
            "Epoch 967/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9054\n",
            "Epoch 968/1000\n",
            "8418/8418 [==============================] - 1s 75us/step - loss: 2.9053\n",
            "Epoch 969/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9050\n",
            "Epoch 970/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 2.9047\n",
            "Epoch 971/1000\n",
            "8418/8418 [==============================] - 1s 74us/step - loss: 2.9042\n",
            "Epoch 972/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9041\n",
            "Epoch 973/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 2.9042\n",
            "Epoch 974/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9039\n",
            "Epoch 975/1000\n",
            "8418/8418 [==============================] - 1s 76us/step - loss: 2.9037\n",
            "Epoch 976/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9033\n",
            "Epoch 977/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9033\n",
            "Epoch 978/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.9030\n",
            "Epoch 979/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.9023\n",
            "Epoch 980/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.9025\n",
            "Epoch 981/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.9024\n",
            "Epoch 982/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.9019\n",
            "Epoch 983/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 2.9014\n",
            "Epoch 984/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 2.9012\n",
            "Epoch 985/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.9009\n",
            "Epoch 986/1000\n",
            "8418/8418 [==============================] - 1s 67us/step - loss: 2.9008\n",
            "Epoch 987/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.9004\n",
            "Epoch 988/1000\n",
            "8418/8418 [==============================] - 1s 68us/step - loss: 2.9008\n",
            "Epoch 989/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.9003\n",
            "Epoch 990/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.9001\n",
            "Epoch 991/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.8998\n",
            "Epoch 992/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.8995\n",
            "Epoch 993/1000\n",
            "8418/8418 [==============================] - 1s 72us/step - loss: 2.8994\n",
            "Epoch 994/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.8991\n",
            "Epoch 995/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.8987\n",
            "Epoch 996/1000\n",
            "8418/8418 [==============================] - 1s 70us/step - loss: 2.8983\n",
            "Epoch 997/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.8977\n",
            "Epoch 998/1000\n",
            "8418/8418 [==============================] - 1s 69us/step - loss: 2.8975\n",
            "Epoch 999/1000\n",
            "8418/8418 [==============================] - 1s 71us/step - loss: 2.8972\n",
            "Epoch 1000/1000\n",
            "8418/8418 [==============================] - 1s 73us/step - loss: 2.8973\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f624dde3b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HzFSVmGulaxK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#defining a method to get the enbeddings which is basically the list of the weights of connections of each neuron of the word output from the last hidden layer.\n",
        "def get_embeddings(model, flag):\n",
        "    embeddings=model.layers[flag+1].get_weights()[0]\n",
        "    embeddings.reshape((len(unique_words),11))\n",
        "    return embeddings"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bL3fqG1hnsax",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#gettig the embeddings by calling the method\n",
        "embeddingst = get_embeddings(model, 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2sXTpGjnx39",
        "colab_type": "code",
        "outputId": "c415f29c-c12d-41a4-b008-1399fae8a40c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#saving the word embeddings and visualising it as a table\n",
        "w2v_df = pd.DataFrame(embeddingst, columns = ['X1','X2','X3','X4','X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11'] )\n",
        "w2v_df['word'] = unique_words\n",
        "w2v_df = w2v_df[['word', 'X1', 'X2','X3','X4','X5', 'X6', 'X7', 'X8', 'X9', 'X10', 'X11']]\n",
        "w2v_df"
      ],
      "execution_count": 0,
      "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>word</th>\n",
              "      <th>X1</th>\n",
              "      <th>X2</th>\n",
              "      <th>X3</th>\n",
              "      <th>X4</th>\n",
              "      <th>X5</th>\n",
              "      <th>X6</th>\n",
              "      <th>X7</th>\n",
              "      <th>X8</th>\n",
              "      <th>X9</th>\n",
              "      <th>X10</th>\n",
              "      <th>X11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>-0.030510</td>\n",
              "      <td>0.794130</td>\n",
              "      <td>-1.061649</td>\n",
              "      <td>-0.083442</td>\n",
              "      <td>-0.130550</td>\n",
              "      <td>-0.352269</td>\n",
              "      <td>0.181860</td>\n",
              "      <td>-0.089650</td>\n",
              "      <td>0.719598</td>\n",
              "      <td>0.729324</td>\n",
              "      <td>0.032105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>high</td>\n",
              "      <td>-2.015454</td>\n",
              "      <td>0.884868</td>\n",
              "      <td>4.533864</td>\n",
              "      <td>0.341083</td>\n",
              "      <td>1.308593</td>\n",
              "      <td>0.774415</td>\n",
              "      <td>0.596743</td>\n",
              "      <td>-5.877708</td>\n",
              "      <td>-0.906213</td>\n",
              "      <td>1.543818</td>\n",
              "      <td>0.609791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>protect</td>\n",
              "      <td>-0.487714</td>\n",
              "      <td>2.816028</td>\n",
              "      <td>-0.131617</td>\n",
              "      <td>-0.185775</td>\n",
              "      <td>-1.269962</td>\n",
              "      <td>0.442647</td>\n",
              "      <td>-1.234280</td>\n",
              "      <td>4.166347</td>\n",
              "      <td>1.736702</td>\n",
              "      <td>0.618867</td>\n",
              "      <td>0.986666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>systems</td>\n",
              "      <td>0.292698</td>\n",
              "      <td>0.728003</td>\n",
              "      <td>1.879227</td>\n",
              "      <td>-0.000530</td>\n",
              "      <td>0.182456</td>\n",
              "      <td>-0.554824</td>\n",
              "      <td>0.675264</td>\n",
              "      <td>-0.770940</td>\n",
              "      <td>0.881297</td>\n",
              "      <td>0.236392</td>\n",
              "      <td>0.138573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>closely</td>\n",
              "      <td>1.435934</td>\n",
              "      <td>0.594579</td>\n",
              "      <td>2.651418</td>\n",
              "      <td>-0.135553</td>\n",
              "      <td>-1.534516</td>\n",
              "      <td>0.055792</td>\n",
              "      <td>2.358037</td>\n",
              "      <td>0.954199</td>\n",
              "      <td>3.507608</td>\n",
              "      <td>0.667975</td>\n",
              "      <td>0.923649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fewer</td>\n",
              "      <td>-0.452418</td>\n",
              "      <td>1.659052</td>\n",
              "      <td>2.633829</td>\n",
              "      <td>-0.745144</td>\n",
              "      <td>3.918916</td>\n",
              "      <td>-2.328767</td>\n",
              "      <td>-1.142608</td>\n",
              "      <td>1.593076</td>\n",
              "      <td>-2.217718</td>\n",
              "      <td>0.309800</td>\n",
              "      <td>1.525806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>within</td>\n",
              "      <td>-2.960756</td>\n",
              "      <td>-4.321366</td>\n",
              "      <td>-0.751060</td>\n",
              "      <td>0.267510</td>\n",
              "      <td>0.057799</td>\n",
              "      <td>-0.745357</td>\n",
              "      <td>0.974449</td>\n",
              "      <td>-2.248575</td>\n",
              "      <td>-3.089024</td>\n",
              "      <td>-1.595938</td>\n",
              "      <td>2.551947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>collaboratively</td>\n",
              "      <td>1.194172</td>\n",
              "      <td>-0.755379</td>\n",
              "      <td>5.680677</td>\n",
              "      <td>-0.535311</td>\n",
              "      <td>-2.665900</td>\n",
              "      <td>-0.734639</td>\n",
              "      <td>1.185942</td>\n",
              "      <td>-0.941296</td>\n",
              "      <td>1.630548</td>\n",
              "      <td>2.094877</td>\n",
              "      <td>1.108514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>california</td>\n",
              "      <td>2.510731</td>\n",
              "      <td>-0.982859</td>\n",
              "      <td>0.435630</td>\n",
              "      <td>3.191416</td>\n",
              "      <td>3.641332</td>\n",
              "      <td>-0.052642</td>\n",
              "      <td>-1.321754</td>\n",
              "      <td>-0.641195</td>\n",
              "      <td>0.131931</td>\n",
              "      <td>1.613738</td>\n",
              "      <td>-3.311498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>science</td>\n",
              "      <td>-0.551139</td>\n",
              "      <td>-1.448377</td>\n",
              "      <td>-1.615890</td>\n",
              "      <td>0.856164</td>\n",
              "      <td>-1.522292</td>\n",
              "      <td>-0.753807</td>\n",
              "      <td>2.777269</td>\n",
              "      <td>0.226023</td>\n",
              "      <td>2.236964</td>\n",
              "      <td>-0.210313</td>\n",
              "      <td>-1.917423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>basic</td>\n",
              "      <td>-0.909066</td>\n",
              "      <td>-2.990848</td>\n",
              "      <td>1.317745</td>\n",
              "      <td>2.683186</td>\n",
              "      <td>0.562220</td>\n",
              "      <td>-1.092166</td>\n",
              "      <td>-3.212826</td>\n",
              "      <td>0.840820</td>\n",
              "      <td>-2.277616</td>\n",
              "      <td>0.961267</td>\n",
              "      <td>0.142223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>array</td>\n",
              "      <td>0.582021</td>\n",
              "      <td>-0.087088</td>\n",
              "      <td>2.279439</td>\n",
              "      <td>3.433734</td>\n",
              "      <td>0.428754</td>\n",
              "      <td>0.796281</td>\n",
              "      <td>-1.130210</td>\n",
              "      <td>-0.969612</td>\n",
              "      <td>-0.140981</td>\n",
              "      <td>-2.696630</td>\n",
              "      <td>1.332681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>report</td>\n",
              "      <td>-2.258520</td>\n",
              "      <td>0.012439</td>\n",
              "      <td>0.632437</td>\n",
              "      <td>0.900421</td>\n",
              "      <td>0.310458</td>\n",
              "      <td>-1.732836</td>\n",
              "      <td>-0.442726</td>\n",
              "      <td>-0.859527</td>\n",
              "      <td>-1.892546</td>\n",
              "      <td>-0.320359</td>\n",
              "      <td>-0.455522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>emphasis</td>\n",
              "      <td>2.039919</td>\n",
              "      <td>1.628837</td>\n",
              "      <td>-1.075701</td>\n",
              "      <td>3.286407</td>\n",
              "      <td>-2.728633</td>\n",
              "      <td>-0.350052</td>\n",
              "      <td>1.796655</td>\n",
              "      <td>-1.404498</td>\n",
              "      <td>0.700287</td>\n",
              "      <td>3.458518</td>\n",
              "      <td>-1.334956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>jobs</td>\n",
              "      <td>-5.421380</td>\n",
              "      <td>0.999904</td>\n",
              "      <td>-0.049857</td>\n",
              "      <td>-0.393037</td>\n",
              "      <td>-0.908295</td>\n",
              "      <td>2.045257</td>\n",
              "      <td>-1.449659</td>\n",
              "      <td>0.672499</td>\n",
              "      <td>0.555225</td>\n",
              "      <td>-0.600967</td>\n",
              "      <td>-0.664459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>dwellers</td>\n",
              "      <td>-0.590797</td>\n",
              "      <td>-0.612070</td>\n",
              "      <td>3.054773</td>\n",
              "      <td>-0.524081</td>\n",
              "      <td>1.945417</td>\n",
              "      <td>-2.918891</td>\n",
              "      <td>-3.591648</td>\n",
              "      <td>3.111043</td>\n",
              "      <td>-1.004840</td>\n",
              "      <td>0.336542</td>\n",
              "      <td>1.002935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>kinds</td>\n",
              "      <td>-0.306280</td>\n",
              "      <td>1.217430</td>\n",
              "      <td>0.140168</td>\n",
              "      <td>1.427476</td>\n",
              "      <td>-0.673760</td>\n",
              "      <td>0.266294</td>\n",
              "      <td>-2.738298</td>\n",
              "      <td>2.238429</td>\n",
              "      <td>0.947521</td>\n",
              "      <td>-2.817619</td>\n",
              "      <td>0.259863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>related</td>\n",
              "      <td>-1.263490</td>\n",
              "      <td>0.090258</td>\n",
              "      <td>0.050840</td>\n",
              "      <td>0.898262</td>\n",
              "      <td>1.053396</td>\n",
              "      <td>-0.643448</td>\n",
              "      <td>0.508116</td>\n",
              "      <td>-0.320027</td>\n",
              "      <td>0.632642</td>\n",
              "      <td>-3.355846</td>\n",
              "      <td>0.517253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>series</td>\n",
              "      <td>-5.240580</td>\n",
              "      <td>-2.059596</td>\n",
              "      <td>0.242666</td>\n",
              "      <td>0.744884</td>\n",
              "      <td>-1.102636</td>\n",
              "      <td>-1.914464</td>\n",
              "      <td>-0.051091</td>\n",
              "      <td>2.903068</td>\n",
              "      <td>-2.928806</td>\n",
              "      <td>0.989769</td>\n",
              "      <td>-2.178857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>children</td>\n",
              "      <td>0.936614</td>\n",
              "      <td>-0.406109</td>\n",
              "      <td>1.159183</td>\n",
              "      <td>-0.559885</td>\n",
              "      <td>-0.569739</td>\n",
              "      <td>1.684381</td>\n",
              "      <td>-0.135495</td>\n",
              "      <td>4.244633</td>\n",
              "      <td>-2.087314</td>\n",
              "      <td>3.371822</td>\n",
              "      <td>0.206031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>perform</td>\n",
              "      <td>-2.981269</td>\n",
              "      <td>-0.258639</td>\n",
              "      <td>-0.259092</td>\n",
              "      <td>0.046852</td>\n",
              "      <td>-0.367801</td>\n",
              "      <td>-0.963906</td>\n",
              "      <td>1.753918</td>\n",
              "      <td>1.385706</td>\n",
              "      <td>3.300816</td>\n",
              "      <td>0.110123</td>\n",
              "      <td>-0.773896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>intent</td>\n",
              "      <td>-1.434528</td>\n",
              "      <td>-1.046121</td>\n",
              "      <td>-0.882730</td>\n",
              "      <td>1.479108</td>\n",
              "      <td>-2.655175</td>\n",
              "      <td>2.283927</td>\n",
              "      <td>-4.234534</td>\n",
              "      <td>3.046739</td>\n",
              "      <td>0.012831</td>\n",
              "      <td>1.286609</td>\n",
              "      <td>-1.638112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>particular</td>\n",
              "      <td>-4.007369</td>\n",
              "      <td>1.641919</td>\n",
              "      <td>1.370438</td>\n",
              "      <td>-1.940596</td>\n",
              "      <td>1.527619</td>\n",
              "      <td>-0.983398</td>\n",
              "      <td>0.580717</td>\n",
              "      <td>-1.234668</td>\n",
              "      <td>2.231275</td>\n",
              "      <td>-3.551228</td>\n",
              "      <td>1.591439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>inception</td>\n",
              "      <td>0.104651</td>\n",
              "      <td>-2.164328</td>\n",
              "      <td>0.082052</td>\n",
              "      <td>-1.151966</td>\n",
              "      <td>2.893062</td>\n",
              "      <td>-4.089683</td>\n",
              "      <td>0.639129</td>\n",
              "      <td>-2.442200</td>\n",
              "      <td>1.805703</td>\n",
              "      <td>-0.637064</td>\n",
              "      <td>1.393364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>surgical</td>\n",
              "      <td>0.301149</td>\n",
              "      <td>0.712686</td>\n",
              "      <td>1.796554</td>\n",
              "      <td>0.234692</td>\n",
              "      <td>0.204631</td>\n",
              "      <td>-0.480412</td>\n",
              "      <td>-5.286329</td>\n",
              "      <td>1.985793</td>\n",
              "      <td>-1.946841</td>\n",
              "      <td>3.723568</td>\n",
              "      <td>-0.444537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>interaction</td>\n",
              "      <td>0.206737</td>\n",
              "      <td>-2.824313</td>\n",
              "      <td>-1.160092</td>\n",
              "      <td>-3.654775</td>\n",
              "      <td>-3.087504</td>\n",
              "      <td>-1.458389</td>\n",
              "      <td>-1.890706</td>\n",
              "      <td>2.471283</td>\n",
              "      <td>-1.093212</td>\n",
              "      <td>1.638046</td>\n",
              "      <td>0.879077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>areas</td>\n",
              "      <td>-0.987216</td>\n",
              "      <td>-1.548787</td>\n",
              "      <td>0.907590</td>\n",
              "      <td>1.415211</td>\n",
              "      <td>-0.886186</td>\n",
              "      <td>-1.194531</td>\n",
              "      <td>1.165091</td>\n",
              "      <td>-0.516618</td>\n",
              "      <td>0.091924</td>\n",
              "      <td>-0.152280</td>\n",
              "      <td>3.195221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>markets</td>\n",
              "      <td>-0.298847</td>\n",
              "      <td>-0.741626</td>\n",
              "      <td>-2.038061</td>\n",
              "      <td>1.322611</td>\n",
              "      <td>-2.803915</td>\n",
              "      <td>0.414941</td>\n",
              "      <td>-1.651908</td>\n",
              "      <td>0.872670</td>\n",
              "      <td>-1.517872</td>\n",
              "      <td>-3.794070</td>\n",
              "      <td>0.492849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>investigates</td>\n",
              "      <td>2.374478</td>\n",
              "      <td>-1.847623</td>\n",
              "      <td>3.357887</td>\n",
              "      <td>-0.179234</td>\n",
              "      <td>-0.463340</td>\n",
              "      <td>-0.957171</td>\n",
              "      <td>1.160333</td>\n",
              "      <td>0.466338</td>\n",
              "      <td>1.512953</td>\n",
              "      <td>-1.543327</td>\n",
              "      <td>2.307041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>learning</td>\n",
              "      <td>0.584265</td>\n",
              "      <td>-0.535348</td>\n",
              "      <td>-1.192148</td>\n",
              "      <td>-0.453188</td>\n",
              "      <td>0.731243</td>\n",
              "      <td>1.098688</td>\n",
              "      <td>0.103071</td>\n",
              "      <td>0.613705</td>\n",
              "      <td>-0.842558</td>\n",
              "      <td>-0.542797</td>\n",
              "      <td>0.979189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1001</th>\n",
              "      <td>accustomed</td>\n",
              "      <td>-1.492105</td>\n",
              "      <td>1.734001</td>\n",
              "      <td>-1.160462</td>\n",
              "      <td>-5.401177</td>\n",
              "      <td>-4.600907</td>\n",
              "      <td>0.137100</td>\n",
              "      <td>1.290076</td>\n",
              "      <td>0.974223</td>\n",
              "      <td>-0.720087</td>\n",
              "      <td>-0.370197</td>\n",
              "      <td>-2.363230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1002</th>\n",
              "      <td>indian</td>\n",
              "      <td>2.522615</td>\n",
              "      <td>-0.143327</td>\n",
              "      <td>-0.395750</td>\n",
              "      <td>-0.666042</td>\n",
              "      <td>3.794597</td>\n",
              "      <td>-1.254263</td>\n",
              "      <td>-1.226389</td>\n",
              "      <td>-0.175451</td>\n",
              "      <td>-3.916353</td>\n",
              "      <td>-1.939659</td>\n",
              "      <td>-2.677902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1003</th>\n",
              "      <td>avoid</td>\n",
              "      <td>0.799362</td>\n",
              "      <td>1.186835</td>\n",
              "      <td>-0.026967</td>\n",
              "      <td>-0.792382</td>\n",
              "      <td>-1.017167</td>\n",
              "      <td>5.158607</td>\n",
              "      <td>1.146910</td>\n",
              "      <td>0.361582</td>\n",
              "      <td>6.195185</td>\n",
              "      <td>-0.006817</td>\n",
              "      <td>0.395418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1004</th>\n",
              "      <td>fantasies</td>\n",
              "      <td>-1.823676</td>\n",
              "      <td>-0.201644</td>\n",
              "      <td>-0.369052</td>\n",
              "      <td>-2.084759</td>\n",
              "      <td>-1.335514</td>\n",
              "      <td>-0.546094</td>\n",
              "      <td>1.204107</td>\n",
              "      <td>-1.307884</td>\n",
              "      <td>1.236073</td>\n",
              "      <td>-4.191468</td>\n",
              "      <td>-1.227603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1005</th>\n",
              "      <td>mere</td>\n",
              "      <td>0.020670</td>\n",
              "      <td>-4.314106</td>\n",
              "      <td>1.472602</td>\n",
              "      <td>-1.260163</td>\n",
              "      <td>1.858813</td>\n",
              "      <td>-1.919627</td>\n",
              "      <td>-0.802453</td>\n",
              "      <td>-1.774072</td>\n",
              "      <td>0.896891</td>\n",
              "      <td>1.209154</td>\n",
              "      <td>3.020351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1006</th>\n",
              "      <td>transformed</td>\n",
              "      <td>-0.758719</td>\n",
              "      <td>-4.562911</td>\n",
              "      <td>-1.045491</td>\n",
              "      <td>-1.320612</td>\n",
              "      <td>-1.445722</td>\n",
              "      <td>0.593815</td>\n",
              "      <td>-0.496694</td>\n",
              "      <td>0.248594</td>\n",
              "      <td>-1.172577</td>\n",
              "      <td>0.764768</td>\n",
              "      <td>-0.495048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1007</th>\n",
              "      <td>constitutes</td>\n",
              "      <td>-2.607455</td>\n",
              "      <td>1.210496</td>\n",
              "      <td>-0.034629</td>\n",
              "      <td>0.458879</td>\n",
              "      <td>-0.124961</td>\n",
              "      <td>-1.976712</td>\n",
              "      <td>2.840545</td>\n",
              "      <td>-1.502271</td>\n",
              "      <td>-2.871768</td>\n",
              "      <td>-0.119759</td>\n",
              "      <td>-3.155386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008</th>\n",
              "      <td>framework</td>\n",
              "      <td>-0.540254</td>\n",
              "      <td>-2.542849</td>\n",
              "      <td>-1.557305</td>\n",
              "      <td>-1.235886</td>\n",
              "      <td>1.863991</td>\n",
              "      <td>0.860988</td>\n",
              "      <td>-0.850409</td>\n",
              "      <td>2.179594</td>\n",
              "      <td>-3.496694</td>\n",
              "      <td>-2.143968</td>\n",
              "      <td>3.714683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1009</th>\n",
              "      <td>currently</td>\n",
              "      <td>-0.364884</td>\n",
              "      <td>-4.202911</td>\n",
              "      <td>-1.008039</td>\n",
              "      <td>0.299645</td>\n",
              "      <td>1.608164</td>\n",
              "      <td>-0.485524</td>\n",
              "      <td>0.636712</td>\n",
              "      <td>-0.289095</td>\n",
              "      <td>-0.070719</td>\n",
              "      <td>0.001268</td>\n",
              "      <td>0.759781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1010</th>\n",
              "      <td>addressing</td>\n",
              "      <td>4.116459</td>\n",
              "      <td>1.621028</td>\n",
              "      <td>-3.880038</td>\n",
              "      <td>-0.428251</td>\n",
              "      <td>-0.800806</td>\n",
              "      <td>1.669281</td>\n",
              "      <td>-1.017861</td>\n",
              "      <td>1.943067</td>\n",
              "      <td>-1.018434</td>\n",
              "      <td>-1.317527</td>\n",
              "      <td>0.492480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1011</th>\n",
              "      <td>forms</td>\n",
              "      <td>-0.503340</td>\n",
              "      <td>-6.595904</td>\n",
              "      <td>-1.044964</td>\n",
              "      <td>0.946706</td>\n",
              "      <td>-0.127387</td>\n",
              "      <td>0.477499</td>\n",
              "      <td>0.652823</td>\n",
              "      <td>2.175205</td>\n",
              "      <td>1.912181</td>\n",
              "      <td>-0.266938</td>\n",
              "      <td>1.187348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1012</th>\n",
              "      <td>labeling</td>\n",
              "      <td>3.349973</td>\n",
              "      <td>-2.003524</td>\n",
              "      <td>-0.881265</td>\n",
              "      <td>1.886145</td>\n",
              "      <td>0.947203</td>\n",
              "      <td>-1.246059</td>\n",
              "      <td>1.401883</td>\n",
              "      <td>1.305416</td>\n",
              "      <td>1.906543</td>\n",
              "      <td>0.098351</td>\n",
              "      <td>3.732025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1013</th>\n",
              "      <td>time</td>\n",
              "      <td>-1.906205</td>\n",
              "      <td>0.364989</td>\n",
              "      <td>1.815584</td>\n",
              "      <td>0.061055</td>\n",
              "      <td>0.487777</td>\n",
              "      <td>-0.454773</td>\n",
              "      <td>1.247999</td>\n",
              "      <td>-0.194764</td>\n",
              "      <td>-1.226973</td>\n",
              "      <td>0.680141</td>\n",
              "      <td>0.070321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1014</th>\n",
              "      <td>teach</td>\n",
              "      <td>-5.403559</td>\n",
              "      <td>0.857080</td>\n",
              "      <td>2.295503</td>\n",
              "      <td>-1.550107</td>\n",
              "      <td>1.292950</td>\n",
              "      <td>-0.263922</td>\n",
              "      <td>-1.335896</td>\n",
              "      <td>2.114068</td>\n",
              "      <td>2.270699</td>\n",
              "      <td>0.935154</td>\n",
              "      <td>1.445499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015</th>\n",
              "      <td>nervous</td>\n",
              "      <td>0.521350</td>\n",
              "      <td>1.199469</td>\n",
              "      <td>4.643741</td>\n",
              "      <td>-3.406666</td>\n",
              "      <td>0.407515</td>\n",
              "      <td>-2.886500</td>\n",
              "      <td>-2.481126</td>\n",
              "      <td>0.774942</td>\n",
              "      <td>1.454482</td>\n",
              "      <td>1.103422</td>\n",
              "      <td>-1.750612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1016</th>\n",
              "      <td>greg</td>\n",
              "      <td>1.160068</td>\n",
              "      <td>-1.211616</td>\n",
              "      <td>0.946144</td>\n",
              "      <td>1.773170</td>\n",
              "      <td>0.063965</td>\n",
              "      <td>-4.757325</td>\n",
              "      <td>-3.460662</td>\n",
              "      <td>-2.573623</td>\n",
              "      <td>0.049873</td>\n",
              "      <td>-2.547289</td>\n",
              "      <td>-2.007923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017</th>\n",
              "      <td>recommendations</td>\n",
              "      <td>-5.778338</td>\n",
              "      <td>0.559813</td>\n",
              "      <td>1.599625</td>\n",
              "      <td>-0.544097</td>\n",
              "      <td>-0.232724</td>\n",
              "      <td>-1.422657</td>\n",
              "      <td>0.513721</td>\n",
              "      <td>-2.878134</td>\n",
              "      <td>-0.688992</td>\n",
              "      <td>-1.671984</td>\n",
              "      <td>-2.439376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018</th>\n",
              "      <td>generality</td>\n",
              "      <td>-1.609791</td>\n",
              "      <td>-4.216568</td>\n",
              "      <td>2.364270</td>\n",
              "      <td>1.449657</td>\n",
              "      <td>0.880447</td>\n",
              "      <td>-1.024882</td>\n",
              "      <td>2.065055</td>\n",
              "      <td>2.391632</td>\n",
              "      <td>1.380363</td>\n",
              "      <td>0.207342</td>\n",
              "      <td>-0.729879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019</th>\n",
              "      <td>j</td>\n",
              "      <td>-0.415475</td>\n",
              "      <td>2.580332</td>\n",
              "      <td>-2.811994</td>\n",
              "      <td>0.789329</td>\n",
              "      <td>-0.288739</td>\n",
              "      <td>-3.365663</td>\n",
              "      <td>0.541177</td>\n",
              "      <td>-1.526636</td>\n",
              "      <td>-0.409209</td>\n",
              "      <td>2.178269</td>\n",
              "      <td>-3.107017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1020</th>\n",
              "      <td>facebook</td>\n",
              "      <td>2.042774</td>\n",
              "      <td>1.544387</td>\n",
              "      <td>2.351853</td>\n",
              "      <td>2.198567</td>\n",
              "      <td>0.275521</td>\n",
              "      <td>0.552238</td>\n",
              "      <td>2.066918</td>\n",
              "      <td>-1.693731</td>\n",
              "      <td>-2.612621</td>\n",
              "      <td>-1.638212</td>\n",
              "      <td>-2.026200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>already</td>\n",
              "      <td>-1.813016</td>\n",
              "      <td>0.058271</td>\n",
              "      <td>0.173425</td>\n",
              "      <td>-0.437495</td>\n",
              "      <td>-0.773097</td>\n",
              "      <td>-0.577098</td>\n",
              "      <td>-1.071727</td>\n",
              "      <td>-0.373906</td>\n",
              "      <td>1.750095</td>\n",
              "      <td>2.148396</td>\n",
              "      <td>-2.358278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1022</th>\n",
              "      <td>improve</td>\n",
              "      <td>-0.209605</td>\n",
              "      <td>-0.684662</td>\n",
              "      <td>-0.164761</td>\n",
              "      <td>0.189808</td>\n",
              "      <td>0.592968</td>\n",
              "      <td>0.333698</td>\n",
              "      <td>0.677973</td>\n",
              "      <td>0.131737</td>\n",
              "      <td>1.139203</td>\n",
              "      <td>0.693647</td>\n",
              "      <td>0.339563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1023</th>\n",
              "      <td>reason</td>\n",
              "      <td>3.280705</td>\n",
              "      <td>2.438143</td>\n",
              "      <td>0.306607</td>\n",
              "      <td>-3.869768</td>\n",
              "      <td>1.105916</td>\n",
              "      <td>-0.451461</td>\n",
              "      <td>-2.695379</td>\n",
              "      <td>-0.179215</td>\n",
              "      <td>0.691698</td>\n",
              "      <td>2.612891</td>\n",
              "      <td>-0.737790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024</th>\n",
              "      <td>constraints</td>\n",
              "      <td>-1.685513</td>\n",
              "      <td>1.430068</td>\n",
              "      <td>4.266109</td>\n",
              "      <td>-0.032281</td>\n",
              "      <td>0.429928</td>\n",
              "      <td>1.482554</td>\n",
              "      <td>0.675563</td>\n",
              "      <td>-2.913542</td>\n",
              "      <td>-3.453462</td>\n",
              "      <td>0.536071</td>\n",
              "      <td>1.284666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1025</th>\n",
              "      <td>prominent</td>\n",
              "      <td>-2.247478</td>\n",
              "      <td>-6.255657</td>\n",
              "      <td>-1.936173</td>\n",
              "      <td>-0.110402</td>\n",
              "      <td>1.497671</td>\n",
              "      <td>-0.438810</td>\n",
              "      <td>-0.235632</td>\n",
              "      <td>1.540921</td>\n",
              "      <td>0.309007</td>\n",
              "      <td>-1.011861</td>\n",
              "      <td>1.114514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>caregivers</td>\n",
              "      <td>0.846105</td>\n",
              "      <td>0.711858</td>\n",
              "      <td>-0.566384</td>\n",
              "      <td>-1.534437</td>\n",
              "      <td>1.982070</td>\n",
              "      <td>-3.113210</td>\n",
              "      <td>1.800421</td>\n",
              "      <td>5.948302</td>\n",
              "      <td>2.971969</td>\n",
              "      <td>-0.778027</td>\n",
              "      <td>-0.709916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>kalyanakrishnan</td>\n",
              "      <td>5.113711</td>\n",
              "      <td>-0.045879</td>\n",
              "      <td>0.071106</td>\n",
              "      <td>1.541493</td>\n",
              "      <td>2.611017</td>\n",
              "      <td>-2.224080</td>\n",
              "      <td>-1.416454</td>\n",
              "      <td>0.342786</td>\n",
              "      <td>-1.085765</td>\n",
              "      <td>-2.639759</td>\n",
              "      <td>-1.907140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1028</th>\n",
              "      <td>critical</td>\n",
              "      <td>1.415863</td>\n",
              "      <td>1.352313</td>\n",
              "      <td>3.606753</td>\n",
              "      <td>-2.538007</td>\n",
              "      <td>-1.019799</td>\n",
              "      <td>-0.414112</td>\n",
              "      <td>-1.758153</td>\n",
              "      <td>0.191282</td>\n",
              "      <td>-0.048384</td>\n",
              "      <td>-3.747708</td>\n",
              "      <td>0.160896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1029</th>\n",
              "      <td>mulligan</td>\n",
              "      <td>1.670050</td>\n",
              "      <td>0.924418</td>\n",
              "      <td>-1.046460</td>\n",
              "      <td>-0.030355</td>\n",
              "      <td>2.275670</td>\n",
              "      <td>2.777074</td>\n",
              "      <td>3.657496</td>\n",
              "      <td>-3.091417</td>\n",
              "      <td>-0.971728</td>\n",
              "      <td>1.507587</td>\n",
              "      <td>3.067374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1030</th>\n",
              "      <td>pinning</td>\n",
              "      <td>-1.601588</td>\n",
              "      <td>3.103243</td>\n",
              "      <td>-1.436457</td>\n",
              "      <td>0.147545</td>\n",
              "      <td>-2.684123</td>\n",
              "      <td>-3.436940</td>\n",
              "      <td>-1.520255</td>\n",
              "      <td>-2.624556</td>\n",
              "      <td>-1.208644</td>\n",
              "      <td>-0.638629</td>\n",
              "      <td>-0.855989</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1031 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 word        X1        X2  ...        X9       X10       X11\n",
              "0                     -0.030510  0.794130  ...  0.719598  0.729324  0.032105\n",
              "1                high -2.015454  0.884868  ... -0.906213  1.543818  0.609791\n",
              "2             protect -0.487714  2.816028  ...  1.736702  0.618867  0.986666\n",
              "3             systems  0.292698  0.728003  ...  0.881297  0.236392  0.138573\n",
              "4             closely  1.435934  0.594579  ...  3.507608  0.667975  0.923649\n",
              "5               fewer -0.452418  1.659052  ... -2.217718  0.309800  1.525806\n",
              "6              within -2.960756 -4.321366  ... -3.089024 -1.595938  2.551947\n",
              "7     collaboratively  1.194172 -0.755379  ...  1.630548  2.094877  1.108514\n",
              "8          california  2.510731 -0.982859  ...  0.131931  1.613738 -3.311498\n",
              "9             science -0.551139 -1.448377  ...  2.236964 -0.210313 -1.917423\n",
              "10              basic -0.909066 -2.990848  ... -2.277616  0.961267  0.142223\n",
              "11              array  0.582021 -0.087088  ... -0.140981 -2.696630  1.332681\n",
              "12             report -2.258520  0.012439  ... -1.892546 -0.320359 -0.455522\n",
              "13           emphasis  2.039919  1.628837  ...  0.700287  3.458518 -1.334956\n",
              "14               jobs -5.421380  0.999904  ...  0.555225 -0.600967 -0.664459\n",
              "15           dwellers -0.590797 -0.612070  ... -1.004840  0.336542  1.002935\n",
              "16              kinds -0.306280  1.217430  ...  0.947521 -2.817619  0.259863\n",
              "17            related -1.263490  0.090258  ...  0.632642 -3.355846  0.517253\n",
              "18             series -5.240580 -2.059596  ... -2.928806  0.989769 -2.178857\n",
              "19           children  0.936614 -0.406109  ... -2.087314  3.371822  0.206031\n",
              "20            perform -2.981269 -0.258639  ...  3.300816  0.110123 -0.773896\n",
              "21             intent -1.434528 -1.046121  ...  0.012831  1.286609 -1.638112\n",
              "22         particular -4.007369  1.641919  ...  2.231275 -3.551228  1.591439\n",
              "23          inception  0.104651 -2.164328  ...  1.805703 -0.637064  1.393364\n",
              "24           surgical  0.301149  0.712686  ... -1.946841  3.723568 -0.444537\n",
              "25        interaction  0.206737 -2.824313  ... -1.093212  1.638046  0.879077\n",
              "26              areas -0.987216 -1.548787  ...  0.091924 -0.152280  3.195221\n",
              "27            markets -0.298847 -0.741626  ... -1.517872 -3.794070  0.492849\n",
              "28       investigates  2.374478 -1.847623  ...  1.512953 -1.543327  2.307041\n",
              "29           learning  0.584265 -0.535348  ... -0.842558 -0.542797  0.979189\n",
              "...               ...       ...       ...  ...       ...       ...       ...\n",
              "1001       accustomed -1.492105  1.734001  ... -0.720087 -0.370197 -2.363230\n",
              "1002           indian  2.522615 -0.143327  ... -3.916353 -1.939659 -2.677902\n",
              "1003            avoid  0.799362  1.186835  ...  6.195185 -0.006817  0.395418\n",
              "1004        fantasies -1.823676 -0.201644  ...  1.236073 -4.191468 -1.227603\n",
              "1005             mere  0.020670 -4.314106  ...  0.896891  1.209154  3.020351\n",
              "1006      transformed -0.758719 -4.562911  ... -1.172577  0.764768 -0.495048\n",
              "1007      constitutes -2.607455  1.210496  ... -2.871768 -0.119759 -3.155386\n",
              "1008        framework -0.540254 -2.542849  ... -3.496694 -2.143968  3.714683\n",
              "1009        currently -0.364884 -4.202911  ... -0.070719  0.001268  0.759781\n",
              "1010       addressing  4.116459  1.621028  ... -1.018434 -1.317527  0.492480\n",
              "1011            forms -0.503340 -6.595904  ...  1.912181 -0.266938  1.187348\n",
              "1012         labeling  3.349973 -2.003524  ...  1.906543  0.098351  3.732025\n",
              "1013             time -1.906205  0.364989  ... -1.226973  0.680141  0.070321\n",
              "1014            teach -5.403559  0.857080  ...  2.270699  0.935154  1.445499\n",
              "1015          nervous  0.521350  1.199469  ...  1.454482  1.103422 -1.750612\n",
              "1016             greg  1.160068 -1.211616  ...  0.049873 -2.547289 -2.007923\n",
              "1017  recommendations -5.778338  0.559813  ... -0.688992 -1.671984 -2.439376\n",
              "1018       generality -1.609791 -4.216568  ...  1.380363  0.207342 -0.729879\n",
              "1019                j -0.415475  2.580332  ... -0.409209  2.178269 -3.107017\n",
              "1020         facebook  2.042774  1.544387  ... -2.612621 -1.638212 -2.026200\n",
              "1021          already -1.813016  0.058271  ...  1.750095  2.148396 -2.358278\n",
              "1022          improve -0.209605 -0.684662  ...  1.139203  0.693647  0.339563\n",
              "1023           reason  3.280705  2.438143  ...  0.691698  2.612891 -0.737790\n",
              "1024      constraints -1.685513  1.430068  ... -3.453462  0.536071  1.284666\n",
              "1025        prominent -2.247478 -6.255657  ...  0.309007 -1.011861  1.114514\n",
              "1026       caregivers  0.846105  0.711858  ...  2.971969 -0.778027 -0.709916\n",
              "1027  kalyanakrishnan  5.113711 -0.045879  ... -1.085765 -2.639759 -1.907140\n",
              "1028         critical  1.415863  1.352313  ... -0.048384 -3.747708  0.160896\n",
              "1029         mulligan  1.670050  0.924418  ... -0.971728  1.507587  3.067374\n",
              "1030          pinning -1.601588  3.103243  ... -1.208644 -0.638629 -0.855989\n",
              "\n",
              "[1031 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gblT1Grwn1j9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#saving the word embeddings a dictionary of arrays where key is the word and the value is the wordvector in the form of an array\n",
        "wordembeddings={}\n",
        "for i in w2v_df['word']:\n",
        "  vec=[]\n",
        "  j=0\n",
        "  vec.append(w2v_df['X1'][j])\n",
        "  vec.append(w2v_df['X2'][j])\n",
        "  vec.append(w2v_df['X3'][j])\n",
        "  vec.append(w2v_df['X4'][j])\n",
        "  vec.append(w2v_df['X5'][j])\n",
        "  vec.append(w2v_df['X6'][j])\n",
        "  vec.append(w2v_df['X7'][j])\n",
        "  vec.append(w2v_df['X8'][j])\n",
        "  vec.append(w2v_df['X9'][j])\n",
        "  vec.append(w2v_df['X10'][j])\n",
        "  vec.append(w2v_df['X11'][j])\n",
        "  j+=1\n",
        "  wordembeddings[i]= np.asarray(vec)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toZPOpVBn6Y9",
        "colab_type": "code",
        "outputId": "8ef35bb1-026c-427f-c329-246acdf29971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "wordembeddings"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'high': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'perform': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'particular': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'inception': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'areas': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'advance': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'mobile': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'navigation': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'approaches': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'talking': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'definition': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'collecting': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'scalable': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'purchases': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'centers': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'directed': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'trained': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'extend': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'retrieval': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'reconfigure': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'images': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'revolution': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'widely': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'one': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'pickup': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'gaining': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'new': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'safe': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'columbia': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'decision': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'handle': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'grosz': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'automated': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'unpredictable': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
              " 'done': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'next': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'individual': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'wealth': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'tom': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'result': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'robot': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'effecting': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'outcomes': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'representations': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'better': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'attention': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'brynjolfsson': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
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              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'substantial': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'reasoning': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'using': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'toward': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'dimensions': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'via': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'shah': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'sensing': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'meaningfully': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'sources': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'come': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'making': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'reality': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'entirely': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'self': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'collaborative': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'soon': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'including': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'contributions': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'designed': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'size': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'frame': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'risk': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'technology': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'sophisticated': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'lesser': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'millions': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'leading': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'instruction': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'unique': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'transform': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'could': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'ehr': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'ahead': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'humankind': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'southern': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'planning': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'experience': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'primarily': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'diminishing': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'provide': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " 'form': array([-0.03050997,  0.79413   , -1.061649  , -0.08344233, -0.13054961,\n",
              "        -0.35226867,  0.18186016, -0.0896504 ,  0.7195977 ,  0.729324  ,\n",
              "         0.03210484], dtype=float32),\n",
              " ...}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcKvIuqKoX8T",
        "colab_type": "code",
        "outputId": "098dbf04-796a-4a5e-eebf-caa178524772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "#finding the sentence vectors by adding the word vectors of the words in the sentence after cleaning them and dividing by the number of words in that sentence\n",
        "sentence_vect=[]\n",
        "for i in cleaned:\n",
        "  if len(i)!=0:\n",
        "    v = sum([wordembeddings.get(w, np.zeros((11,))) for w in i.split()])/(len(i.split())+0.000001)\n",
        "  else:\n",
        "    v=np.zeros((11,))\n",
        "  sentence_vect.append(v)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-78-21f445032664>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcleaned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwordembeddings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.000001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'numpy.float64' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDgnw0EOodPP",
        "colab_type": "code",
        "outputId": "2ca6e5e7-bc90-4c35-c199-135542300358",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sentence_vect"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uec_F0Ogn9ul",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#defining the similarity matrix\n",
        "sim_mat=np.zeros([len(cleaned),len(cleaned)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9BbAqPsoHrl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#defing a method for finding out cosine similarity\n",
        "def cossim(g,h):\n",
        "  l=len(g)\n",
        "  x,y,z=0,0,0\n",
        "  for i in range(0,l):\n",
        "    x=x+g[i]*h[i]\n",
        "    y=y+g[i]**2\n",
        "    z=z+h[i]**2\n",
        "  x=x/(y*z)\n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqrFK58NoK59",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#filling up the similarity matrix by finding out the similarity between sentences using cossim method\n",
        "for i in range(0, len(cleaned)):\n",
        "  for j in range(0, len(cleaned)):\n",
        "    if i!=j:\n",
        "      g=sentence_vect[i]\n",
        "      h=sentence_vect[j]\n",
        "      sim_mat[i][j]=cossim(g,h)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnYcQnKioO_E",
        "colab_type": "code",
        "outputId": "a0ab9793-1b33-40af-b999-a8cda90cf414",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "#similarity matrix\n",
        "sim_mat"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.23189048, 0.23189049, ..., 0.23189046, 0.23189047,\n",
              "        0.23189056],\n",
              "       [0.23189048, 0.        , 0.2318905 , ..., 0.23189047, 0.23189049,\n",
              "        0.23189058],\n",
              "       [0.23189049, 0.2318905 , 0.        , ..., 0.23189049, 0.2318905 ,\n",
              "        0.23189059],\n",
              "       ...,\n",
              "       [0.23189046, 0.23189047, 0.23189049, ..., 0.        , 0.23189047,\n",
              "        0.23189055],\n",
              "       [0.23189047, 0.23189049, 0.2318905 , ..., 0.23189047, 0.        ,\n",
              "        0.23189057],\n",
              "       [0.23189056, 0.23189058, 0.23189059, ..., 0.23189055, 0.23189057,\n",
              "        0.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AsrkgU0or6d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#finding the edge values ,that is by how much each sentence is similar to other sentences\n",
        "edge=[]\n",
        "for i in range(0,len(cleaned)):\n",
        "  for j in range(i+1,len(cleaned)):\n",
        "    dict={}\n",
        "    dict['weight']=sim_mat[i][j]\n",
        "    edge=edge+[(i,j,dict)]\n",
        "edge=(edge)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nAHBCjhtyI_",
        "colab_type": "code",
        "outputId": "f0cb322a-6be3-4b0f-a271-787548ef7cd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "edge"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " (7, 107, {'weight': 0.2318904983149687}),\n",
              " (7, 108, {'weight': 0.23189050256193966}),\n",
              " (7, 109, {'weight': 0.23189049511387166}),\n",
              " (7, 110, {'weight': 0.2318904777264387}),\n",
              " (7, 111, {'weight': 0.23189047191679923}),\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXkqltM6ow-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#finding the score of each sentence and storing it in a dictionary. the score is the mean of the edge values for each sentence with every other sentence.\n",
        "scores1={}\n",
        "k=0\n",
        "while(k<len(cor)):\n",
        "  sum=0\n",
        "  for i in edge:\n",
        "    if i[0] == k:\n",
        "      sum+=i[2]['weight']\n",
        "    elif i[1] == k:\n",
        "      sum+=i[2]['weight']\n",
        "  sum=sum/len(cor)\n",
        "  scores1[k]=sum \n",
        "  k+=1\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iLyFLfCuHzt",
        "colab_type": "code",
        "outputId": "8851cfbc-b765-45bb-e81c-6624ad0e72f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "scores1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 100: 0.23013375465285482,\n",
              " 101: 0.23013375242593753,\n",
              " 102: 0.23013380172444206,\n",
              " 103: 0.23013375465285482,\n",
              " 104: 0.23013374829170852,\n",
              " 105: 0.23013374829170852,\n",
              " 106: 0.23013375810996023,\n",
              " 107: 0.2301337493121702,\n",
              " 108: 0.23013375343864292,\n",
              " 109: 0.23013374720822372,\n",
              " 110: 0.2301337292586721,\n",
              " 111: 0.23013372487252912,\n",
              " 112: 0.23013373501068285,\n",
              " 113: 0.23013374720822372,\n",
              " 114: 0.23013375310021184,\n",
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              " 116: 0.23013375810996023,\n",
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              " 118: 0.23013375465285482,\n",
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              " 123: 0.23013374647045537,\n",
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              " 125: 0.23013374720822372,\n",
              " 126: 0.2301337493121702,\n",
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              " 130: 0.2301337441122801,\n",
              " 131: 0.23013382742904953}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cgCubk3o3Gd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ranked_sentences = sorted(((scores1[i],s) for i,s in enumerate(cor)), reverse=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYxXTvf6vHmO",
        "colab_type": "code",
        "outputId": "8617405c-337e-4c6e-c888-3dbae412f2fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "ranked_sentences"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.32465956564658704, '10 AI policy, now and'),\n",
              " (0.32465952064885595, 'What’s next for AI research?'),\n",
              " (0.3246594612461253,\n",
              "  'While impressive, these technologies are highly tailored to particular tasks.'),\n",
              " (0.3246594612461253,\n",
              "  'AI is also changing how people interact with technology.'),\n",
              " (0.32465946045194916,\n",
              "  'Great strides have also been made in machine translation among different languages, with more real-time person-to-person exchanges on the near horizon.'),\n",
              " (0.3246594604519491,\n",
              "  'People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals.'),\n",
              " (0.3246594604519491,\n",
              "  'Once a mostly academic area of study, twenty-first century AI enables a constellation of mainstream technologies that are having a substantial impact on everyday lives.'),\n",
              " (0.3246594604519491,\n",
              "  'On the other hand, if society approaches AI with a more open mind, the technologies emerging from the field could profoundly transform society for the better in the coming decades.'),\n",
              " (0.3246594604519491,\n",
              "  'Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers.'),\n",
              " (0.3246594604519491,\n",
              "  'In each domain, even as AI continues to deliver important benefits, it also raises important ethical and social issues, including privacy concerns.'),\n",
              " (0.3246594604519491,\n",
              "  'However, in many realms, AI will likely replace tasks rather than jobs in the near term, and will also create new kinds of jobs.'),\n",
              " (0.3246594604519491,\n",
              "  'Enabling more fluid interactions between people and promising AI technologies also remains a critical challenge in Education, which has seen considerable progress in the same period.'),\n",
              " (0.3246594604519491,\n",
              "  'But technical constraints and the high costs of reliable mechanical devices will continue to limit commercial opportunities to narrowly defined applications for the foreseeable future.'),\n",
              " (0.3246594604519491,\n",
              "  'Autonomous transportation will soon be commonplace and, as most people’s first experience with physically embodied AI systems, will strongly influence the public’s perception of AI.'),\n",
              " (0.3246594604519491,\n",
              "  'As a society, we are now at a crucial juncture in determining how to deploy AI-based technologies in ways that promote, not hinder, democratic values such as freedom, equality, and transparency.'),\n",
              " (0.3246594604519491,\n",
              "  '7 An accurate and sophisticated picture of AI—one that competes with its popular portrayal—is hampered at the start by the difficulty of pinning down a precise definition of artificial intelligence.'),\n",
              " (0.3246594604426896,\n",
              "  'This study’s focus on a typical North American city is deliberate and meant to highlight specific changes affecting the everyday lives of the millions of people who inhabit them.'),\n",
              " (0.3246594604426896,\n",
              "  'The latter raises the specter of innocent people being unjustifiably monitored, and care must be taken to avoid systematizing human bias and to protect civil liberties.'),\n",
              " (0.3246594604426896,\n",
              "  'Some traditional sources of entertainment have also embraced AI to compose music, create stage performances, and even to generate 3D scenes from natural language text.'),\n",
              " (0.3246594604426896,\n",
              "  'Natural Language Processing, often coupled with automatic speech recognition, is quickly becoming a commodity for widely spoken languages with large data sets.'),\n",
              " (0.3246594604426896,\n",
              "  'Longer term, AI may be thought of as a radically different mechanism for wealth creation in which everyone should be entitled to a portion of the world’s AI-produced treasures.'),\n",
              " (0.3246594604426896,\n",
              "  'In the typical North American city in 2030, physically embodied AI applications will not be limited to cars, but are likely to include trucks, flying vehicles, and personal robots.'),\n",
              " (0.3246594604426896,\n",
              "  'Beyond education, many opportunities exist for AI methods to assist Low-resource Communities by providing mitigations and solutions to a variety of social problems.'),\n",
              " (0.32465946044268956,\n",
              "  'Natural Language Processing (NLP) and knowledge representation and reasoning have enabled a machine to beat the Jeopardy champion and are bringing new power to Web searches.'),\n",
              " (0.32465946044268956,\n",
              "  'AI and robotics will also be applied across the globe in industries struggling to attract younger workers, such as agriculture, food processing, fulfillment centers, and factories.'),\n",
              " (0.3246594601703908,\n",
              "  'It has been the sub-area of AI most transformed by the rise of deep learning.'),\n",
              " (0.3246594601703908,\n",
              "  'Computer vision is currently the most prominent form of machine perception.'),\n",
              " (0.3246594601703908,\n",
              "  'AI will increasingly enable entertainment that is more interactive, personalized, and engaging.'),\n",
              " (0.32465946017039077,\n",
              "  'As a result, progress is uneven within and among the eight domains.'),\n",
              " (0.32465945481201997,\n",
              "  'Traditionally, funders have underinvested in AI research lacking commercial application.'),\n",
              " (0.32465945481201997,\n",
              "  'The research that fuels the AI revolution has also seen rapid changes.'),\n",
              " (0.32465945481201997,\n",
              "  'The enthusiasm with which people have already responded to AI-driven entertainment has been surprising.'),\n",
              " (0.32465945481201997,\n",
              "  'Robots and other AI technologies have already begun to displace jobs in some sectors.'),\n",
              " (0.32465945481201997,\n",
              "  'Much current research is focused on automatic image and video captioning.'),\n",
              " (0.32465945481201997,\n",
              "  'Many have already grown accustomed to touching and talking to their smart phones.'),\n",
              " (0.32465945481201997,\n",
              "  'It is not too soon for social debate on how the economic fruits of AI technologies should be shared.'),\n",
              " (0.3246594548120199,\n",
              "  'Some cut across sectors, such as employment/workplace and low-resource communities.'),\n",
              " (0.3246594548120199,\n",
              "  'Each application requires years of focused research and a careful, unique construction.'),\n",
              " (0.3246594544656324,\n",
              "  'Gaining public trust is also a challenge for AI use by Public Safety and Security professionals.'),\n",
              " (0.3246594544656324,\n",
              "  'AI will also lower the cost of many goods and services, effectively making everyone better off.'),\n",
              " (0.32465945446563227,\n",
              "  'While largely confined to academia over the past several decades, it is now seeing some practical, real-world successes.'),\n",
              " (0.32465945446563227,\n",
              "  'In fact, beneficial AI applications in schools, homes, and hospitals are already growing at an accelerated pace.'),\n",
              " (0.32465945446563227,\n",
              "  'For many applications, AI systems will have to work closely with care providers and patients to gain their trust.'),\n",
              " (0.32465945446563227,\n",
              "  'A prime example is Transportation, where a few key technologies have catalyzed the widespread adoption of AI with astonishing speed.'),\n",
              " (0.32465945446563227,\n",
              "  '5 how human labor is augmented or replaced by AI, creating new challenges for the economy and society more broadly.'),\n",
              " (0.32465945369570526,\n",
              "  'This alone will reconfigure the urban landscape, as traffic jams and parking challenges become obsolete.'),\n",
              " (0.32465945369570526,\n",
              "  'It promises to carry AI applications forward toward taking actions in the real world.'),\n",
              " (0.32465945369570526,\n",
              "  'Even Hollywood uses AI technologies to bring its dystopian AI fantasies to the screen.'),\n",
              " (0.32465945369570526,\n",
              "  'As with many aspects of AI, there is ongoing debate about the extent to which the technology replaces or enhances sociability.'),\n",
              " (0.3246594536957052,\n",
              "  'Interactive machine tutors are now being matched to students for teaching science, math, language, and other disciplines.'),\n",
              " (0.3246594536957052,\n",
              "  'In the approaches the Study Panel considered, none suggest there is currently a “general purpose” AI.'),\n",
              " (0.3246594536957052,\n",
              "  'For individuals, the quality of the lives we lead and how our contributions are valued are likely to shift gradually, but markedly.'),\n",
              " (0.3246594536957052,\n",
              "  'But the new jobs that will emerge are harder to imagine in advance than the existing jobs that will likely be lost.'),\n",
              " (0.3246594536957052,\n",
              "  'Advances in how intelligent machines interact naturally with caregivers, patients, and patients’ families are crucial.'),\n",
              " (0.3246594536957052,\n",
              "  'AI is poised to replace people in certain kinds of jobs, such as in the driving of taxis and trucks.'),\n",
              " (0.32465944929868196,\n",
              "  'Though drawing from a common source of research, AI technologies have influenced and will continue to influence these domains differently.'),\n",
              " (0.32465944929868196,\n",
              "  'These budding efforts suggest more could be done, particularly if agencies and organizations can engage and build trust with these communities.'),\n",
              " (0.32465944929868196,\n",
              "  'The report begins with a reflection on what constitutes Artificial Intelligence, and concludes with recommendations concerning AI-related policy.'),\n",
              " (0.32465944929868196,\n",
              "  'No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future.'),\n",
              " (0.32465944929868196,\n",
              "  'In each of these domains, the report both reflects on progress in the past fifteen years and anticipates developments in the coming fifteen years.'),\n",
              " (0.32465944929868196,\n",
              "  'Computer vision and AI planning, for example, drive the video games that are now a bigger entertainment industry than Hollywood.'),\n",
              " (0.32465944929868196,\n",
              "  '8 AI technologies could help address the needs of low-resource communities, and budding efforts are promising.'),\n",
              " (0.32465944927655177,\n",
              "  'Reinforcement learning is a framework that shifts the focus of machine learning from pattern recognition to experience-driven sequential decision-making.'),\n",
              " (0.32465944927655177,\n",
              "  'Collaborative systems research investigates models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans.'),\n",
              " (0.32465944927655177,\n",
              "  '9 markets for data-driven products, and the economic incentives to find new products and markets, have also stimulated research advances.'),\n",
              " (0.3246594492765517,\n",
              "  'This report is the first in a series to be issued at regular intervals as a part of the One Hundred Year Study on Artificial Intelligence (AI100).'),\n",
              " (0.3246594492765517,\n",
              "  'They will facilitate delivery of online purchases through flying drones, self-driving trucks, or robots that can get up the stairs to the front door.'),\n",
              " (0.3246594492765517,\n",
              "  'North American cities and federal agencies have already begun to deploy AI technologies in border administration and law enforcement.'),\n",
              " (0.3246594492765517,\n",
              "  'In reality, AI is already changing our daily lives, almost entirely in ways that improve human health, safety, and productivity.'),\n",
              " (0.3246594492765517,\n",
              "  'By 2030, they will rely heavily upon them, including improved cameras and drones for surveillance, algorithms to detect financial fraud, and predictive policing.'),\n",
              " (0.3246594492617177,\n",
              "  'Though clinical applications have been slow to move from the computer science lab to the real-world, there are hopeful signs that the pace of innovation will improve.'),\n",
              " (0.3246594492617177,\n",
              "  'Other factors include the rise of cloud computing resources and consumer demand for widespread access to services such as speech recognition and navigation support.'),\n",
              " (0.3246594492617177,\n",
              "  'Foremost among them is the maturation of machine learning, stimulated in part by the rise of the digital economy, which both provides and leverages large amounts of data.'),\n",
              " (0.3246594492617177,\n",
              "  'Contrary to the more fantastic predictions for AI in the popular press, the Study Panel found no cause for concern that AI is an imminent threat to humankind.'),\n",
              " (0.3246594492617177,\n",
              "  'Advances in healthcare can be promoted via the development of incentives and mechanisms for sharing data and for removing overbearing policy, regulatory, and commercial obstacles.'),\n",
              " (0.32465944899478416,\n",
              "  'While the rate of progress in AI has been patchy and unpredictable, there have been significant advances since the field’s inception sixty years ago.'),\n",
              " (0.32465944899478416,\n",
              "  'These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when needed or wanted.'),\n",
              " (0.32465944899478416,\n",
              "  'Some domains are primarily business sectors, such as transportation and healthcare, while others are more oriented to consumers, such as entertainment and home service robots.'),\n",
              " (0.32465944899478416,\n",
              "  'Research is now shifting to develop refined and capable systems that are able to interact with people through dialog, not just react to stylized requests.'),\n",
              " (0.32465944899478416,\n",
              "  'Advances in robotics will rely on commensurate advances to improve the reliability and generality of computer vision and other forms of machine perception.'),\n",
              " (0.32465944899478416,\n",
              "  'AI-based applications could improve health outcomes and the quality of life for millions of people in the coming years.'),\n",
              " (0.32465944899478416,\n",
              "  '6 OVERVIEW The frightening, futurist portrayals of Artificial Intelligence that dominate films and novels, and shape the popular imagination, are fictional.'),\n",
              " (0.3246594487962813,\n",
              "  'Unlike in the movies, there is no race of superhuman robots on the horizon or probably even possible.'),\n",
              " (0.3246594487962813,\n",
              "  'Research should be directed toward understanding how to leverage these attributes for individuals’ and society’s benefit.'),\n",
              " (0.3246594487962813,\n",
              "  'Many people have already grown accustomed to touching and talking to their smart phones.'),\n",
              " (0.3246594487962813,\n",
              "  'For the first time, computers are able to perform some vision tasks better than people.'),\n",
              " (0.3246594487962812,\n",
              "  'Special purpose robots will deliver packages, clean offices, and enhance security.'),\n",
              " (0.3246594487962812,\n",
              "  'People’s future relationships with machines will become ever more nuanced, fluid, and personalized.'),\n",
              " (0.3246594487962812,\n",
              "  'Each application typically requires years of specialized research and careful, unique construction.'),\n",
              " (0.3246594374000056,\n",
              "  'While drawing on common research and technologies, AI systems are specialized to accomplish particular tasks, and each application requires years of focused research and a careful, unique construction.'),\n",
              " (0.3246594374000056,\n",
              "  'Though quality education will always require active engagement by human teachers, AI promises to enhance education at all levels, especially by providing personalization at scale.'),\n",
              " (0.3246594374000056,\n",
              "  'These recommendations include accruing technical expertise about AI in government and devoting more resources—and removing impediments—to research on the fairness, security, privacy, and societal impacts of AI systems.'),\n",
              " (0.3246594374000056,\n",
              "  'Social and political decisions are likewise at play in AI’s influences on Employment and Workplace trends, such as the safety nets needed to protect people from structural changes in the economy.'),\n",
              " (0.3246594374000056,\n",
              "  'Robotics is currently concerned with how to train a robot to interact with the world around it in generalizable and predictable ways, how to facilitate manipulation of objects in interactive environments, and how to interact with people.'),\n",
              " (0.3246594374000056,\n",
              "  'Over the next several years, AI research, systems development, and social and regulatory frameworks will shape how the benefits of AI are weighed against its costs and risks, and how broadly these benefits are spread.'),\n",
              " (0.3246594374000056,\n",
              "  'New platforms and Longer term, AI may be thought of as a radically different mechanism for wealth creation in which everyone should be entitled to a portion of the world’s AI-produced treasures.'),\n",
              " (0.3246594374000056,\n",
              "  'Machine learning has been propelled dramatically forward by impressive empirical successes of artificial neural networks, which can now be trained with huge data sets and large-scale computing.'),\n",
              " (0.3246594374000056,\n",
              "  'Innovations relying on computer-based vision, speech recognition, and Natural Language Processing have driven these changes, as have concurrent scientific and technological advances in related fields.'),\n",
              " (0.3246594374000056,\n",
              "  'Better chips, low-cost 3D sensors, cloud-based machine learning, and advances in speech understanding will enhance future robots’ services and their interactions with people.'),\n",
              " (0.3246594374000056,\n",
              "  'As cars become better drivers than people, city-dwellers will own fewer cars, live further from work, and spend time differently, leading to an entirely new urban organization.'),\n",
              " (0.32465943371085215,\n",
              "  'Internet of Things (IoT) research is devoted to the idea that a wide array of devices, including appliances, vehicles, buildings, and cameras, can be interconnected to collect and share their abundant sensory information to use for intelligent purposes.'),\n",
              " (0.32465943371085215,\n",
              "  'Deep learning, a class of learning procedures, has facilitated object recognition in images, video labeling, and activity recognition, and is making significant inroads into other areas of perception, such as audio, speech, and natural language processing.'),\n",
              " (0.32465943371085215,\n",
              "  'Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems and bodies to sense, learn, reason, and take action.'),\n",
              " (0.3246594325575807,\n",
              "  'In Healthcare, there has been an immense forward leap in collecting useful data from personal monitoring devices and mobile apps, from electronic health records (EHR) in clinical settings and, to a lesser extent, from surgical robots designed to assist with medical procedures and service robots supporting hospital operations.'),\n",
              " (0.32465943066861364,\n",
              "  'With targeted incentives and funding priorities, Society is now at a crucial juncture in determining how to deploy AI-based technologies in ways that promote rather than hinder democratic values such as freedom, equality, and transparency.'),\n",
              " (0.32465943066861364,\n",
              "  'This approach has been come to be known as “deep learning.” The leap in the performance of information processing algorithms has been accompanied by significant progress in hardware technology for basic operations such as sensing, perception, and object recognition.'),\n",
              " (0.32465943066861364,\n",
              "  'Crowdsourcing and human computation research investigates methods to augment computer systems by making automated calls to human expertise to solve problems that computers alone cannot solve well.'),\n",
              " (0.32465943066861364,\n",
              "  'And while the potential to abuse AI technologies must be acknowledged and addressed, their greater potential is, among other things, to make driving safer, help children learn, and extend and enhance people’s lives.'),\n",
              " (0.3246594306686135,\n",
              "  'Well-deployed AI prediction tools have the potential to provide new kinds of transparency about data and inferences, and may be applied to detect, remove, or reduce human bias, rather than reinforcing it.'),\n",
              " (0.3246594306686135,\n",
              "  'If society approaches these technologies primarily with fear and suspicion, missteps that slow AI’s development or drive it underground will result, impeding important work on ensuring the safety and reliability of AI technologies.'),\n",
              " (0.3246594299875998,\n",
              "  'Natural Language Processing, machine learning, and crowdsourcing have boosted online learning and enabled teachers in higher education to multiply the size of their classrooms while addressing individual students’ learning needs and styles.'),\n",
              " (0.3246594299875998,\n",
              "  'Major research universities devote departments to AI studies, and technology companies such as Apple, Facebook, Google, IBM, and Microsoft spend heavily to explore AI applications they regard as critical to their futures.'),\n",
              " (0.3246594299875998,\n",
              "  'In similarly targeted applications, substantial increases in the future uses of AI technologies, including more self-driving cars, healthcare diagnostics and targeted treatments, and physical assistance for elder care can be expected.'),\n",
              " (0.3246594299875998,\n",
              "  'Entertainment has been transformed by social networks and other platforms for sharing and browsing blogs, videos, and photos, which rely on techniques actively developed in NLP, information retrieval, image processing, crowdsourcing, and machine learning.'),\n",
              " (0.32465942824199295,\n",
              "  'Over the next fifteen years in a typical North American city, the use of these technologies in the classroom and in the home is likely to expand significantly, provided they can be meaningfully integrated with face-to-face learning.'),\n",
              " (0.32465942824199295,\n",
              "  'Improvements in safe and reliable hardware will spur innovation over the next fifteen years, as they will with Home/Service Robots, which have already entered people’s houses, primarily in the form of vacuum cleaners.'),\n",
              " (0.3246594282419929,\n",
              "  'The field of AI is shifting toward building intelligent systems that can collaborate effectively with people, including creative ways to develop interactive and scalable ways for people to teach robots.'),\n",
              " (0.32465942684209553,\n",
              "  'Algorithmic game theory and computational social choice draw attention to the economic and social computing dimensions of AI, such as how systems can handle potentially misaligned incentives, including self-interested human participants or firms and the automated AI-based agents representing them.'),\n",
              " (0.32465942587530544,\n",
              "  'These trends drive the currently “hot” areas of AI research into both fundamental methods and application areas: Large-scale machine learning concerns the design of learning algorithms, as well as scaling existing algorithms, to work with extremely large data sets.'),\n",
              " (0.32465942103123047,\n",
              "  'Neuromorphic computing is a set of technologies that seek to mimic biological neural networks to improve the hardware efficiency and robustness of computing systems, often replacing an older emphasis on separate modules for input/ output, instruction-processing, and memory.'),\n",
              " (0.3246594210312304,\n",
              "  'The Study Panel further narrowed its inquiry to eight domains where AI is already having or is projected to have the greatest impact: transportation, healthcare, education, low-resource communities, public safety and security, employment and workplace, home/service robots, and entertainment.'),\n",
              " (0.3246594210312304,\n",
              "  'For example, in a mere fifteen years in a typical North American city—the time frame and scope of this report— AI applications are likely to transform transportation toward self-driving vehicles with on-time pickup and delivery of people and packages.'),\n",
              " (0.3246594210312304,\n",
              "  'Deep learning, a form of machine learning based on layered representations of variables referred to as neural networks, has made speech-understanding practical on our phones and in our kitchens, and its algorithms can be applied widely to an array of applications that rely on pattern recognition.'),\n",
              " (0.3246594203292699,\n",
              "  'Now, as it becomes a central force in society, the field of AI is shifting toward building intelligent systems that can collaborate effectively with people, and that are more generally human-aware, including creative ways to develop interactive and scalable ways for people to teach robots.'),\n",
              " (0.32465941896937783,\n",
              "  'Using data mining and machine learning, for example, AI has been used to create predictive models to help government agencies address issues such as prevention of lead poisoning in at-risk children and distribution of food efficiently.'),\n",
              " (0.32465941896937783,\n",
              "  'At the same time, many of these developments will spur disruptions in Substantial increases in the future uses of AI applications, including more self-driving cars, healthcare diagnostics and targeted treatment, and physical assistance for elder care can be expected.'),\n",
              " (0.32465940998202464,\n",
              "  'Application design and policy decisions made in the near term are likely to have long-lasting influences on the nature and directions of such developments, making it important for AI researchers, developers, social scientists, and policymakers to balance the imperative to innovate with mechanisms to ensure that AI’s economic and social benefits are broadly shared across society.'),\n",
              " (0.3246593787409826,\n",
              "  'Starting from a charge given by the AI100 Standing Committee to consider the likely influences of AI in a typical North American city by the year 2030, the 2015 Study Panel, comprising experts in AI and other relevant areas focused their attention on eight domains they considered most salient: transportation; service robots; healthcare; education; low-resource communities; public safety and security; employment and workplace; and entertainment.'),\n",
              " (0.32465936686934516,\n",
              "  'Each domain faces varied AI-related challenges, including the difficulty of creating safe and reliable hardware for sensing and effecting (transportation and service robots), the difficulty of smoothly interacting with human experts (healthcare and education), the challenge of gaining public trust (low-resource communities and public safety and security), the challenge of overcoming fears of marginalizing humans (employment and workplace) and the risk of diminishing interpersonal interaction (entertainment).'),\n",
              " (0.324659358431399,\n",
              "  'Though drawing from a common source of research, each domain reflects different AI influences and challenges, such as the difficulty of creating safe and reliable hardware (transportation and service robots), the difficulty of smoothly interacting with human experts (healthcare and education), the challenge of gaining public trust (low-resource communities and public safety and security), the challenge of overcoming fears of marginalizing humans (employment and workplace), and the social and societal risk of diminishing interpersonal interactions (entertainment).'),\n",
              " (0.324659358431399,\n",
              "  'Study Panel: Peter Stone, Chair, University of Texas at Austin, Rodney Brooks, Rethink Robotics, Erik Brynjolfsson, Massachussets Institute of Technology, Ryan Calo, University of Washington, Oren Etzioni, Allen Institute for AI, Greg Hager, Johns Hopkins University, Julia Hirschberg, Columbia University, Shivaram Kalyanakrishnan, Indian Institute of Technology Bombay, Ece Kamar, Microsoft Research, Sarit Kraus, Bar Ilan University.'),\n",
              " (0.32465932096047756,\n",
              "  'Kevin Leyton-Brown, University of British Columbia, David Parkes, Harvard University, William Press, University of Texas at Austin, AnnaLee (Anno) Saxenian, University of California, Berkeley, Julie Shah, Massachussets Institute of Technology, Milind Tambe, University of Southern California, Astro Teller, X Standing Committee of the One Hundred Year Study of Artificial Intelligence: Barbara J. Grosz, Chair, Russ Altman, Eric Horvitz, Alan Mackworth, Tom Mitchell, Deidre Mulligan, Yoav Shoham While drawing on common research and technologies, AI systems are specialized to accomplish particular tasks.')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCaIPaTdpBMN",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        }
      },
      "source": [
        "#sorting the sentences in the reverse manner for getting sentences with high scores and printing them.\n",
        "for i in range(10):\n",
        "  print(ranked_sentences[i][1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10 AI policy, now and\n",
            "What’s next for AI research?\n",
            "While impressive, these technologies are highly tailored to particular tasks.\n",
            "AI is also changing how people interact with technology.\n",
            "Great strides have also been made in machine translation among different languages, with more real-time person-to-person exchanges on the near horizon.\n",
            "People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals.\n",
            "Once a mostly academic area of study, twenty-first century AI enables a constellation of mainstream technologies that are having a substantial impact on everyday lives.\n",
            "On the other hand, if society approaches AI with a more open mind, the technologies emerging from the field could profoundly transform society for the better in the coming decades.\n",
            "Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030, the period this report considers.\n",
            "In each domain, even as AI continues to deliver important benefits, it also raises important ethical and social issues, including privacy concerns.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCWhEv1dpEul",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
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}